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Journal articles on the topic 'Lung nodule segmentation'
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1
Joshua, Eali Stephen Neal. "LUNG NODULE SEMANTIC SEGMENTATION WITH BI-DIRECTION FEATURES USING U-INET." Journal of Medical pharmaceutical and allied sciences 10, no. 5 (2021): 3494–99. http://dx.doi.org/10.22270/jmpas.v10i5.1454.
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It's difficult to detect lung cancer and determine the severity of the disease without a CT scan of the lungs. The anonymity of nodules, as well as physical characteristics such as curvature and surrounding tissue, suggest that CT lung nodule segmentation has limitations. According to the study, a new, resource-efficient deep learning architecture dubbed U-INET is required. When a doctor orders a computed tomography (CT) scan for cancer diagnosis, precise and efficient lung nodule segmentation is required. Due to the nodules' hidden form, poor visual quality, and context, lung nodule segmentation is a challenging job. The U-INET model architecture is given in this article as a resource-efficient deep learning approach for dealing with the problem. To improve segmentation operations, it also includes the Mish non-linearity functions and mask class weights. Furthermore, the LUNA-16 dataset, which included 1200 lung nodules, was heavily utilized to train and evaluate the proposed model. The U-INET architecture outperforms the current U-INET model by 81.89 times and reaches human expert level accuracy.
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Li, Rui, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, and Bingding Huang. "Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review." Diagnostics 12, no. 2 (2022): 298. http://dx.doi.org/10.3390/diagnostics12020298.
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Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people’s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.
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Tang, Tiequn, Feng Li, Minshan Jiang, Xunpeng Xia, Rongfu Zhang, and Kailin Lin. "Improved Complementary Pulmonary Nodule Segmentation Model Based on Multi-Feature Fusion." Entropy 24, no. 12 (2022): 1755. http://dx.doi.org/10.3390/e24121755.
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Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic segmentation of lung nodules. However, they are still challenged by the large diversity of segmentation targets, and the small inter-class variances between the nodule and its surrounding tissues. To tackle this issue, we propose a features complementary network according to the process of clinical diagnosis, which made full use of the complementarity and facilitation among lung nodule location information, global coarse area, and edge information. Specifically, we first consider the importance of global features of nodules in segmentation and propose a cross-scale weighted high-level feature decoder module. Then, we develop a low-level feature decoder module for edge feature refinement. Finally, we construct a complementary module to make information complement and promote each other. Furthermore, we weight pixels located at the nodule edge on the loss function and add an edge supervision to the deep supervision, both of which emphasize the importance of edges in segmentation. The experimental results demonstrate that our model achieves robust pulmonary nodule segmentation and more accurate edge segmentation.
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Usman, Muhammad, and Yeong-Gil Shin. "DEHA-Net: A Dual-Encoder-Based Hard Attention Network with an Adaptive ROI Mechanism for Lung Nodule Segmentation." Sensors 23, no. 4 (2023): 1989. http://dx.doi.org/10.3390/s23041989.
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Measuring pulmonary nodules accurately can help the early diagnosis of lung cancer, which can increase the survival rate among patients. Numerous techniques for lung nodule segmentation have been developed; however, most of them either rely on the 3D volumetric region of interest (VOI) input by radiologists or use the 2D fixed region of interest (ROI) for all the slices of computed tomography (CT) scan. These methods only consider the presence of nodules within the given VOI, which limits the networks’ ability to detect nodules outside the VOI and can also encompass unnecessary structures in the VOI, leading to potentially inaccurate segmentation. In this work, we propose a novel approach for 3D lung nodule segmentation that utilizes the 2D region of interest (ROI) inputted from a radiologist or computer-aided detection (CADe) system. Concretely, we developed a two-stage lung nodule segmentation technique. Firstly, we designed a dual-encoder-based hard attention network (DEHA-Net) in which the full axial slice of thoracic computed tomography (CT) scan, along with an ROI mask, were considered as input to segment the lung nodule in the given slice. The output of DEHA-Net, the segmentation mask of the lung nodule, was inputted to the adaptive region of interest (A-ROI) algorithm to automatically generate the ROI masks for the surrounding slices, which eliminated the need for any further inputs from radiologists. After extracting the segmentation along the axial axis, at the second stage, we further investigated the lung nodule along sagittal and coronal views by employing DEHA-Net. All the estimated masks were inputted into the consensus module to obtain the final volumetric segmentation of the nodule. The proposed scheme was rigorously evaluated on the lung image database consortium and image database resource initiative (LIDC/IDRI) dataset, and an extensive analysis of the results was performed. The quantitative analysis showed that the proposed method not only improved the existing state-of-the-art methods in terms of dice score but also showed significant robustness against different types, shapes, and dimensions of the lung nodules. The proposed framework achieved the average dice score, sensitivity, and positive predictive value of 87.91%, 90.84%, and 89.56%, respectively.
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Maqsood, Muazzam, Sadaf Yasmin, Irfan Mehmood, Maryam Bukhari, and Mucheol Kim. "An Efficient DA-Net Architecture for Lung Nodule Segmentation." Mathematics 9, no. 13 (2021): 1457. http://dx.doi.org/10.3390/math9131457.
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A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.
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Annavarapu, Chandra Sekhara Rao, Samson Anosh Babu Parisapogu, Nikhil Varma Keetha, Praveen Kumar Donta, and Gurindapalli Rajita. "A Bi-FPN-Based Encoder–Decoder Model for Lung Nodule Image Segmentation." Diagnostics 13, no. 8 (2023): 1406. http://dx.doi.org/10.3390/diagnostics13081406.
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Early detection and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodules as observed in the CT images pose a challenging and critical problem to the robust segmentation of lung nodules. This article proposes a resource-efficient model architecture: an end-to-end deep learning approach for lung nodule segmentation. It incorporates a Bi-FPN (bidirectional feature network) between an encoder and a decoder architecture. Furthermore, it uses the Mish activation function and class weights of masks with the aim of enhancing the efficiency of the segmentation. The proposed model was extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. To increase the probability of the suitable class of each voxel in the mask, a weighted binary cross-entropy loss of each sample of training was utilized as network training parameter. Moreover, on the account of further evaluation of robustness, the proposed model was evaluated on the QIN Lung CT dataset. The results of the evaluation show that the proposed architecture outperforms existing deep learning models such as U-Net with a Dice Similarity Coefficient of 82.82% and 81.66% on both datasets.
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Snezhko, E. V., S. A. Kharuzhyk, A. V. Tuzikov, and V. A. Kovalev. "SMALL NODULES LOCALIZATION ON CT IMAGES OF LUNGS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W4 (May 10, 2017): 141–44. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w4-141-2017.
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According to the World Health Organization (WHO) lung cancer remains the leading cause of death of men among all malignant tumors [1, 2]. One of the reasons of such a statistics is the fact that the lung cancer is hardly diagnosed on the yearly stages when it is almost asymptomatic. The purpose of this paper is to present a Computer-Aided Diagnosis (CAD) software developed for assistance of early detection of nodules in CT lung images including solitary pulmonary nodules (SPN) as well as multiple nodules. The efficiency of nodule localization was intended to be as high as the level of the best practice. The software developed supports several functions including lungs segmentation, selection of nodule candidates and nodule candidates filtering.
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Lokhande, Narendra Lalchand, and Tushar Hrishikesh Jaware. "A Systematic Review of AI Based Software Test Case Optimization." International Research Journal of Multidisciplinary Scope 05, no. 04 (2024): 860–71. http://dx.doi.org/10.47857/irjms.2024.v05i04.01452.
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In the realm of computer-aided diagnosis systems designed for lung cancer, accurately segmenting nodules holds vital importance. This segmentation process has a vital role in examining the image attributes of lung nodules captured in computed tomography scans, ultimately aiding in separation of benign and cancerous nodules. Timely detection of these lesions stands as the most effective strategy in combating lung cancer, a disease notorious for its high malignancy rates across both genders. Despite numerous deep learning techniques proposed for nodule segmentation, it remains challenging due to factors such as nodule characteristics, location, false positives, and the necessity for precise boundary detection. The present paper presents an ultra-modern method for lung nodule segmentation in computer tomographic images, based on a Generative Adversarial Network. A discriminator and a generator make up the GAN model. Our generator, Residual Dilated Attention Gate UNet, serves as the segmentation module, while a discriminator is Convolutional Neural Network classifier. To enhance training stability, we utilize the Wasserstein GAN algorithm. We compare our hybrid deep learning model, called WGAN-LUNet, both quantitatively and qualitatively with other methods that are already in use. We evaluate the model using multiple quantitative criteria.
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Iqbal, Saleem, Khalid Iqbal, Fahim Arif, Arslan Shaukat, and Aasia Khanum. "Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/241647.
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Computed tomography (CT) is an important imaging modality. Physicians, surgeons, and oncologists prefer CT scan for diagnosis of lung cancer. However, some nodules are missed in CT scan. Computer aided diagnosis methods are useful for radiologists for detection of these nodules and early diagnosis of lung cancer. Early detection of malignant nodule is helpful for treatment. Computer aided diagnosis of lung cancer involves lung segmentation, potential nodules identification, features extraction from the potential nodules, and classification of the nodules. In this paper, we are presenting an automatic method for detection and segmentation of lung nodules from CT scan for subsequent features extraction and classification. Contribution of the work is the detection and segmentation of small sized nodules, low and high contrast nodules, nodules attached with vasculature, nodules attached to pleura membrane, and nodules in close vicinity of the diaphragm and lung wall in one-go. The particular techniques of the method are multistep threshold for the nodule detection and shape index threshold for false positive reduction. We used 60 CT scans of “Lung Image Database Consortium-Image Database Resource Initiative” taken by GE medical systems LightSpeed16 scanner as dataset and correctly detected 92% nodules. The results are reproducible.
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Qu, Ming Zhi, and Gui Rong Weng. "Lung Nodule Segmentation Using Mathematical Morphology." Applied Mechanics and Materials 58-60 (June 2011): 1378–83. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1378.
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Contemporary computed tomography (CT) technology offers the better potential of screening for the early detection of lung cancer than the traditional x-ray chest radiographs. In order to help improve radiologists’ diagnostic performance and efficiency, many researchers propose to develop computer-aided detection and diagnosis (CAD) system for the detection and characterization of lung nodules depicted on CT images and to evaluate its potentially clinical utility in assisting radiologists. Based on review of computer-aided detection and diagnosis of lung nodules using CT at home and abroad in recent years, this paper presented a new algorithm that achieves an automated way for applying multi-scale nodule enhancement, mathematical morphology and morphological Segmentation.
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Banu, Syeda Furruka, Md Mostafa Kamal Sarker, Mohamed Abdel-Nasser, Domenec Puig, and Hatem A. Raswan. "AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation." Applied Sciences 11, no. 21 (2021): 10132. http://dx.doi.org/10.3390/app112110132.
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Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such as the heterogeneity in CT images and variation in nodule size, shape, and location, which limit their accuracy. In an attempt to handle these challenges, this article proposes a fully automated deep learning framework that consists of lung nodule detection and segmentation models. Our proposed system comprises two cascaded stages: (1) nodule detection based on fine-tuned Faster R-CNN to localize the nodules in CT images, and (2) nodule segmentation based on the U-Net architecture with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule feature representations. The experimental results demonstrate that the proposed system yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly available LUNA16 and LIDC-IDRI datasets, respectively.
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Zhang, Na, Jianping Lin, Bengang Hui, et al. "Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net." Computational and Mathematical Methods in Medicine 2022 (March 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/5112867.
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Lung nodules are the main lesions of the lung, and conditions of the lung can be directly displayed through CT images. Due to the limited pixel number of lung nodules in the lung, doctors have the risk of missed detection and false detection in the detection process. In order to reduce doctors’ work intensity and assist doctors to make accurate diagnosis, a lung nodule segmentation and recognition algorithm is proposed by simulating doctors’ diagnosis process with computer intelligent methods. Firstly, the attention mechanism model is established to focus on the region of lung parenchyma. Then, a pyramid network of bidirectional enhancement features is established from multiple body positions to extract lung nodules. Finally, the morphological and imaging features of lung nodules are calculated, and then, the signs of lung nodules can be identified. The experiments show that the algorithm conforms to the doctor’s diagnosis process, focuses the region of interest step by step, and achieves good results in lung nodule segmentation and recognition.
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Yang, Zhang, Xie Yingying, Guo Li, et al. "Robust Pulmonary Nodule Segmentation in CT Image for Juxta-pleural and Juxta-vascular Case." Current Bioinformatics 14, no. 2 (2019): 139–47. http://dx.doi.org/10.2174/1574893613666181029100249.
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Background: Lung cancer is a greatest threat to people's health and life. CT image leads to unclear boundary segmentation. Segmentation of irregular nodules and complex structure, boundary information is not well considered and lung nodules have always been a hot topic. Objective: In this study, the pulmonary nodule segmentation is accomplished with the new graph cut algorithm. The problem of segmenting the juxta-pleural and juxta-vascular nodules was investigated which is based on graph cut algorithm. Methods: Firstly, the inflection points by the curvature was decided. Secondly, we used kernel graph cut to segment the nodules for the initial edge. Thirdly, the seeds points based on cast raying method is performed; lastly, a novel geodesic distance function is proposed to improve the graph cut algorithm and applied in lung nodules segmentation. Results: The new algorithm has been tested on total 258 nodules. Table 1 summarizes the morphologic features of all the nodules and given the results between the successful segmentation group and the poor/failed segmentation group. Figure 1 to Fig. (12) shows segmentation effect of Juxta-vascular nodules, Juxta-pleural nodules, and comparted with the other interactive segmentation methods. Conclusion: The experimental verification shows better results with our algorithm, the results will measure the volume numerical approach to nodule volume. The results of lung nodules segmentation in this study are as good as the results obtained by the other methods.
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Mekali, Vijayalaxmi, and Girijamma H. A. "Fully Automatic Detection and Segmentation Approach for Juxta-Pleural Nodules From CT Images." International Journal of Healthcare Information Systems and Informatics 16, no. 2 (2021): 87–104. http://dx.doi.org/10.4018/ijhisi.20210401.oa5.
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Early detection of all types of lung nodules with different characters in medical modality images using computer-aided detection is the best acceptable remedy to save the lives of lung cancer sufferers. But accuracy of different types of nodule detection rates is based on chosen segmented procedures for parenchyma and nodules. Separation of pleural from juxta-pleural nodules (JPNs) is difficult as intensity of pleural and attached nodule is similar. This research paper proposes a fully automated method to detect and segment JPNs. In the proposed method, lung parenchyma is segmented using iterative thresholding algorithm. To improve the nodules detection rate separation of connected lung lobes, an algorithm is proposed to separate connected left and right lung lobes. The new method segments JPNs based on lung boundary pixels extraction, concave points extraction, and separation of attached pleural from nodule. Validation of the proposed method was performed on LIDC-CT images. The experimental result confirms that the developed method segments the JPNs with less computational time and high accuracy.
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Shariaty, F., V. A. Pavlov, S. V. Zavjalov, M. Orooji, and T. M. Pervunina. "Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest." Journal of the Russian Universities. Radioelectronics 25, no. 3 (2022): 96–117. http://dx.doi.org/10.32603/1993-8985-2022-25-3-96-117.
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Introduction. Lung cancer is one of the most critical diseases globally, with more than 1.6 million new cases registered every year. Early detection of lung cancer is essential; therefore, particular attention should be paid to the development of effective diagnostic and therapeutic procedures. Computer processing of CT scans in the course of lung cancer diagnostics involves the following stages: medical image acquisition, pre-processing of medical images, segmentation, and false-positive reduction. Since segmentation is an essential stage in the process of medical image analysis, the development of novel segmentation approaches is attracting much research interest. Model-based segmentation approaches have recently gained in popularity, largely due to their potential to restore lost information.Aim. To apply a texture appearance model for the segmentation of pulmonary nodules on computed tomography of the chest.Materials and methods. A novel model-based Texture Appearance Model (TAM) is proposed for precise and effective segmentation of all sorts of nodule regions. We taught the TAM for segmentation of a lung nodule in lung CT images using a combination of extracted texture characteristics from CT scans and Texture Representation of Image (TRI).Results. The results of applying the described TAM method to normal and noisy CT images are presented and compared to those obtained using the Region Growing and Active Contour algorithms, as well as the combination of Active Contour and Watershed algorithms. The TAM was tested in 85 nodules from a dataset, yielding an average dice similarity coefficient (DSC) of 84.75 percent.Conclusion. A novel method for segmenting nodules in the lung, which is capable of segmenting all forms of nodules with excellent accuracy, is proposed. This model-based technique, when used with the active loop algorithm, can enhance accuracy and decrease false positives by selecting the initial mask. The precision, dice, accuracy, and specificity of lung nodule segmentation on a normal CT scan are 85.5, 85, 96, and 98, which levels are superior to those produced by the Active Contour, Region Growing and the combination of Active Contour and Watershed algorithms.
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GUO, LI, YUNTING ZHANG, ZEWEI ZHANG, DONGYUE LI, and YING LI. "AN IMPROVED RANDOM WALK SEGMENTATION ON THE LUNG NODULES." International Journal of Biomathematics 06, no. 06 (2013): 1350043. http://dx.doi.org/10.1142/s1793524513500435.
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In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM (Fuzzy C Means) segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.
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Ziyad, Shabana Rasheed, Venkatachalam Radha, and Thavavel Vayyapuri. "Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (2020): 16–26. http://dx.doi.org/10.2174/1573405615666190206153321.
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Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.
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Xiao, Zhitao, Bowen Liu, Lei Geng, Fang Zhang, and Yanbei Liu. "Segmentation of Lung Nodules Using Improved 3D-UNet Neural Network." Symmetry 12, no. 11 (2020): 1787. http://dx.doi.org/10.3390/sym12111787.
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Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. This paper proposes a CT (Computed Tomography) image lung nodule segmentation method based on 3D-UNet and Res2Net, and establishes a new convolutional neural network called 3D-Res2UNet. 3D-Res2Net has a symmetrical hierarchical connection network with strong multi-scale feature extraction capabilities. It enables the network to express multi-scale features with a finer granularity, while increasing the receptive field of each layer of the network. This structure solves the deep level problem. The network is not prone to gradient disappearance and gradient explosion problems, which improves the accuracy of detection and segmentation. The U-shaped network ensures the size of the feature map while effectively repairing the lost features. The method in this paper was tested on the LUNA16 public dataset, where the dice coefficient index reached 95.30% and the recall rate reached 99.1%, indicating that this method has good performance in lung nodule image segmentation.
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Zheng, Li, and Yiran Lei. "A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography Images." MATEC Web of Conferences 232 (2018): 02001. http://dx.doi.org/10.1051/matecconf/201823202001.
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The detection and segmentation of lung nodules based on computer tomography images (CT) is a basic and significant step to achieve the robotic needle biopsy. In this paper, we reviewed some typical segmentation algorithms, including thresholding, active contour, differential operator, region growing and watershed. To analyse their performance on lung nodule detection, we applied them to four CT images of different kinds of lung nodules. The results show that thresholding, active contour and differential operator do well in the segmentation of solitary nodules, while region growing has an advantage over the others on segmenting nodules adhere to vessels. For segmentation of semi-transparent nodules, differential operator is an especially suitable choice. Watershed can segment nodules adhere to vessels and semi-transparent nodules well, but it has low sensitivity in solitary nodules.
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V, Asha, and Bhavanishankar K. "Towards Efficient Lung Cancer Detection: V-Net-based Segmentation of Pulmonary Nodules." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 11 (2024): 31–45. http://dx.doi.org/10.3991/ijoe.v20i11.49165.
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The novel approach uses the V-Net architecture to segment pulmonary nodules from computed tomography (CT) scans, enhancing lung cancer detection’s efficiency. Addressing lung cancer, a major global mortality cause, underscores the urgency for improved diagnostic methods. The aim of this research is to refine segmentation, a critical step for early cancer detection. The study leverages V-Net, a three-dimensional (3D) convolutional neural network (CNN) tailored for medical image segmentation, applied to lung nodule identification. It utilizes the LUNA16 dataset, containing 888 annotated CT images, for model training and evaluation. This dataset’s variety of pulmonary conditions allows for a comprehensive method of assessment. The tailored V-Net architecture is optimized for lung nodule segmentation, with a focus on data preprocessing to elevate input image quality. Outcomes reveal significant progress in segmentation precision, achieving a loss score of 0.001 and a mIOU of 98%, setting new standards in the domain. Visuals of segmented lung nodules illustrate the method’s effectiveness, indicating a promising avenue for early lung cancer detection and potentially better patient prognoses. The study contributes significantly to enhancing lung cancer diagnostic methodologies through advanced image analysis. An improved segmentation method based on V-Net architecture surpasses current techniques and encourages further deep learning exploration in medical diagnostics.
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Chen, Juepu, Shuxian Liu, and Yulong Liu. "ALKU-Net: Adaptive Large Kernel Attention Convolution Network for Lung Nodule Segmentation." Electronics 13, no. 16 (2024): 3121. http://dx.doi.org/10.3390/electronics13163121.
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The accurate segmentation of lung nodules in computed tomography (CT) images is crucial for the early screening and diagnosis of lung cancer. However, the heterogeneity of lung nodules and their similarity to other lung tissue features make this task more challenging. By using large receptive fields from large convolutional kernels, convolutional neural networks (CNNs) can achieve higher segmentation accuracies with fewer parameters. However, due to the fixed size of the convolutional kernel, CNNs still struggle to extract multi-scale features for lung nodules of varying sizes. In this study, we propose a novel network to improve the segmentation accuracy of lung nodules. The network integrates adaptive large kernel attention (ALK) blocks, employing multiple convolutional layers with variously sized convolutional kernels and expansion rates to extract multi-scale features. A dynamic selection mechanism is also introduced to aggregate the multi-scale features obtained from variously sized convolutional kernels based on selection weights. Based on this, we propose a lightweight convolutional neural network with large convolutional kernels, called ALKU-Net, which integrates the ALKA module in a hierarchical encoder and adopts a U-shaped decoder to form a novel architecture. ALKU-Net efficiently utilizes the multi-scale large receptive field and enhances the model perception capability through spatial attention and channel attention. Extensive experiments demonstrate that our method outperforms other state-of-the-art models on the public dataset LUNA-16, exhibiting considerable accuracy in the lung nodule segmentation task.
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Li, Yongbin, Xinyue Yang, Xinqian Chen, Enlin Fu, Guanghong Ren, and Yu Mu. "Automatic Detection of Lung Nodules in Computed Tomography (CT) Images: A Systematic Review." Scientific Journal of Intelligent Systems Research 7, no. 1 (2025): 72–89. https://doi.org/10.54691/a3577023.
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Lung cancer is the leading cause of cancer-related mortality worldwide, and early detection of lung nodules is crucial for improving patient survival rates. Computed tomography (CT) is a widely used screening tool for lung cancer, effectively capturing the morphological characteristics of lung nodules. However, the diversity and complexity of lung nodules present challenges for clinical detection and diagnosis. With advancements in deep learning and the availability of large annotated datasets, computer-aided detection (CADe) tools have shown high robustness, sensitivity, and low false-positive rates in lung nodule detection, gradually establishing themselves as mainstream methods in cancer screening. This review summarizes recent research advancements, current trends, and future challenges in automatic lung nodule detection within CT scans, covering studies published up to February 2024. The paper focuses on the techniques involved in various stages of automated lung nodule detection, including commonly used lung parenchyma segmentation methods, lung nodule detection, and false-positive reduction techniques. Finally, the article discusses the challenges faced by current methods and outlines potential future research directions. This review aims to provide researchers with the latest insights into the field of automatic lung nodule detection, advancing the development of early lung cancer diagnosis and treatment.
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Sathyamoorthy, Sathyamoorthy, and Ravikumar S. "Lung nodule growth measurement and prediction using Multi scale - 3 D- UNet segmentation and shape variance analysis." Fusion: Practice and Applications 16, no. 1 (2024): 52–66. http://dx.doi.org/10.54216/fpa.160104.
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In this work, a statistical model is constructed to forecast the possibility of lung nodules that may grow in the future. This study segments all potential lung nodule candidates using the Multi-scale 3D UNet (M-3D-UNet) method. 34 patients' CT scan series yielded an average of approximately 600 nodule candidates larger than 3 mm, which were then segmented. After removing the arteries, non-nodules and 3D shape variation analysis, 34 actual nodules remained. On actual nodules, the nodule growth Rate (NGR) was calculated in terms of 3D-volume change. Three of the 34 actual nodules had RNG values greater than one, indicating that they were malignant. Compactness, Tissue deficit, Tissue excess, Isotropic Factor and Edge gradient were used to develop the nodule growth predictive measure.
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Zhang, Xinying, Shanshan Kong, Yang Han, Baoshan Xie, and Chunfeng Liu. "Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network." Mathematics 11, no. 6 (2023): 1363. http://dx.doi.org/10.3390/math11061363.
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To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a local residual design was adopted in the upsampling process. Finally, the effectiveness of the proposed algorithm was evaluated using the LIDC-IDRI lung nodule public dataset. The results showed that the improved algorithm had 7.03%, 14.05%, and 10.43% higher performance than the U-Net segmentation algorithm under the three loss functions of DC, MIOU, and SE, and the accuracy was 2.45% higher compared with that of U-Net. Thus, the proposed method had an effective network structure.
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Kumar, S. Pramod, and Mrityunjaya V. Latte. "Fully Automated Segmentation of Lung Parenchyma Using Break and Repair Strategy." Journal of Intelligent Systems 28, no. 2 (2019): 275–89. http://dx.doi.org/10.1515/jisys-2017-0020.
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Abstract The traditional segmentation methods available for pulmonary parenchyma are not accurate because most of the methods exclude nodules or tumors adhering to the lung pleural wall as fat. In this paper, several techniques are exhaustively used in different phases, including two-dimensional (2D) optimal threshold selection and 2D reconstruction for lung parenchyma segmentation. Then, lung parenchyma boundaries are repaired using improved chain code and Bresenham pixel interconnection. The proposed method of segmentation and repairing is fully automated. Here, 21 thoracic computer tomography slices having juxtapleural nodules and 115 lung parenchyma scans are used to verify the robustness and accuracy of the proposed method. Results are compared with the most cited active contour methods. Empirical results show that the proposed fully automated method for segmenting lung parenchyma is more accurate. The proposed method is 100% sensitive to the inclusion of nodules/tumors adhering to the lung pleural wall, the juxtapleural nodule segmentation is >98%, and the lung parenchyma segmentation accuracy is >96%.
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Govindan, Inbasakaran, and Anitha Ruth Joseph Raj. "Deep lung nodule detection using multi-resolution analysis on computed tomography images." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 3 (2025): 1989. https://doi.org/10.11591/ijai.v14.i3.pp1989-2000.
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The lung nodule must be detected early because the patient's outcome can be enhanced following the lung cancer diagnosis. The candidate research proposed a novel computer-aided detection system based on multi-resolution technique (MRT) and local Gaussian distribution (LGD) methods to accurately identify and label the lung nodules in a computed tomography (CT) screening image. The research aimed to find all the potential nodule constructs, which combined wavelet and multiscale morphological analysis and then used the LGD method to calculate the Gaussian function parameters for each image block. Subsequently, we calculated the probability that each pixel belongs to a particular institute, which shall be used to achieve lung nodule segmentation reliably. After the segmentation, the research employed a convolutional neural network (CNN) variant to improve the detection performance further. The proposed method attained an accuracy of 0.9958, a sensitivity of 0.7899, a specificity of 0.9994 and an F1-score of 0.8651. The comparison with other methods shows that the proposed method had better detection accuracy than the different methods in terms of lung nodule detection.
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Khalid, Shehzad, Anwar C. Shaukat, Amina Jameel, and Imran Fareed. "Segmentation of Lung Nodules in CT Scan Data." International Journal of Privacy and Health Information Management 3, no. 2 (2015): 66–77. http://dx.doi.org/10.4018/ijphim.2015070104.
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Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow up examinations. Different image pre-processing and segmentation techniques are used in various research sides to segment different tumors or ulcers from different images. This paper aims to make a review on the existing segmentation algorithms used for CT images of pulmonary nodules and presents a study of the existing methods on automated lung nodule detection. It provides a comparison of the performance of the existing approaches in regards to effective domain results.
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Gopalakrishnan, Ravichandran C., and Veerakumar Kuppusamy. "Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images." Computational and Mathematical Methods in Medicine 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/572494.
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Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.
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Chung-Hyok, O., Ri Jong-Hyok, and Om Chol-Nam. "LUNG NODULE SEGMENTATION BASED ON LUNG-RANGE-STANDARDIZATION." ICTACT Journal on Image and Video Processing 15, no. 4 (2025): 3630–40. https://doi.org/10.21917/ijivp.2025.0513.
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Checking radiological image is a very toilsome work for radiologists because it requires long time practice and experienced skill. Therefore, many computer-aided diagnosis (CAD) systems have been introduced to cooperate with radiologists and nowadays many CAD systems based on deep learning exceed human experts in diagnosing accuracy. Nowadays, the much of progress has been made in designing architectures. However, peculiar pre-processing method customized for a certain problem can also increase the model accuracy. After checking the LIDC dataset [44], it has been realized that the locations and sizes of lungs were not regularized. Therefore, in this paper, a new pre- processing method (lung-range-standardization) is proposed in order to improve the general accuracy of lung-related diagnosis systems. And the efficiency of the proposed pre-processing method is validated through comparison between the nodule segmentation model trained using our proposed pre-processing method and the nodule segmentation model, which is trained using the prior pre-processing methods. By using lung-range-standardization we could reduce the difference between train loss and test loss in a great deal (from 0.337 to 0.119).
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Rozengauz, E. V., D. V. Nesterov, Z. A. Al’derov, and N. M. Korablin. "Pulmonary nodules volumetry. Variability in Results After Manual Correction of Contours." Journal of radiology and nuclear medicine 100, no. 2 (2019): 74–81. http://dx.doi.org/10.20862/0042-4676-2019-100-2-74-81.
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Objective. To study variability of volumetric of the pulmonary nodules volumetry after manual correction of their contours.Material and methods. Twenty-seven nodules uncircumscribed from the vascular structures and pleura were selected. A linear regression model was used to investigate the impact of the size of a nodule, the area of its contact with the adjacent structures on variability in results.Results. The linear regression model based on contact area and nodule size can correctly predict volumetry variability.Conclusion. Even after manual segmentation volumetry remain suitable method for size assessment of lung nodules. Segmentation should be made with the same person because of significant difference of interobserver and intraobserver variabilities.
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Khan, Muhammad Attique, Venkatesan Rajinikanth, Suresh Chandra Satapathy, et al. "VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images." Diagnostics 11, no. 12 (2021): 2208. http://dx.doi.org/10.3390/diagnostics11122208.
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Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.
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Tang, Tiequn, and Rongfu Zhang. "A Multi-Task Model for Pulmonary Nodule Segmentation and Classification." Journal of Imaging 10, no. 9 (2024): 234. http://dx.doi.org/10.3390/jimaging10090234.
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In the computer-aided diagnosis of lung cancer, the automatic segmentation of pulmonary nodules and the classification of benign and malignant tumors are two fundamental tasks. However, deep learning models often overlook the potential benefits of task correlations in improving their respective performances, as they are typically designed for a single task only. Therefore, we propose a multi-task network (MT-Net) that integrates shared backbone architecture and a prediction distillation structure for the simultaneous segmentation and classification of pulmonary nodules. The model comprises a coarse segmentation subnetwork (Coarse Seg-net), a cooperative classification subnetwork (Class-net), and a cooperative segmentation subnetwork (Fine Seg-net). Coarse Seg-net and Fine Seg-net share identical structure, where Coarse Seg-net provides prior location information for the subsequent Fine Seg-net and Class-net, thereby boosting pulmonary nodule segmentation and classification performance. We quantitatively and qualitatively analyzed the performance of the model by using the public dataset LIDC-IDRI. Our results show that the model achieves a Dice similarity coefficient (DI) index of 83.2% for pulmonary nodule segmentation, as well as an accuracy (ACC) of 91.9% for benign and malignant pulmonary nodule classification, which is competitive with other state-of-the-art methods. The experimental results demonstrate that the performance of pulmonary nodule segmentation and classification can be improved by a unified model that leverages the potential correlation between tasks.
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Yi, Xiaochen, Zongze Sun, Baolong Yu, Munan Yang, and Zhuo Zhang. "Research on CT Scan Image of Lung Cancer Based on Deep Learning Method in Artificial Intelligence Field." Journal of Medical Imaging and Health Informatics 10, no. 4 (2020): 934–39. http://dx.doi.org/10.1166/jmihi.2020.2957.
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Cancer is one of the diseases with high mortality in the 21st century, and lung cancer ranks first in all cancer morbidity and mortality. In recent years, with the rise of big data and artificial intelligence, lung cancer-assisted diagnosis based on deep learning has gradually become A popular research topic. Computer-aided lung cancer diagnosis technology is mainly the process of processing and analyzing the lung image data obtained by medical instrument imaging. The process is summarized into four steps: medical image data preprocessing, lung parenchymal segmentation, lung Nodule detection and segmentation, as well as lesion diagnosis. In order to solve the problem that the two-dimensional image model is not applicable to three-dimensional images, this paper proposes a three-dimensional convolutional neural network model suitable for lung cancer diagnosis. The model consists of two parts. The first part is a three-dimensional deep nodule detection network (FCN) model, which generates a heat map of the lung nodules. We can locate the locations of those malignant nodules through the heat map. According to the heat map generated in the first part, the second part selects those malignant nodules that are likely to be large, and then fuses the features of these selected nodules into one feature vector, showing the whole lung scan. Finally, we use this feature to classify and determine whether we have lung cancer.
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Satra, Henil. "Lung Nodule Detection using Segmentation Approach for Computed Tomography Scan Images." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 1778–90. http://dx.doi.org/10.22214/ijraset.2021.38258.
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Abstract: Lung disorders have become really common in today’s world due to growing amount of air pollution, our increased exposure to harmful radiations and our unhealthy lifestyles. Hence, the diagnosis of lung disorders has become of paramount importance. The commonly used Thresholding approaches and morphological operations often fail to detect the peripheral pathology bearing areas. Hence, we present the segmentation approach of the lung tissue for computer aided diagnosis system. We use a novel technique for segmentation of lungs from CT scan (Computed Tomography) of the chest or upper torso. The accuracy of analysis and its implication majorly depends on the kind of segmentation technique used. Hence, it is important that the method used is highly reliable and is successful in nodule detection and classification. We use MATLAB and OpenCV libraries to apply segmentation on CT scan images to get the desired output. We have also created a working proprietary user interface called “PULMONIS” for the ease of doctors and patients to upload the CT scan images and get the output after the image processing is done in the backend. Keywords: Lung nodule detection, Image Processing, Computed Tomography, Image Segmentation, Lung Cancer, Contour Segmentation, MATLAB, OpenCV, Computer Vision.
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Kim, D. Y., J. H. Kim, S. M. Noh, and J. W. Park. "Pulmonary nodule detection using chest CT images." Acta Radiologica 44, no. 3 (2003): 252–57. http://dx.doi.org/10.1080/j.1600-0455.2003.00061.x.
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Purpose: Automated methods for the detection of pulmonary nodules and nodule volume calculation on CT are described. Material and Methods: Gray-level threshold methods were used to segment the thorax from the background and then the lung parenchyma from the thoracic wall and mediastinum. A deformable model was applied to segment the lung boundaries, and the segmentation results were compared with the thresholding method. The lesions that had high gray values were extracted from the segmented lung parenchyma. The selected lesions included nodules, blood vessels and partial volume effects. The discriminating features such as size, solid shape, average, standard deviation and correlation coefficient of selected lesions were used to distinguish true nodules from pseudolesions. With texture features of true nodules, the contour-following method, which tracks the segmented lung boundaries, was applied to detect juxtapleural nodules that were contiguous to the pleural surface. Volume and circularity calculations were performed for each identified nodule. The identified nodules were sorted in descending order of volume. These methods were applied to 827 image slices of 24 cases. Results: Computer-aided diagnosis gave a nodule detection sensitivity of 96% and no false-positive findings. Conclusion: The computer-aided diagnosis scheme was useful for pulmonary nodule detection and gave characteristics of detected nodules.
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Liu, Jinjiang, Yuqin Li, Wentao Li, Zhenshuang Li, and Yihua Lan. "Multiscale lung nodule segmentation based on 3D coordinate attention and edge enhancement." Electronic Research Archive 32, no. 5 (2024): 3016–37. http://dx.doi.org/10.3934/era.2024138.
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<abstract> <p>An important prerequisite for improving the reliability of lung cancer surveillance and clinical interventions is accurate lung nodule segmentation. Although deep learning is effective at performing medical image segmentation, lung CT image heterogeneity, nodule size, shape, and location variations, convolutional localized feature extraction characteristics, the receptive field limitations of continuous downsampling, lesion edge information losses, fuzzy boundary segmentation challenges, and the low segmentation accuracy achieved when segmenting lung CT images using deep learning remain. An edge-enhanced multiscale Sobel coordinate attention-atrous spatial convolutional pooling pyramid V-Net (SCA-VNet) algorithm for lung nodule segmentation was proposed to solve these problems. First, a residual edge enhancement module was designed, which was used to enhance the edges of the original data. Using an edge detection operator in combination with a residual module, this module could reduce data redundancy and alleviate the gray level similarity between the foreground and background. Then, a 3D atrous spatial convolutional pooling pyramid module set different expansion rates, which could obtain feature maps under different receptive fields and capture the multiscale information of the segmentation target. Finally, a three-dimensional coordinate attention network (3D CA-Net) module was added to the encoding and decoding paths to extract channel weights from multiple dimensions. This step propagated the spatial information in the coding layer to the subsequent layers, and it could reduce the loss of information during the forward propagation process. The proposed method achieved a Dice coefficient of 87.50% on the lung image database consortium and image database resource initiative (LIDC-IDRI). It significantly outperformed the existing lung nodule segmentation models (UGS-Net, REMU-Net, and multitask models) and compared favorably with the Med3D, CENet, and PCAM_Net segmentation models in terms of their Dice coefficients, which were 3.37%, 2.2%, and 1.43%, respectively. The experimental results showed that the proposed SCA-VNet model attained improved lung nodule segmentation accuracy and laid a good foundation for improving the early detection rate of lung cancer.</p> </abstract>
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Weikert, Thomas, Tugba Akinci D’Antonoli, Jens Bremerich, Bram Stieltjes, Gregor Sommer, and Alexander W. Sauter. "Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors." Contrast Media & Molecular Imaging 2019 (July 1, 2019): 1–10. http://dx.doi.org/10.1155/2019/1545747.
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Automated detection and segmentation is a prerequisite for the deployment of image-based secondary analyses, especially for lung tumors. However, currently only applications for lung nodules ≤3 cm exist. Therefore, we tested the performance of a fully automated AI-based lung nodule algorithm for detection and 3D segmentation of primary lung tumors in the context of tumor staging using the CT component of FDG-PET/CT and including all T-categories (T1–T4). FDG-PET/CTs of 320 patients with histologically confirmed lung cancer performed between 01/2010 and 06/2016 were selected. First, the main primary lung tumor within each scan was manually segmented using the CT component of the PET/CTs as reference. Second, the CT series were transferred to a platform with AI-based algorithms trained on chest CTs for detection and segmentation of lung nodules. Detection and segmentation performance were analyzed. Factors influencing detection rates were explored with binominal logistic regression and radiomic analysis. We also processed 94 PET/CTs negative for pulmonary nodules to investigate frequency and reasons of false-positive findings. The ratio of detected tumors was best in the T1-category (90.4%) and decreased continuously: T2 (70.8%), T3 (29.4%), and T4 (8.8%). Tumor contact with the pleura was a strong predictor of misdetection. Segmentation performance was excellent for T1 tumors (r = 0.908, p<0.001) and tumors without pleural contact (r = 0.971, p<0.001). Volumes of larger tumors were systematically underestimated. There were 0.41 false-positive findings per exam. The algorithm tested facilitates a reliable detection and 3D segmentation of T1/T2 lung tumors on FDG-PET/CTs. The detection and segmentation of more advanced lung tumors is currently imprecise due to the conception of the algorithm for lung nodules <3 cm. Future efforts should therefore focus on this collective to facilitate segmentation of all tumor types and sizes to bridge the gap between CAD applications for screening and staging of lung cancer.
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Jerónimo, Alejandro, Olga Valenzuela, and Ignacio Rojas. "Statistical Analysis of nnU-Net Models for Lung Nodule Segmentation." Journal of Personalized Medicine 14, no. 10 (2024): 1016. http://dx.doi.org/10.3390/jpm14101016.
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This paper aims to conduct a statistical analysis of different components of nnU-Net models to build an optimal pipeline for lung nodule segmentation in computed tomography images (CT scan). This study focuses on semantic segmentation of lung nodules, using the UniToChest dataset. Our approach is based on the nnU-Net framework and is designed to configure a whole segmentation pipeline, thereby avoiding many complex design choices, such as data properties and architecture configuration. Although these framework results provide a good starting point, many configurations in this problem can be optimized. In this study, we tested two U-Net-based architectures, using different preprocessing techniques, and we modified the existing hyperparameters provided by nnU-Net. To study the impact of different settings on model segmentation accuracy, we conducted an analysis of variance (ANOVA) statistical analysis. The factors studied included the datasets according to nodule diameter size, model, preprocessing, polynomial learning rate scheduler, and number of epochs. The results of the ANOVA analysis revealed significant differences in the datasets, models, and preprocessing.
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Liu, Brian, and Ashish Raj. "Discriminative Random Field Segmentation of Lung Nodules in CT Studies." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/683216.
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The ability to conduct high-quality semiautomatic 3D segmentation of lung nodules in CT scans is of high value to busy radiologists. Discriminative random fields (DRFs) were used to segment 3D volumes of lung nodules in CT scan data using only one seed point per nodule. Optimal parameters for the DRF inference were first found using simulated annealing. These parameters were then used to solve the inference problem using the graph cuts algorithm. Results of the segmentation exhibited high precision and recall. The system can be adapted to facilitate the process of longitudinal studies but will still require human checking for failed cases.
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Wang, Jinke, and Haoyan Guo. "Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction." Computational and Mathematical Methods in Medicine 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/2962047.
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This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD) 11.15±69.63 cm3, volume overlap error (VOE) 3.5057±1.3719%, average surface distance (ASD) 0.7917±0.2741 mm, root mean square distance (RMSD) 1.6957±0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430±8.1743 mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.
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Dr. Samuel Manoharan and Sathish. "Improved Version of Graph-Cut Algorithm for CT Images of Lung Cancer With Clinical Property Condition." December 2020 2, no. 4 (2020): 201–6. http://dx.doi.org/10.36548/jaicn.2020.4.002.
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In a clinical evaluation, the detection of lung cancer is a challenging task. Segmentation methods are used to detect the extra growing nodule. Early diagnosis of lung cancer is significant in clinical research. The early stage of lung nodules is very soft tissues and tough to segment accurately. Generally, conservative graph cut methods are very weak to detect those soft edges in medical images. In this article, the proposed algorithm is improved to obtain the accuracy of the process to segment the edges than the conventional graph cut methods. This investigation is executed to shows the accuracy of lung segmentation.
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Khanum, Afshan, S. Purushothaman, and P. Rajeswari. "Performance comparisons of the soft computing algorithms in lung segmentation and nodule identification." International Journal of Engineering & Technology 7, no. 1.1 (2017): 189. http://dx.doi.org/10.14419/ijet.v7i1.1.9287.
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This paper presents the implementation back propagation algorithm (BPA) and fuzzy logic(FL) in lung image segmentation and nodule identification. Lung image database consortium (LIDC) database images has been used. Features are extracted using statistical methods. These features are used for training the BPA and FL algorithms. Weights are stored in a file that is used for segmentation of the lung image. Subsequently, texture properties are used for nodule identification.
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Hooshangnejad, Hamed, Gaofeng Huang, Katelyn Kelly, et al. "EXACT-Net: Framework for EHR-Guided Lung Tumor Auto-Segmentation for Non-Small Cell Lung Cancer Radiotherapy." Cancers 16, no. 23 (2024): 4097. https://doi.org/10.3390/cancers16234097.
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Background/Objectives: Lung cancer is a devastating disease with the highest mortality rate among cancer types. Over 60% of non-small cell lung cancer (NSCLC) patients, accounting for 87% of lung cancer diagnoses, require radiation therapy. Rapid treatment initiation significantly increases the patient’s survival rate and reduces the mortality rate. Accurate tumor segmentation is a critical step in diagnosing and treating NSCLC. Manual segmentation is time- and labor-consuming and causes delays in treatment initiation. Although many lung nodule detection methods, including deep learning-based models, have been proposed. Most of these methods still have a long-standing problem of high false positives (FPs). Methods: Here, we developed an electronic health record (EHR)-guided lung tumor auto-segmentation called EXACT-Net (EHR-enhanced eXACtitude in Tumor segmentation), where the extracted information from EHRs using a pre-trained large language model (LLM) was used to remove the FPs and keep the TP nodules only. Results: The auto-segmentation model was trained on NSCLC patients’ computed tomography (CT), and the pre-trained LLM was used with the zero-shot learning approach. Our approach resulted in a 250% boost in successful nodule detection using the data from ten NSCLC patients treated in our institution. Conclusions: We demonstrated that combining vision-language information in EXACT-Net multi-modal AI framework greatly enhances the performance of vision only models, paving the road to multimodal AI framework for medical image processing.
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Wang, Hui, Yanying Li, Shanshan Liu, and Xianwen Yue. "Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules." Computational and Mathematical Methods in Medicine 2022 (January 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/7729524.
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At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.
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HM, Naveen, Naveena C, and Manjunath Aradhya VN. "An approach for classification of lung nodules." Tumor Discovery 2, no. 1 (2023): 317. http://dx.doi.org/10.36922/td.317.
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The main objective of the proposed work is to develop an automated computer-aided detection (CAD) system to classify lung nodules using various classifiers from computed tomography (CT) images. One of the most important steps in lung nodule detection is the classification of nodule and non-nodule patterns in CT. The early detection of the condition helps lower the mortality rate. The developed CAD systems consist of segmentation, feature extraction, and classification. In this work, a filter method is used to segment the infected region. Later, we extracted features through and fed into classifiers such as Decision Stump (DS), Random Forest (RF), and Back Propagation Neural Network (BPNN). The experimentation was conducted on LIDC-IDRI dataset, and the results with BPNN outperformed those with DS and RF classifiers.
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Guo, Xixi, Yuze Li, Chunjie Yang, et al. "Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation." Journal of Healthcare Engineering 2021 (October 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/6556266.
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This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) ( P < 0.05 ); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) ( P < 0.05 ) that was slightly higher for solid and subsolid nodules with a diameter of 5–10 mm (37 cases vs. 28 cases) ( P < 0.05 ). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) ( P < 0.05 ). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.
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Prasanthi, N. R. S. L., G. V. Jyotsna, L. Lokeswar Rao, G. S. Varshini, B. Tejeswar, and D. K. S. Kartheek. "EFFICIENT U-NET FOR INSIGHTFUL LUNG CANCER DIAGNOSIS." Journal of Nonlinear Analysis and Optimization 15, no. 01 (2024): 1733–40. http://dx.doi.org/10.36893/jnao.2024.v15101.1733-1740.
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Lung cancer detection often relies on interpreting subtle nodules in CT scans, a task demanding precise segmentation tools beyond simple image classification models. While existing methods utilizing other architectures might achieve decent accuracy, they often struggle with limited CT scan datasets and scalability, hindering their real-world impact. Our paper addresses the imperative need for enhanced lung cancer detection by integrating the Efficient U-Net architecture, which is implemented to achieve better results on image classification tasks while using low computational resources, it means achieve high accuracy can be achieved with few parameters and makes computation less expensive compared to other models, with the Luna16 dataset. Our proposed model excels in feature extraction and overcoming vanishing gradients, ensuring sharper and more accurate nodule segmentation in CT scans. The Luna16 dataset, a diverse and annotated benchmark, facilitates comprehensive learning and adaptability to various nodule types and imaging conditions.
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Xia, Lei. "Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning." Computational and Mathematical Methods in Medicine 2022 (May 4, 2022): 1–7. http://dx.doi.org/10.1155/2022/1994082.
Full textAbstract:
This study was aimed at two image segmentation methods of three-dimensional (3D) U-shaped network (U-Net) and multilevel boundary sensing residual U-shaped network (RUNet) and their application values on the auxiliary diagnosis of lung cancer. In this study, on the basis of the 3D U-Net segmentation method, the multilevel boundary sensing RUNet was worked out after optimization. 92 patients with lung cancer were selected, and their clinical data were counted; meanwhile, the lung nodule detection was performed to obtain the segmentation effect under 3D U-Net. The accuracy of 3D U-Net and multilevel boundary sensing RUNet was compared on lung magnetic resonance imaging (MRI) after lung nodule segmentation. Patients with benign lung tumors were taken as controls; the blood immune biochemical indicators progastrin-releasing peptide (pro-CRP), carcinoembryonic antigen (CEA), and neuron-specific enolase (NSE) in patients with malignant lung tumors were analyzed. It was found that the accuracy, sensitivity, and specificity were all greater than 90% under the algorithm-based MRI of benign and malignant tumor patients. Based on the imaging signs for the MRI image of lung nodules, the segmentation effect of the RUNet was clearer than that of the 3D U-Net. In addition, serum levels of pro-CRP, NSE, and CAE in patients with benign lung tumors were 28.9 pg/mL, 12.5 ng/mL, and 10.8 ng/mL, respectively, which were lower than 175.6 pg/mL, 33.6 ng/mL, and 31.9 ng/mL in patients with malignant lung tumors significantly ( P < 0.05 ). Thus, the RUNet image segmentation method was better than the 3D U-Net. The pro-CRP, CEA, and NSE could be used as diagnostic indicators for malignant lung tumors.
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Zhang, Chan, Jing Li, Jian Huang, and Shangjie Wu. "Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination." Journal of Healthcare Engineering 2021 (October 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/3417285.
Full textAbstract:
The objective of this study was to perform segmentation and extraction of CT images of pulmonary nodules based on convolutional neural networks (CNNs). The Mask-RCNN algorithm model is a typical end-to-end image segmentation model, which uses the R-FCN structure for nodule detection. The effect of applying the two algorithm models to the computed tomography (CT) diagnosis of pulmonary nodules was analyzed, and different indexes of pulmonary nodule CT images in lung function examination after algorithm optimization were compared. A total of 56 patients diagnosed with pulmonary nodules by surgery or puncture were taken as the research objects. Based on the Mask-RCNN algorithm, a model for CT image segmentation processing of pulmonary nodules was proposed. Subsequently, the 3D Faster-RCNN model was used to label the nodules in the pulmonary nodules. The experimental results showed that the trained Mask-RCNN algorithm model can effectively complete the segmentation task of lung CT images, but there was a little jitter at the boundary. The speed of R-FCN algorithm for nodular detection was 0.172 seconds/picture, and the accuracy was 88.9%. CT scans were performed on the 56 patients based on a deep learning algorithm. The results showed that 30 cases of malignant pulmonary nodules were confirmed, and the diagnostic accuracy was 93.75%. There were 22 benign lesions, the diagnostic accuracy was 91.67%, and the overall diagnostic accuracy was 92.85%. This study effectively improved the diagnostic efficiency of CT images of pulmonary nodules, and the accuracy of CT images in the diagnosis of pulmonary nodules was analyzed and evaluated. It provided theoretical support for the follow-up diagnosis of pulmonary nodules and the treatment of lung cancer. It also significantly improved the diagnostic effect and detection efficiency of pulmonary nodules.
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Asuntha A and Andy Srinivasan. "Lung cancer detection using Adaptive Bilateral Filter (ABF) techniques." International Journal of Research in Pharmaceutical Sciences 10, no. 3 (2019): 1857–60. http://dx.doi.org/10.26452/ijrps.v10i3.1383.
Full textAbstract:
Lung cancer plays a major role among the people who are affected with cancer. The major reason is the presence of nodule in a lung region. Early diagnosis of this nodule may decrease the severity also increase the life span of a patient. In this paper, a methodology is proposed to detect the lung nodule and nodule region using texture features. Various image processing techniques are used in this paper. CT images are taken as input over MRI because of its advantages over less exposure of radiation[4]. The given input image is denoised by using adaptive bilateral filter and image contrast is improved by the histogram equalization technique. Superpixel segmentation is used for the segmentation process. A Simulation process has been done using MATLAB software.