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Journal articles on the topic 'Cancer image segmentation'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Stevens, Michiel, Afroditi Nanou, Leon W. M. M. Terstappen, Christiane Driemel, Nikolas H. Stoecklein, and Frank A. W. Coumans. "StarDist Image Segmentation Improves Circulating Tumor Cell Detection." Cancers 14, no. 12 (2022): 2916. http://dx.doi.org/10.3390/cancers14122916.

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After a CellSearch-processed circulating tumor cell (CTC) sample is imaged, a segmentation algorithm selects nucleic acid positive (DAPI+), cytokeratin-phycoerythrin expressing (CK-PE+) events for further review by an operator. Failures in this segmentation can result in missed CTCs. The CellSearch segmentation algorithm was not designed to handle samples with high cell density, such as diagnostic leukapheresis (DLA) samples. Here, we evaluate deep-learning-based segmentation method StarDist as an alternative to the CellSearch segmentation. CellSearch image archives from 533 whole blood sample

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Wang, Duan. "Skin lesion segmentation of dermoscopy images using U-Net." Applied and Computational Engineering 6, no. 1 (2023): 7–14. http://dx.doi.org/10.54254/2755-2721/6/20230360.

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Skin cancer is one of the most threatening cancers as reported and has been on the increase over the past 10 years. The traditional methods of skin cancer segmentation are time-consuming and inefficient. U-Net is a powerful and accurate way of self-segmentation in the medical field. In order to solve this problem, this paper proposes a U-Net skin cancer segmentation system that can provide results and feedback quickly, accurately and intelligently. It is composed of two parts: Skin Image Analysis Module and Skin Image Segmentation Module. In the skin image analysis module, the system learns se

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Putri, Erlinda Ratnasari, Amar Vijai Nasrulloh, and Arfan Eko Fahrudin. "Coloring of Cervical Cancer’s Ct Images to Localize Cervical Cancer." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 2 (2015): 304. http://dx.doi.org/10.11591/ijece.v5i2.pp304-310.

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<p>Cervical cancer is the most common gynecologic cancer in women. Cervical cancer and the normal cervix usually have similar attenuations on CT images which are obtained. The normal cervix and the tumour cannot be distinguished on normal CT images. CT image of cervical cancer is used by the experts for the analysis of diseases. In this research study, CT image of cervical cancer is done with process of image segmentation and coloring. The process of image segmentation is done after the image sharpening process and the determination of cervical cancer’s area. Fuzzy C-Means is used as the

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Kwak, Deawon, Jiwoo Choi, and Sungjin Lee. "Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition." Sensors 23, no. 4 (2023): 2307. http://dx.doi.org/10.3390/s23042307.

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This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3),

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Weishaupt, L. L., T. Vuong, A. Thibodeau-Antonacci, et al. "A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (2022): 140–42. http://dx.doi.org/10.1093/jcag/gwab049.120.

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Abstract Background Tumor delineation in endoscopy images is a crucial part of clinical diagnoses and treatment planning for rectal cancer patients. However, it is challenging to detect and adequately determine the size of tumors in these images, especially for inexperienced clinicians. This motivates the need for a standardized, automated segmentation method. While deep learning has proven to be a powerful tool for medical image segmentation, it requires a large quantity of high-quality annotated training data. Since the annotation of endoscopy images is prone to high inter-observer variabili

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Rasika, Joat, A. P. Thakare Dr., Ketaki Kalele Dr., and Viashali Thakare Dr. "Genetic Programming Approach for Oral Cancer Detection and its Image Restoration." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2422–26. https://doi.org/10.31142/ijtsrd12787.

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Cancer is one of the leading causes of death in developing countries. Cancers are of different types like breast cancer, lung cancer, skin cancer and so on. Oral Cancer is one of the types of cancers. Oral cancer is a very common type of cancer. This Oral Cancer is observed in both males as well as females. It is a big challenge to detect Oral Cancer. This is a time consuming process in medical image processing. Detection and prevention of oral cancer at early stage is critical. But it increases the chances of survival. This work presents the detection of oral cancers using Image Processing. C

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Maolood, Ismail Yaqub, Yahya Eneid Abdulridha Al-Salhi, and Songfeng Lu. "Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm." Open Medicine 13, no. 1 (2018): 374–83. http://dx.doi.org/10.1515/med-2018-0056.

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AbstractIn this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentat

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Kavitha, M. S., J. Shanthini, and R. M. Bhavadharini. "ECIDS-Enhanced Cancer Image Diagnosis and Segmentation Using Artificial Neural Networks and Active Contour Modelling." Journal of Medical Imaging and Health Informatics 10, no. 2 (2020): 428–34. http://dx.doi.org/10.1166/jmihi.2020.2976.

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In the present decade, image processing techniques are extensively utilized in various medical image diagnoses, specifically in dealing with cancer images for detection and treatment in advance. The quality of the image and the accuracy are the significant factors to be considered while analyzing the images for cancer diagnosis. With that note, in this paper, an Enhanced Cancer Image Diagnosis and Segmentation (ECIDS) framework has been developed for effective detection and segmentation of lung cancer cells. Initially, the Computed Tomography lung image (CT image) has been processed for denois

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Dena Nadir George, Haitham Salman Chyad, and Raniah Ali Mustafa. "Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques." Global Journal of Engineering and Technology Advances 6, no. 3 (2021): 056–62. http://dx.doi.org/10.30574/gjeta.2021.6.3.0031.

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Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and

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Dena, Nadir George, Salman Chyad Haitham, and Ali Mustafa Raniah. "Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques." Global Journal of Engineering and Technology Advances 6, no. 3 (2021): 056–62. https://doi.org/10.5281/zenodo.4643420.

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Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and

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Dr., Vimalkumar P., and C.Balasubramanian Dr. "Cutaneous Melanoma Skin Cancer Analysis Using Threshold Image Segmentation Techniques." International Journal of Scientific Development and Research 9, no. 2 (2024): 203–10. https://doi.org/10.5281/zenodo.12665026.

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Medical image processing is one of the most difficult subfields in the dynamic field of image processing research. The internal parts of the human body are imaged using medical imaging techniques in order to aid in diagnosis. Uncontrolled proliferation of abnormal skin cells is known as skin cancer, one of the most serious skin illnesses. Sunshine's UV radiation is the cause of it. The most dangerous kind of skin cancer is cutaneous melanoma. The suggested method uses image processing techniques to diagnose Cutaneous Melanoma Skin Cancer. The analysis of digital lesion images has been conducte

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Saeed, Jwan N. "A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES." Academic Journal of Nawroz University 9, no. 1 (2020): 1. http://dx.doi.org/10.25007/ajnu.v9n1a523.

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The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges assoc

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Chacón, Gerardo, Johel E. Rodríguez, Valmore Bermúdez, et al. "Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer." F1000Research 7 (July 17, 2018): 1098. http://dx.doi.org/10.12688/f1000research.14491.1.

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Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three

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Chacón, Gerardo, Johel E. Rodríguez, Valmore Bermúdez, et al. "Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer." F1000Research 7 (October 9, 2018): 1098. http://dx.doi.org/10.12688/f1000research.14491.2.

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Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three

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Gupta, Ankit, Priyaraj Priyaraj, and Yashi Agarwal. "DETECTION OF LUNG CANCER USING IMAGE PROCESSING." International journal of multidisciplinary advanced scientific research and innovation 1, no. 10 (2021): 323–27. http://dx.doi.org/10.53633/ijmasri.2021.1.10.012.

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This project constructs and assesses an image processing approach for lung cancer diagnosis in this study. Image processing techniques are frequently utilized for picture improvement in the detection phase to enable early medical therapy in a variety of medical issues. We suggested a lung cancer detection approach based on picture segmentation in this study. Image segmentation is a level of image processing that is intermediate. To segment a CT scan image, a marker control watershed and region growth technique is applied. Following the detection phases, picture augmentation with the Gabor filt

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S, Nivas, Sarankumar N V, Vishwa M, and Mohanapriya V. "SKIN CANCER IMAGE SEGMENTATION USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25987.

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Because skin cancer is a frequent and possibly lethal illness, early identification is crucial for surgical treatment. Personal dermatoscopy is crucial in the perception of skin cancer. Recent research on deep literacy techniques has shown astonishing success in automating the segmentation of skin lesions, assisting in the early diagnosis and treatment of skin cancer. This goal provides a summary of improvements in skin cancer image segmentation that are based on deep literacy. The goal of this project is to create a dependable and effective deep literacy frame for segmenting skin lesions from

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Bharath, P. S. V. N. "Detection of Cancer Cells Using Matlab Image Processing (Otsu's Thresholding)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43198.

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Early and accurate cancer detection is critical for improving treatment outcomes. Traditional methods rely on manual examination of histopathological images, which can be time-consuming and prone to human error. This study introduces a MATLAB-based system that automates cancer cell detection using advanced image processing techniques. The system enhances diagnostic accuracy by performing preprocessing, segmentation, and classification with minimal manual intervention. Experimental results on histopathological images show improved clarity, precise segmentation, and faster detection compared to

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Gopi, K., and J. Selvakumar. "Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model." International Journal of Engineering & Technology 7, no. 4.10 (2018): 410. http://dx.doi.org/10.14419/ijet.v7i4.10.21028.

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Lung cancer is the most common leading cancer in both men and women all over the world. Accurate image segmentation is an essential image analysis tool that is responsible for partitioning an image into several sub-regions. Active contour model have been widely used for effective image segmentation methods as this model produce sub-regions with continuous boundaries. It is used in the applications such as image analysis, deep learning, computer vision and machine learning. Advanced level set method helps to implement active contours for image segmentation with good boundary detection accuracy.

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Liu, Hujun, Hui Gao, and Fei Jia. "The Value of Convolutional Neural Network-Based Magnetic Resonance Imaging Image Segmentation Algorithm to Guide Targeted Controlled Release of Doxorubicin Nanopreparation." Contrast Media & Molecular Imaging 2021 (July 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/9032017.

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There was an investigation of the auxiliary role of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image segmentation algorithm in MRI image-guided targeted drug therapy of doxorubicin nanomaterials so that the value of drug-controlled release in liver cancer patients was evaluated. In this study, 80 patients with liver cancer were selected as the research objects. It was hoped that the CNN-based MRI image segmentation algorithm could be applied to the guided analysis of MRI images of the targeted controlled release of doxorubicin nanopreparation to analyze the ima

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Zhang, Wenyue, Ziyan Jia, Qing Li, Dachuan Zhang, Lingjiao Pan, and Dawei Shen. "Gastric Cancer Pathological Image Segmentation based on Convolutional Neural Network." BIO Web of Conferences 111 (2024): 03020. http://dx.doi.org/10.1051/bioconf/202411103020.

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The difficulty in pathological image diagnosis of gastric cancer lies in the accurate segmentation of cancerous tissue in the picture. To increase gastric cancer pathological images segmentation accuracy, we optimized the basic UNet model and proposed the DCU-Net model. First, add a direct channel module to each layer of the encoding part to obtain more detailed information. In addition, considering that the image may cause a loss of information during the transmission process, a CA module is added before the up-sampling and down-sampling of each layer so that the model can obtain more channel

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Jucevicius, Justinas, Povilas Treigys, Jolita Bernataviciene, et al. "Automated 2D Segmentation of Prostate in T2-weighted MRI Scans." International Journal of Computers Communications & Control 12, no. 1 (2016): 53. http://dx.doi.org/10.15837/ijccc.2017.1.2783.

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The prostate cancer is the second most frequent tumor amongst men. Statistics shows that biopsy reveals only 70-80% clinical cancer cases. Multiparametric magnetic resonance imaging (MRI) technique comes to play and is used to help to determine the location to perform a biopsy. With the aim to automating the biopsy localization, prostate segmentation has to be performed in magnetic resonance images. Computer image analysis methods play the key role here. The problem of automated prostate magnetic resonance (MR) image segmentation is burdened by the fact that MRI signal intensity is not standar

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Arokiyamary Delphina, A., M. Kamarasan, and S. Sathiamoorthy. "Self-Organization Map Based Segmentation of Breast Cancer." Asian Journal of Engineering and Applied Technology 7, no. 2 (2018): 31–36. http://dx.doi.org/10.51983/ajeat-2018.7.2.1015.

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Breast cancer is second major leading cause of cancer fatality in women. Mammography prevails best method for initial detection of cancers of breast, capable of identifying small pieces up to two years before they grow large enough to be evident on physical testing. X-ray images of breast must be accurately evaluated to identify beginning signs of cancerous growth. Segmenting, or partitioning, Radio-graphic images into regions of similar texture is usually performed during method of image analysis and interpretation. The comparative lack of structure definition in mammographic images and impli

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Ramadhan, Awf A., Omer S. Kareem, and Diyar Q. Zeebaree. "A Novel Skin Cancer Detection Approach Using Deep Learning Algorithm with Image Segmentation Filters." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 13, no. 1 (2025): 153–61. https://doi.org/10.14500/aro.12024.

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Skin cancer is considered one of the most common and dangerous diseases in the world because so many people do not pay attention to it. In addition, skin cancer is a medical condition that a doctor cannot accurately diagnose from imaging data during a manual examination. Therefore, there is a great need to apply deep learning methods for early detection of skin cancer, as these methods are excellent in the field of medical image processing. This paper presents a deep learning model based on the convolutional neural network algorithm to provide automatic detection of skin cancer. The model basi

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Shibata, Tomoyuki, Atsushi Teramoto, Hyuga Yamada, Naoki Ohmiya, Kuniaki Saito, and Hiroshi Fujita. "Automated Detection and Segmentation of Early Gastric Cancer from Endoscopic Images Using Mask R-CNN." Applied Sciences 10, no. 11 (2020): 3842. http://dx.doi.org/10.3390/app10113842.

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Gastrointestinal endoscopy is widely conducted for the early detection of gastric cancer. However, it is often difficult to detect early gastric cancer lesions and accurately evaluate the invasive regions. Our study aimed to develop a detection and segmentation method for early gastric cancer regions from gastrointestinal endoscopic images. In this method, we first collected 1208 healthy and 533 cancer images. The gastric cancer region was detected and segmented from endoscopic images using Mask R-CNN, an instance segmentation method. An endoscopic image was provided to the Mask R-CNN, and a b

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Rossi, Farli, and Ashrani Aizzuddin Abd Rahni. "Joint Segmentation Methods of Tumor Delineation in PET – CT Images: A Review." International Journal of Engineering & Technology 7, no. 3.32 (2018): 137. http://dx.doi.org/10.14419/ijet.v7i3.32.18414.

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Segmentation is one of the crucial steps in applications of medical diagnosis. The accurate image segmentation method plays an important role in proper detection of disease, staging, diagnosis, radiotherapy treatment planning and monitoring. In the advances of image segmentation techniques, joint segmentation of PET-CT images has increasingly received much attention in the field of both clinic and image processing. PET - CT images have become a standard method for tumor delineation and cancer assessment. Due to low spatial resolution in PET and low contrast in CT images, automated segmentation

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H N, Swaroopa, Basavaraj N. Jagadale, Omar Abdullah Murshed Farhan Alnaggar, Vijayalakshmi Hegde, and Abhisheka T E. "Human Epithelial Cell Image Analysis and Segmentation using Threshold Based Fusion Technique." Biomedical and Pharmacology Journal 17, no. 1 (2024): 443–52. http://dx.doi.org/10.13005/bpj/2872.

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The most demanding aspect of digital image processing is segmenting an image efficiently. Cell segmentation or classifying cells in an image is essential while analyzing cell images in medical research, especially in spot diagnosis, cancer cell detection, and live-cell imaging segmentation forms a crucial component. This research examines existing segmentation algorithms and suggests a new segmentation technique that employs image filtering and thresholding. Thresholding is an essential part of image analysis and segmentation. Finally, the segmented image and the FCM (fuzzy C-means) based clus

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Pitoy, Pingkan Anggriani, and I. Putu Gede Hendra Suputra. "Dermoscopy Image Segmentation in Melanoma Skin Cancer using Otsu Thresholding Method." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 3 (2021): 397. http://dx.doi.org/10.24843/jlk.2021.v09.i03.p11.

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Melanoma is a skin cancer that originates from melanocytes, melanin-producing cells in the skin. It requires quite a long time to detect melanoma through a biopsy. By utilizing technology, the time required to obtain biopsy results in detection of melanoma can be shortened using image pattern recognition. Segmentation is a stage that affects the results in image analysis for pattern recognition in digital images because of the accuracy of a confident segment in an image analysis. Otsu thresholding is a segmentation method aims to find the threshold point that divides the grayscale image of his

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T.Manivannan, *1 M.Jayakandan 2. "COLORECTAL CANCER DETECTION IN MRI IMAGES USING IMAGE PROCESSING TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 2 (2018): 349–54. https://doi.org/10.5281/zenodo.1173520.

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Cancer is a disease that begins in the cells of the body. Colorectal cancer is cancer that starts in the colon or the rectum. These cancers can also be referred to separately as colon cancer or rectal cancer, depending on where they start.  When the body has extra cell growth it forms a growth or tumor. One of the key problems in the treatment of cancer is the early detection of the disease. Often, cancer is detected in its later stages, when it has compromised the function of one or more vital organ systems and is widespread throughout the body. Methods for the early detection of cancer

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Erandika, D. D. H., and U. A. Piumi Ishanka. "Breast Cancer Detection using Image Processing and Machine Learning: A Comprehensive Review and Improved Segmentation Approach." Sabaragamuwa University Journal 19, no. 2 (2023): 27–45. http://dx.doi.org/10.4038/suslj.v19i2.7791.

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Breast cancer stands as one of the most prevalent health concerns for women. Early detection of breast cancer can significantly improve the chances of survival. In developed countries, more than 19.9% of women will die per year due to breast cancer. Regular breast cancer screening is an important way to detect cancer early. Image processing techniques are highly used for different types of cancer detection applications with medical screening. Segmentation of the breast tumor region is a critical step in image processing related to this manner. Lots of research work can be found on developing w

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Mukherjee, Sudhanshu. "Predicting Malignant Cancer Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1668–72. http://dx.doi.org/10.22214/ijraset.2021.39078.

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Abstract: One of the primary concerns that is also a demanding issue within the realm of medical specialism is the detection and removal of tumours. Because visualisation approaches had the drawback of being adversarial, doctors relied heavily on MRI images to provide a superior result. Pre-processing, tumour segmentation, and tumour operations are the three stages in which tumour image processing takes place. Following the acquisition of the source image, the original image is converted to grayscale. Additionally, a noise removal filter and a median filter for quality development are provided

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Abed, Areej Rebat, and Karim Hussein. "Comparative Analysis of Mammography Image Segmentation Strategies." Journal La Multiapp 3, no. 2 (2022): 37–43. http://dx.doi.org/10.37899/journallamultiapp.v3i2.567.

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Breast cancer is a serious medical problem that affects women all over the world, and it is one of the most well-known tumors that kill women. The specialists of Breast cancer Prefer to use imaging methods such as a mammography to speed up recovery and reduce the risk of breast cancer. An ROI describe the tumor will be retrieved from the image that is entered to detect a malignant tumor. One of the basic techniques used to classify breast cancer is segmentation. Segmentation may be difficult in the presence of noise, blurring or low contrast. Pre-processing aids in the removal of extraneous da

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B, Senthil Kumar, Karpagavalli S, Keerthana K, and Krishnaja A. "AUTOMATIC SEGMENTATION OF COLON CANCER USING SAM AI." Archives for Technical Sciences 2, no. 31 (2024): 296–304. https://doi.org/10.70102/afts.2024.1631.296.

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The third most prevalent form of cancer globally in both men and women is colon cancer which affects the digestive tract and occurs in the human body's greater intestine develops from certain polyps are tiisues that grow inside the colon of various sizes which at later stage can develop into a cancer cell. The tissues are taken from colon using biopsy method and cured. Under a microscope, histopathology images are categorised as a manual screening of colon the study tissue. Size of the nucleus and form of the glands are accepted standards for identifying colon cancer cells. The images obtaimed

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Habibi, Amin, and Mousa Shamsi. "A Novel Color Reduction Based Image Segmentation Technique For Detection Of Cancerous Region in Breast Thermograms." Ciência e Natura 37 (December 19, 2015): 380. http://dx.doi.org/10.5902/2179460x20799.

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Segmentation of an image into its components plays an important role in most of the image processing applications. In this article an important application of image processing in determination of Breast Cancer is studied, and A Novel Image Segmentation Technique is proposed in order to determine Cancer in Breast Thermograms. First, this image is converted from RGB to color space HSV. Then Breast shape is extracted by ACM algorithm. Finally, the image has segmented using Color Reduction Based algorithm. Experimental results on the acquired images show Accuracy of the proposed algorithm on the a

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Pushpalatha, V. "A Novel Framework for Detection of Cervical Cancer." Asian Journal of Engineering and Applied Technology 7, no. 2 (2018): 26–30. http://dx.doi.org/10.51983/ajeat-2018.7.2.1016.

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Today, Uterine Cervical Cancer is most general form of cancer for women. Prevention of cervical cancer is possible via various screening courses. Colposcopy images of cervix are analyzed in this study for the recognition of cervical cancer. An innovative framework is suggested to correctly identify cervical cancer by employing effective pre-processing, image enhancement, and image segmentation techniques. This framework comprises of five phases, (i) Dual tree discrete wavelet transform to pre-process the image (ii) Curvelet transform and contour transform to enhance the image (iii) K-means for

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Orlando, Nathan, Igor Gyacskov, Derek J. Gillies, et al. "Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound." Physics in Medicine & Biology 67, no. 7 (2022): 074002. http://dx.doi.org/10.1088/1361-6560/ac5a93.

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Abstract Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image qua

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Mohd, Firdaus Abdullah, Noraini Sulaiman Siti, Khusairi Osman Muhammad, Khairiah Abdul Karim Noor, Setumin Samsul, and Sazanita Isa Iza. "A new procedure for lung region segmentation from computed tomography images." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4978–87. https://doi.org/10.11591/ijece.v12i5.pp4978-4987.

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Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Fi

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Zhang, Jinling, Jun Yang, and Min Zhao. "Automatic Segmentation Algorithm of Magnetic Resonance Image in Diagnosis of Liver Cancer Patients under Deep Convolutional Neural Network." Scientific Programming 2021 (September 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/4614234.

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To study the influence of different sequences of magnetic resonance imaging (MRI) images on the segmentation of hepatocellular carcinoma (HCC) lesions, the U-Net was improved. Moreover, deep fusion network (DFN), data enhancement strategy, and random data (RD) strategy were introduced, and a multisequence MRI image segmentation algorithm based on DFN was proposed. The segmentation experiments of single-sequence MRI image and multisequence MRI image were designed, and the segmentation result of single-sequence MRI image was compared with those of convolutional neural network (FCN) algorithm. In

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Abdullah, Mohd Firdaus, Siti Noraini Sulaiman, Muhammad Khusairi Osman, et al. "GEOMETRICAL FEATURE OF LUNG LESION IDENTIFICATION USING COMPUTED TOMOGRAPHY SCAN IMAGES." Jurnal Teknologi 85, no. 2 (2023): 149–56. http://dx.doi.org/10.11113/jurnalteknologi.v85.18828.

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Lung lesion identification is an important aspect of an early lung cancer diagnosis. Early identification of lung cancer may assist physicians in treating patients. This paper uses computed tomography scan images to present a lung lesion identification geometrical feature. From the previous studies, lung segmentation is particularly challenging because differences in pulmonary inflation with an elastic chest wall can result in significant variability in volumes and margins when attempting to automate lung segmentation. Besides, the features used to describe a lung lesion focus on image feature

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Sammouda, Rachid, and Ali El-Zaart. "An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method." Computational Intelligence and Neuroscience 2021 (November 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/4553832.

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Prostate cancer disease is one of the common types that cause men’s prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to res

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Shawon, Mayinuzzaman, Kazi Fakhrul Abedin, Anik Majumder, Abir Mahmud, and Md Mahbub Chowdhury Mishu. "Identification of Risk of Occurring Skin Cancer (Melanoma) Using Convolutional Neural Network (CNN)." AIUB Journal of Science and Engineering (AJSE) 20, no. 2 (2021): 47–51. http://dx.doi.org/10.53799/ajse.v20i2.140.

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Skin cancer is one of the most common malignancy in human, has drawn attention from researchers around the world. As skin cancer can turn into fatal if not treated in its earliest stages, the necessity of devising automated skin cancer diagnosis system that can automatically detect skin cancer efficiently in its earliest stage in a faster process than traditional one is of crucial importance. In this paper, a computer aided skin cancer diagnosis system based Convolutional Neural Network method has been shown. Our proposed system consists of five stages namely image acquisition, image preproces

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Heidari, Zeinab, Mehrdad Dadgostar, and Zahra Einalou. "AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES." Biomedical Engineering: Applications, Basis and Communications 30, no. 03 (2018): 1850024. http://dx.doi.org/10.4015/s1016237218500242.

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Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to u

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Zhang, Yan Ling, Yue Jia Zhang, and Li Li. "Lymph Node Image Segmentation Based on Improved FCM Clustering and Multi-Threshold." Advanced Materials Research 760-762 (September 2013): 1510–14. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1510.

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The pathological change of lymph node is an important basis of malignant tumor detection and judgment of metastasis of cancer (lung cancer, colorectal cancer, breast cancer, liver cancer, cervical cancer, etc.) An algorithm of lymph node image segmentation based on improved FCM clustering and multi-threshold is proposed to segment the lymph CT image with blurred edge. First, the improved FCM peak clustering is used to sharpen the fuzzy boundary of lymph CT image effectively. Then the multi-threshold algorithm based on image entropy change is introduced to segment enhanced images. The experimen

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Muhimmah, Izzati, Dadang Heksaputra, and Indrayanti. "Color feature extraction of HER2 Score 2+ overexpression on breast cancer using Image Processing." MATEC Web of Conferences 154 (2018): 03016. http://dx.doi.org/10.1051/matecconf/201815403016.

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One of the major challenges in the development of early diagnosis to assess HER2 status is recognized in the form of Gold Standard. The accuracy, validity and refraction of the Gold Standard HER2 methods are widely used in laboratory (Perez, et al., 2014). Method determining the status of HER2 (human epidermal growth factor receptor 2) is affected by reproductive problems and not reliable in predicting the benefit from anti-HER2 therapy (Nuciforo, et al., 2016). We extracted color features by methods adopting Statistics-based segmentation using a continuous-scale naïve Bayes approach. In this

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Agita, T. K. R., and M. Moorthi. "Liver Cancer Detection and Classification Using Raspberry Pi." Journal of Medical Imaging and Health Informatics 12, no. 3 (2022): 230–37. http://dx.doi.org/10.1166/jmihi.2022.3941.

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In practical radiology, early diagnosis and precise categorization of liver cancer are difficult issues. Manual segmentation is also a time-consuming process. So, utilizing various methodologies based on an embedded system, we detect liver cancer from abdominal CT images using automated liver cancer segmentation and classification. The objective is to categorize CT scan images of primary and secondary liver disease using a Back Propagation Neural Network (BPNN) classifier, which has greater accuracy than previous approaches. In this work, a newly proposed method is shown which has four phases:

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Brar, Raman. "Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (2012): 253–55. http://dx.doi.org/10.24297/ijct.v3i2a.2816.

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Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

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Sammouda, Mohamed, Rachid Sammouda, Noboru Niki, and Kiyoshi Mukai. "Liver Cancer Detection System Based on the Analysis of Digitized Color Images of Tissue Samples Obtained Using Needle Biopsy." Information Visualization 1, no. 2 (2002): 130–38. http://dx.doi.org/10.1057/palgrave.ivs.9500012.

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In this article, the authors propose a method for automatic diagnosis of liver cancer based on analysis of digitized color images of liver tissue obtained by needle biopsy. The approach is a combination of an unsupervised segmentation algorithm, using a modified artificial Hopfield neural network (HNN), and an analysis algorithm based on image quantization. The segmentation algorithm is superior to HNN in the sense that it converges to a nearby global minimum rather than a local one in a prespecified time. Furthermore, as the segmentation of color images does not only depend on the segmentatio

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Maulida, Atika, Ramacos Fardela, and Dina Arfiani Rusjdi. "Automated Breast Cancer Lesion Segmentation and Diameter Detection in Mammogram Images Using Active Contour Lankton with MATLAB GUI." Jurnal Ilmiah Pendidikan Fisika Al-Biruni 14, no. 1 (2025): 155–65. https://doi.org/10.24042/jipfalbiruni.v14i1.23907.

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Breast cancer is the most commonly diagnosed cancer among women worldwide and remains one of the leading causes of cancer-related deaths, accounting for approximately 10 million deaths in 2020. Diagnosis is generally performed through routine examinations or when symptoms appear; however, physical examination alone is often insufficient. Image segmentation techniques are increasingly utilized to enhance diagnostic accuracy. This study aims to develop a MATLAB-based program using the Graphical User Interface (GUI) and the Active Contour Lankton method to perform segmentation on mammogram images

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Fulton, Lawrence, Alex McLeod, Diane Dolezel, Nathaniel Bastian, and Christopher P. Fulton. "Deep Vision for Breast Cancer Classification and Segmentation." Cancers 13, no. 21 (2021): 5384. http://dx.doi.org/10.3390/cancers13215384.

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(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss func

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Khoiro, M., R. A. Firdaus, E. Suaebah, M. Yantidewi, and Dzulkiflih. "Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network." Journal of Physics: Conference Series 2392, no. 1 (2022): 012024. http://dx.doi.org/10.1088/1742-6596/2392/1/012024.

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Abstract The effect of segmentation on lung X-ray image classification has been analyzed in this study. The 150 lung x-ray images in this study were separated into 78 as training data, 30 as validation data, and 42 as testing in three categories: normal lungs, effusion lungs, and cancer lungs. In pre-processing, the images were modified by adaptive histogram equalization to improve image quality and increase image contrast. The segmentation aims to mark the image by contouring the lung area obtained from the thresholding and some morphological manipulation processes such as filling holes, area

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Jing, Wang, Liew Siau Chuin, and Azian Abd Aziz. "Model-based hybrid variational level set method applied to lung cancer detection." Journal of Autonomous Intelligence 7, no. 5 (2024): 921. http://dx.doi.org/10.32629/jai.v7i5.921.

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<p>The precise segmentation of lung lesions in computed tomography (CT) scans holds paramount importance for lung cancer research, offering invaluable information for clinical diagnosis and treatment. Nevertheless, achieving efficient detection and segmentation with acceptable accuracy proves to be challenging due to the heterogeneity of lung nodules. This paper presents a novel model-based hybrid variational level set method (VLSM) tailored for lung cancer detection. Initially, the VLSM introduces a scale-adaptive fast level-set image segmentation algorithm to address the inefficiency o

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