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Journal articles on the topic 'Tissue image segmentation'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Lee, Seung Hyeun, Sanghyuck Lee, Jaesung Lee, Jeong Kyu Lee, and Nam Ju Moon. "Effective encoder-decoder neural network for segmentation of orbital tissue in computed tomography images of Graves’ orbitopathy patients." PLOS ONE 18, no. 5 (2023): e0285488. http://dx.doi.org/10.1371/journal.pone.0285488.

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Purpose To propose a neural network (NN) that can effectively segment orbital tissue in computed tomography (CT) images of Graves’ orbitopathy (GO) patients. Methods We analyzed orbital CT scans from 701 GO patients diagnosed between 2010 and 2019 and devised an effective NN specializing in semantic orbital tissue segmentation in GO patients’ CT images. After four conventional (Attention U-Net, DeepLab V3+, SegNet, and HarDNet-MSEG) and the proposed NN train the various manual orbital tissue segmentations, we calculated the Dice coefficient and Intersection over Union for comparison. Results C

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Muñoz-Aguirre, Manuel, Vasilis F. Ntasis, Santiago Rojas, and Roderic Guigó. "PyHIST: A Histological Image Segmentation Tool." PLOS Computational Biology 16, no. 10 (2020): e1008349. http://dx.doi.org/10.1371/journal.pcbi.1008349.

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The development of increasingly sophisticated methods to acquire high-resolution images has led to the generation of large collections of biomedical imaging data, including images of tissues and organs. Many of the current machine learning methods that aim to extract biological knowledge from histopathological images require several data preprocessing stages, creating an overhead before the proper analysis. Here we present PyHIST (https://github.com/manuel-munoz-aguirre/PyHIST), an easy-to-use, open source whole slide histological image tissue segmentation and preprocessing command-line tool a

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Li, Qun, and Linlin Liu. "Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation." Computational Intelligence and Neuroscience 2022 (June 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/3500592.

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In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificia

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Kong, Zhenglun, Ting Li, Junyi Luo, and Shengpu Xu. "Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning." Journal of Healthcare Engineering 2019 (January 31, 2019): 1–10. http://dx.doi.org/10.1155/2019/2912458.

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Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies, or other novel imaging technologies. In addition, image segmentation also provides detailed structural description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation methods. Here, we first use some preprocessing methods such as wavelet denoising to extract the accurate contours of different tissues such as skull, cerebrospinal flu

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N, Nazeeya Anjum. "A Study on Segmenting Brain Tumor MRI Images." Journal of Computational Science and Intelligent Technologies 2, no. 1 (2021): 1–6. http://dx.doi.org/10.53409/mnaa/jcsit/2101.

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Segmentation of image has traditionally been referred to as the initial stage in image processing. A successful segmentation output will make image processing analysis considerably further easier. There are several image segmentation techniques and methodologies available. Clustering is the most widely used segmentation algorithm for image processing. Segmentation of tumor using magnetic resonance imaging (MRI) data is a critical procedure yet time-consuming process manually carried out by medical specialists. Due to the considerable difference in the tumor tissue appearances across patients,

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Ortiz, Andrés, Antonio A. Palacio, Juan M. Górriz, Javier Ramírez, and Diego Salas-González. "Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors." Computational and Mathematical Methods in Medicine 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/638563.

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Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD) systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on

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Liu, Jinhua, Yongsheng Shi, Dongjin Huang, and Jiantao Qu. "Neural Radiance Fields for High-Fidelity Soft Tissue Reconstruction in Endoscopy." Sensors 25, no. 2 (2025): 565. https://doi.org/10.3390/s25020565.

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The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft tissue regions by invalid pixels, deformations in soft tissue, and poor image quality, which severely limits their application in endoscopic scenarios. To address the above issues, we propose a novel framework to reconstruct high-fidelity soft tissue scenes from low-quality endoscopic images. We first construct an EndoTissue dataset of soft tissue

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Hamghalam, Mohammad, Baiying Lei, and Tianfu Wang. "High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4067–74. http://dx.doi.org/10.1609/aaai.v34i04.5825.

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Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic field. Despite the non-invasive advantage of MRI in frequent imaging, the low contrast MR images in the target area make tissue segmentation a challenging problem. This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle generative adversarial network (CycleGAN) with an attention mechanism to increase the contrast within underlying tissues. The attention block,

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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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Ramesh, Patil Vinodkumar, Jaware Tushar Hrishikesh, and Manisha S. Patil. "Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 1s (2023): 71–79. http://dx.doi.org/10.17762/ijritcc.v11i1s.6002.

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Infant MRI brain soft tissue segmentation become more difficult task compare with adult MRI brain tissue segmentation, due to Infant’s brain have a very low Signal to noise ratio among the white matter_WM and the gray matter _GM. Due the fast improvement of the overall brain at this time , the overall shape and appearance of the brain differs significantly. Manual segmentation of anomalous tissues is time-consuming and unpleasant. Essential Feature extraction in traditional machine algorithm is based on experts, required prior knowledge and also system sensitivity has change. Recently, bio-med

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More, Sujeet, Jimmy Singla, Ahed Abugabah, and Ahmad Ali AlZubi. "Machine Learning Techniques for Quantification of Knee Segmentation from MRI." Complexity 2020 (December 7, 2020): 1–13. http://dx.doi.org/10.1155/2020/6613191.

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Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process i

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Gao, Jingjing, Mei Xie, and Yan Zhou. "INTERLEAVED EM SEGMENTATION FOR MR IMAGE WITH INTENSITY INHOMOGENEITY." Biomedical Engineering: Applications, Basis and Communications 26, no. 05 (2014): 1450058. http://dx.doi.org/10.4015/s1016237214500586.

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Expectation–maximization (EM) algorithm has been extensively applied in brain MR image segmentation. However, the conventional EM method usually leads to severe misclassifications MR images with bias field, due to the significant intensity inhomogeneity. It limits the applications of the conventional EM method in MR image segmentation. In this paper, we proposed an interleaved EM method to perform tissue segmentation and bias field estimation. In the proposed method, the tissue segmentation is performed by the modified EM classification, and the bias field estimation is accomplished by an ener

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Suja G, Et al. "Enhancing Alzheimer Disease Segmentation through Adaptively Regularized Weighted Kernel-Based Clustering." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1410–21. http://dx.doi.org/10.17762/ijritcc.v11i10.8685.

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Image segmentation is important in image analysis because it helps to locate objects and boundaries within a picture. This study offers Adaptively Regularized Weighted Kernel-Based Clustering (ARWKC), a unique segmentation technique built exclusively for recovering brain tissue from medical pictures. The proposed approach incorporates adaptive regularization and weighted kernel-based clustering techniques to increase the accuracy and resilience of brain tissue segmentation. The picture is initially preprocessed with the ARWKC method to improve its quality and eliminate any noise or artifacts.

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Yahya, Rafaa I., Siti Mariyam Shamsuddin, Salah I. Yahya, Bisan Alsalibi, and Ghada K. Al-Khafaji. "Membrane Computing for Real Medical Image Segmentation." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 6, no. 2 (2018): 27. http://dx.doi.org/10.14500/aro.10442.

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In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in

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Sahalan, Mariaulpa, Aidil Munir Mazlee, Farah Nabila Mustafa Amirrudin, et al. "Auto Segmentation of Lymph Node Microscopy Images." Journal of Medical Device Technology 1, no. 1 (2022): 50–55. http://dx.doi.org/10.11113/jmeditec.v1n1.17.

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The manual histology assessment on the biopsy tissue sample still remains the gold standard procedure for cancer and its progression in human body. Auto nuclei segmentation is an important method to measure cellularity but often suffered an issue due to the present of overlapping nuclei. The implementation of auto segmentation of cells could speed up the process of histology assessment for cancer cases. The first step to implement, a wide data profile of normal and cancerous need to be compile and analyze further as a reference guide. Tissue data profile can be collected based on cellularity p

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Noble, J. A. "Ultrasound image segmentation and tissue characterization." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 224, no. 2 (2009): 307–16. http://dx.doi.org/10.1243/09544119jeim604.

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Lee, Jiann-Shu, and Wen-Kai Wu. "Breast Tumor Tissue Image Classification Using DIU-Net." Sensors 22, no. 24 (2022): 9838. http://dx.doi.org/10.3390/s22249838.

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Inspired by the observation that pathologists pay more attention to the nuclei regions when analyzing pathological images, this study utilized soft segmentation to imitate the visual focus mechanism and proposed a new segmentation–classification joint model to achieve superior classification performance for breast cancer pathology images. Aiming at the characteristics of different sizes of nuclei in pathological images, this study developed a new segmentation network with excellent cross-scale description ability called DIU-Net. To enhance the generalization ability of the segmentation network

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Devi, B. Sudha, and D. S. Misbha. "Segmentation of Visceral Adipose Tissue causing Central Obesity using Deep Learning on Abdominal MRI." International Journal on Cybernetics & Informatics 10, no. 2 (2021): 65–72. http://dx.doi.org/10.5121/ijci.2021.100208.

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In recent years, obesity is highly prevalent and is related with increased risk of many diseases. The distribution of abdominal adipose tissue plays a major role to assess central obesity. The basic objective of this study is to develop a novel method for automatic segmentation of visceral adipose tissue(VAT) and subcutaneous adipose tissue(SAT) from abdominal Magnetic resonance imaging(MRI) slices which is implemented in two steps. First, clustering of image is done to classify MR image into adipose tissue and non-adipose tissue. Second, after clustering the image, segmentation is done to sep

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Chen, Yen Sheng, Shao Hsien Chen, and Jeih Jang Liou. "Comparison of Multispectral Image Processing Techniques to Brain MR Image Classification." Applied Mechanics and Materials 182-183 (June 2012): 1998–2002. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1998.

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Brain Magnetic Resonance Imaging (MRI) has become a widely used modality because it produces multispectral image sequences that provide information of free water, proteinaceous fluid, soft tissue and other tissues with a variety of contrast. The abundance fractions of tissue signatures provided by multispectral images can be very useful for medical diagnosis compared to other modalities. Multiple Sclerosis (MS) is thought to be a disease in which the patient immune system damages the isolating layer of myelin around the nerve fibers. This nerve damage is visible in Magnetic Resonance (MR) scan

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Robota, D. V., and B. S. Burlaka. "SELECTING AN EFFECTIVE METHOD OF COLOR NORMALIZATION FOR HISTOLOGICAL IMAGES OF INTESTINAL TISSUES IN DEEP LEARNING MODEL DEVELOPMENT." Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії 25, no. 1 (2025): 203–10. https://doi.org/10.31718/2077-1096.25.1.203.

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The advancement of modern computer technologies opens new opportunities for the automated analysis of whole-slide histological images. This is made possible by digital pathology approaches and artificial intelligence methods, particularly machine learning and deep learning. One of the key challenges in this process is the significant variability in the color of histological images. This variability arises from different staining techniques, the characteristics of laboratory equipment used for scanning specimens, and the individual properties of patient tissues. These factors can significantly

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Alaba, AJALA Funmilola, AKANDE Noah Oluwatobi, ADEYEMO Isiaka Akinkunmi, and Ogundokun Roseline Oluwaseun. "Smallest Univalue Segment Assimilating Nucleus approach to Brain MRI Image Segmentation using Fuzzy C-Means and Fuzzy K-Means Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 7 (2017): 7065–76. http://dx.doi.org/10.24297/ijct.v16i7.6170.

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Image segmentation still remains an important task in image processing and analysis. Sequel to any segmentation process, preprocessing activities carried out on the images have a great effect on the accuracy of the segmentation task. This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms. Subsequently, brain tissue extraction tool was employed in extracting non-brain tissues from the brain image. Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (F

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Wei, Yun Tao, and Yi Bing Zhou. "Segmentations of Liver and Hepatic Tumors from 3D Computed Tomography Abdominal Images." Advanced Materials Research 898 (February 2014): 684–87. http://dx.doi.org/10.4028/www.scientific.net/amr.898.684.

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The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images for liver segmentation. An adaptive initialization method was developed to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms. This method was applied to abdominal Computed Tomography (CT) images for segmentation of liver tissue and hepatic tumors. Twenty-five anonymized datasets were randomly collected from seve

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Lian, Jie, Mingyu Zhang, Na Jiang, Wei Bi, and Xiaoqiu Dong. "Feature Extraction of Kidney Tissue Image Based on Ultrasound Image Segmentation." Journal of Healthcare Engineering 2021 (April 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/9915697.

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The kidney tissue image is affected by other interferences in the tissue, which makes it difficult to extract the kidney tissue image features, and it is difficult to judge the lesion characteristics and types by intelligent feature recognition. In order to improve the efficiency and accuracy of feature extraction of kidney tissue images, refer to the ultrasonic heart image for analysis and then apply it to the feature extraction of kidney tissue. This paper proposes a feature extraction method based on ultrasound image segmentation. Moreover, this study combines the optical flow method and th

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Kolli, Aravind, Qi Wei, and Stephen A. Ramsey. "Predicting Time-to-Healing from a Digital Wound Image: A Hybrid Neural Network and Decision Tree Approach Improves Performance." Computation 12, no. 3 (2024): 42. http://dx.doi.org/10.3390/computation12030042.

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Despite the societal burden of chronic wounds and despite advances in image processing, automated image-based prediction of wound prognosis is not yet in routine clinical practice. While specific tissue types are known to be positive or negative prognostic indicators, image-based wound healing prediction systems that have been demonstrated to date do not (1) use information about the proportions of tissue types within the wound and (2) predict time-to-healing (most predict categorical clinical labels). In this work, we analyzed a unique dataset of time-series images of healing wounds from a co

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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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Li, Qingyun, Zhibin Yu, Yubo Wang, and Haiyong Zheng. "TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation." Sensors 20, no. 15 (2020): 4203. http://dx.doi.org/10.3390/s20154203.

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The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we i

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Li, Kai, Xenophon Papademetris, and Don M. Tucker. "BrainK for Structural Image Processing: Creating Electrical Models of the Human Head." Computational Intelligence and Neuroscience 2016 (2016): 1–25. http://dx.doi.org/10.1155/2016/1349851.

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BrainK is a set of automated procedures for characterizing the tissues of the human head from MRI, CT, and photogrammetry images. The tissue segmentation and cortical surface extraction support the primary goal of modeling the propagation of electrical currents through head tissues with a finite difference model (FDM) or finite element model (FEM) created from the BrainK geometries. The electrical head model is necessary for accurate source localization of dense array electroencephalographic (dEEG) measures from head surface electrodes. It is also necessary for accurate targeting of cerebral s

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O'Neill, Brenna, Smriti Kala, Sam Lim, et al. "Abstract 4625: A comprehensive guided workflow for highplex imaging, tissue segmentation, and multiplex cellular phenotyping for tumor microenvironment analysis." Cancer Research 83, no. 7_Supplement (2023): 4625. http://dx.doi.org/10.1158/1538-7445.am2023-4625.

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Abstract The growth in cancer immunotherapy agents requires an understanding of the immune contexture of the tumor microenvironment (TME). Understanding immune contexture requires multiplex staining, imaging, and analysis to obtain multi-marker phenotypes of specific cells and analyze their biodistribution in the TME. Imaging Mass Cytometry™ (IMC) is the method of choice for single-step staining and highplex imaging of FFPE tissues. FFPE tissue is autofluorescent, which limits the utility of immunofluorescence methods. Lung and colorectal tissue (and bone, skin, etc) are highly autofluorescent

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Song, Jianhua, and Lei Yuan. "Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field." Mathematical Biosciences and Engineering 19, no. 2 (2021): 1891–908. http://dx.doi.org/10.3934/mbe.2022089.

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<abstract> <p>The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful task because it provides a diagnosis and treatment basis for observing brain tissue development, delineating lesions, and planning surgery. However, MRI images are often damaged by factors such as noise, low contrast and intensity brightness, which seriously affect the accuracy of segmentation. A non-local fuzzy c-means clustering framework incorporating the Markov random field for brain tissue segmentation is proposed in this paper. Firstly, according to the statistica

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Zehntner, Simone P., M. Mallar Chakravarty, Rozica J. Bolovan, Christopher Chan, and Barry J. Bedell. "Synergistic Tissue Counterstaining and Image Segmentation Techniques for Accurate, Quantitative Immunohistochemistry." Journal of Histochemistry & Cytochemistry 56, no. 10 (2008): 873–80. http://dx.doi.org/10.1369/jhc.2008.950345.

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Quantitative analysis of digitized IHC-stained tissue sections is increasingly used in research studies and clinical practice. Accurate quantification of IHC staining, however, is often complicated by conventional tissue counterstains caused by the color convolution of the IHC chromogen and the counterstain. To overcome this issue, we implemented a new counterstain, Acid Blue 129, which provides homogeneous tissue background staining. Furthermore, we combined this counterstaining technique with a simple, robust, fully automated image segmentation algorithm, which takes advantage of the high de

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Lor, Kuo-Lung, and Chung-Ming Chen. "FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES." Biomedical Engineering: Applications, Basis and Communications 33, no. 02 (2021): 2150012. http://dx.doi.org/10.4015/s1016237221500125.

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The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from

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Sandhya, G., Kande Giri Babu, and T. Satya Savithri. "An Efficient Computational Approach for the Detection of MR Brain Tissues in the Presence of Noise and Intensity Inhomogeneity." Journal of Biomimetics, Biomaterials and Biomedical Engineering 33 (July 2017): 65–79. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.33.65.

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The automatic detection of brain tissues such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) from the MR images of the brain using segmentation is of immense interest for the early detection and diagnosing various brain-related diseases. MR imaging technology is one of the best and most reliable ways of studying the brain. Segmentation of MR images is a challenging task due to various artifacts such as noise, intensity inhomogeneity, partial volume effects and elemental texture of the image. This work proposes a region based, efficient and modern energy minimization proc

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Skalski, Andrzej, and Paweł Turcza. "Heart Segmentation in Echo Images." Metrology and Measurement Systems 18, no. 2 (2011): 305–14. http://dx.doi.org/10.2478/v10178-011-0012-y.

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Heart Segmentation in Echo ImagesCardiovascular system diseases are the major causes of mortality in the world. The most important and widely used tool for assessing the heart state is echocardiography (also abbreviated as ECHO). ECHO images are used e.g. for location of any damage of heart tissues, in calculation of cardiac tissue displacement at any arbitrary point and to derive useful heart parameters like size and shape, cardiac output, ejection fraction, pumping capacity. In this paper, a robust algorithm for heart shape estimation (segmentation) in ECHO images is proposed. It is based on

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Gaddipati, A., D. G. Vince, R. M. Cothren, and J. F. Cornhill. "Automated Color Segmentation for Quantitative Microscopy." Microscopy and Microanalysis 3, S2 (1997): 229–30. http://dx.doi.org/10.1017/s1431927600008035.

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Over the past decade, there has been increased interest in quantifying cell populations and morphological structures in tissue sections using image analysis systems. Automated analysis is now being used in limited pathological applications, such as PAP smear evaluation, with the dual aim of increasing the accuracy of diagnosis and reducing the review time. Applications such as these primarily use gray scale images and deal with cells that are well separated. Quantification of routinely stained tissue represents a more difficult problem in that objects can not be separated in gray scale and the

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Attia, Salim J. "Detection of malignant Cases by Segmentation of Cells in Medical Images and Applying Fuzzy Logic Technique." University of Thi-Qar Journal of Science 4, no. 4 (2014): 71–74. http://dx.doi.org/10.32792/utq/utjsci/v4i4.669.

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The process of detection and segmentation of cells is considered in digital optical images of human breast tissue as important base to diagnose the diseases. The major features of malignancy are related with the cells. It is therefore essential to operate a segmentation of the image, to isolate the cells from the rest of image, i. e., from other tissue components, and from some other undesirable elements of images. The recognition process includes a segmentation algorithm based on an adaptive imaging threshold procedure that is sensitive to local ranges in pixel intensity (minimum-maximum valu

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Sun, Hongyu, Qi Zhang, Qiangdi Zhang, and Zifeng Zhou. "Function tissue unit segmentation based on UNext model." Theoretical and Natural Science 5, no. 1 (2023): 601–6. http://dx.doi.org/10.54254/2753-8818/5/20230386.

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Accurate segmentation for Functional Tissue Units (FTUs) is a challenging issue in past decades. In this study, a model using the dataset of tissue section images will be built to evaluate and mark FTUs across five human organs as clearly as possible. We have the Human Protein Atlas (HPA) as training data and the data from Human BioMolecular Atlas Program (HuBMAP) as testing data. To balance accuracy and inference speed, this study applied Unext, an efficient network based on Unet, as the basic model. We also aim to use some tricks to further improve the performance of the model. First, we use

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Chen, Shaolong, Changzhen Qiu, Weiping Yang, and Zhiyong Zhang. "Multiresolution Mutual Assistance Network for Cardiac Magnetic Resonance Images Segmentation." Journal of Healthcare Engineering 2022 (October 31, 2022): 1–10. http://dx.doi.org/10.1155/2022/5311825.

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The automatic segmentation of cardiac magnetic resonance (MR) images is the basis for the diagnosis of cardiac-related diseases. However, the segmentation of cardiac MR images is a challenging task due to the inhomogeneity of MR images intensity distribution and the unclear boundaries between adjacent tissues. In this paper, we propose a novel multiresolution mutual assistance network (MMA-Net) for cardiac MR images segmentation. It is mainly composed of multibranch input module, multiresolution mutual assistance module, and multilabel deep supervision. First, the multibranch input module help

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Nugroho, Arief Kelik, Afiah ayati, Moh Edi Wibowo, Hardyanto Soebono, and Retantyo Wardoyo. "Skin Lesion Segmentation Using Adaptive Color Segmentation and Decision Tree." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 3 (2024): 109–24. http://dx.doi.org/10.58346/jowua.2024.i3.008.

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A decision tree operates as a predictive model in machine learning, utilizing a structured hierarchy of rules for the decision-making process. This model is graphically depicted through a tree-like structure, where each node symbolizes a decision point, and the ensuing branches represent the resultant outcomes of these decisions. This method proves particularly efficacious in addressing both classification and regression challenges, offering clear interpretability through its graphical illustration of the connections between input features and predicted outcomes. In the domain of image process

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Cheng, Hao, Kaijie Wu, Jie Tian, Kai Ma, Chaocheng Gu, and Xinping Guan. "Colon tissue image segmentation with MWSI-NET." Medical & Biological Engineering & Computing 60, no. 3 (2022): 727–37. http://dx.doi.org/10.1007/s11517-022-02501-7.

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Koduru*, Gagan Kumar, Kuda Nageswararao, and Anupama Namburu. "T1 Weighted MR Brain Image Segmentation with Triangular Intuitionistic Fuzzy Set." International Journal of Innovative Technology and Exploring Engineering 9, no. 4 (2020): 762–68. http://dx.doi.org/10.35940/ijitee.c8384.029420.

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Segmentation of medical image is a very important step in processing of image to help examination of diseases. The early detection of ailments from the normal diseases is essential for the physicist to stop and provide treatment. Increasing Cerebrospinal fluid in brain causes dementia which is an increasing mostly prevalent now days. Segmentation of brain images is a challenge due to the existence of noise and intensity in-homogeneity that creates hesitation in segmenting the tissues. This paper is about a fresh segmentation method that uses triangular membership function to distinguish the ea

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Nayyef, Rasha Helmi, and Mohammed S. H. Al-Tammi. "Skull Stripping Based on the Segmentation Models." Journal of Engineering 29, no. 10 (2023): 74–89. http://dx.doi.org/10.31026/j.eng.2023.10.05.

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Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for process

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Scherf, N., J. Einenkel, L. C. Horn, et al. "Large Histological Serial Sections for Computational Tissue Volume Reconstruction." Methods of Information in Medicine 46, no. 05 (2007): 614–22. http://dx.doi.org/10.1160/me9065.

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Summary Objectives: A proof of principle study was conducted for microscopic tissue volume reconstructions using a new image processing chain operating on alternately stained large histological serial sections. Methods: Digital histological images were obtained from conventional brightfield transmitted light microscopy. A powerful nonparametric nonlinear optical flow-based registration approach was used. In order to apply a simple but computationally feasible sum-of-squared-differences similarity measure even in case of differing histological stainings, a new consistent tissue segmentation pro

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Huo, Chun Bao, Shuai Tong, Li Hui Zhao, and Xiang Yun Li. "Research on Image Segmentation Technology with Tissue Section Cell Segmentation Algorithm." Advanced Materials Research 1046 (October 2014): 88–91. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.88.

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Generally, the effect of cell image that segmented via the threshold value method is not ideal generally; the found cell boundary cannot conform to the cell edge in the original picture well. In this paper, the threshold value segmentation method is improved; apply the judging criterion of gray level difference maximum interval to be the minimum, and conduct secondary treating on the image, and the image’s segmentation effect is more ideal.

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Cárdenas-Peña, David, Eduardo Fernández, José M. Ferrández-Vicente, and German Castellanos-Domínguez. "Multi-atlas label fusion by using supervised local weighting for brain image segmentation." TecnoLógicas 20, no. 39 (2017): 209–25. http://dx.doi.org/10.22430/22565337.724.

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The automatic segmentation of interest structures is devoted to the morphological analysis of brain magnetic resonance imaging volumes. It demands significant efforts due to its complicated shapes and since it lacks contrast between tissues and intersubject anatomical variability. One aspect that reduces the accuracy of the multi-atlasbased segmentation is the label fusion assumption of one-to-one correspondences between targets and atlas voxels. To improve the performance of brain image segmentation, label fusion approaches include spatial and intensity information by using voxel-wise weighte

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Ortiz, A., J. M. Gorriz, J. Ramirez, and D. Salas-Gonzalez. "Unsupervised Neural Techniques Applied to MR Brain Image Segmentation." Advances in Artificial Neural Systems 2012 (June 7, 2012): 1–7. http://dx.doi.org/10.1155/2012/457590.

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The primary goal of brain image segmentation is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting, since accurate segmentation in white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders such as dementia, schizophrenia or Alzheimer’s disease (AD). Then, image segmentation results in a very interesting tool for neuroanatomical analyses. In this paper we show three alternatives to MR brain image segmentation algorithms, with the Self-Organizing

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Amgad, Mohamed, Habiba Elfandy, Hagar Hussein, et al. "Structured crowdsourcing enables convolutional segmentation of histology images." Bioinformatics 35, no. 18 (2019): 3461–67. http://dx.doi.org/10.1093/bioinformatics/btz083.

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Abstract Motivation While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. Results We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-part

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Hu, Jiankun. "H. -T. Wu, K. Zheng, Q. Huang and J. Hu, "Contrast Enhancement of Multiple Tissues in MR Brain Images With Reversibility," in IEEE Signal Processing Letters, vol. 28, pp. 160-164, 2021, doi: 10.1109/LSP.2020.3048840." IEEE Signal Processing Letters 28 (April 11, 2024): 160–64. https://doi.org/10.5281/zenodo.10957974.

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Abstract: Contrast enhancement (CE) of magnetic resonance (MR) brain images is an important technique to bring out the tissue details for clinical diagnosis. Recently, a new form of image enhancement has been proposed to complete the task without any information loss. Specifically, information required to restore the original image is reversibly hidden into the enhanced image. Moreover, several image segmentation based algorithms have been proposed so that the region of interest can be exclusively enhanced. However, with the reversible algorithms, it is hard to properly enhance the tissues in

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Rezaei Tabar, Yousef, and Ilkay Ulusoy. "The Effect of Labeled/Unlabeled Prior Information for Masseter Segmentation." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/928469.

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Several segmentation methods are implemented and applied to segment the facial masseter tissue from magnetic resonance images. The common idea for all methods is to take advantage of prior information from different MR images belonging to different individuals in segmentation of a test MR image. Standard atlas-based segmentation methods and probabilistic segmentation methods based on Markov random field use labeled prior information. In this study, a new approach is also proposed where unlabeled prior information from a set of MR images is used to segment masseter tissue in a probabilistic fra

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Teng, Lin, Hang Li, and Shahid Karim. "DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation." Journal of Healthcare Engineering 2019 (December 27, 2019): 1–10. http://dx.doi.org/10.1155/2019/8597606.

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Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this artic

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Stevens, Courtney R., Josh Berenson, Michael Sledziona, Timothy P. Moore, Lynn Dong, and Jonathan Cheetham. "Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle." PLOS ONE 15, no. 12 (2020): e0243163. http://dx.doi.org/10.1371/journal.pone.0243163.

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Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger’s line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed

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