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Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting

Hui Kong, M. Gurcan, K. Belkacem-Boussaid
2011 IEEE Transactions on Medical Imaging  
To this end, we propose an integrated framework consisting of a novel supervised cell-image segmentation algorithm and a new touching-cell splitting method.  ...  For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing them by classification  ...  Olcay Sertel and Dr. Gerard Lozanski for their helpful suggestions.  ... 
doi:10.1109/tmi.2011.2141674 pmid:21486712 pmcid:PMC3165069 fatcat:fwiq4wsjkfgkjcfs237zleztwq

Review of Nuclei Detection, Segmentation in Microscopic Images

Rujuta O, Vyavahare AJ
2017 Journal of Bioengineering and Biomedical Sciences  
This paper is a review of some recent state-of-art nucleus/cell segmentation approaches on different types of microscopic images.  ...  Recently some automatic methods have been evolving in digital histopathology with growing application related to nuclear detection and segmentation.  ...  Figure 2 : 2 (a) Original BC histopathology image, (b) binarized lymphocyte nuclei, (c) contour initialization, (d) segmentation after splitting contour.  ... 
doi:10.4172/2155-9538.1000227 fatcat:xxvyqhsghzgplddods4bjlulq4

Fast Segmentation for Texture-based Cartography of whole Slide Images

Grégory Apou, Benoît Naegel, Germain Forestier, Friedrich Feuerhake, Cédric Wemmert
2014 Proceedings of the 9th International Conference on Computer Vision Theory and Applications  
The method is based on an original segmentation algorithm and on a supervised multiclass classification using a textural characterization of the regions computed by the segmentation.  ...  The fast and accurate display of such images for visual analysis by pathologists and the conventional automated analysis remain challenging, mainly due to the image size (sometimes billions of pixels)  ...  framework to describe the contents of a histopathological image, regardless of the cell con- tents of the studied classes.  ... 
doi:10.5220/0004687403090319 dblp:conf/visapp/ApouNFFW14 fatcat:4knrjxs2jzhtbcxlqiwfha3dji

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review [article]

Xiaoqi Li, Haoyuan Chen, Chen Li, Md Mamunur Rahaman, Xintong Li, Jian Wu, Xiaoyan Li, Hongzan Sun, Marcin Grzegorzek
2022 arXiv   pre-print
This paper reviews the applications of image processing technology based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection.  ...  At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide doctors with diagnostic references.  ...  We also thank Miss Zixian Li and Mr. Guoxian Li for their important discussion.  ... 
arXiv:2201.08550v2 fatcat:tzc4oiurzngkrlke4w4zt4f26u

Image Automatic Categorisation using Selected Features Attained from Integrated Non-Subsampled Contourlet with Multiphase Level Sets

Rajyalakshmi Uppada, Koteswara Rao Sanagapallela, Satya Prasad Kodati
2018 Defence Life Science Journal  
Proposed Method-II, an integrated approach of NSC and Multiphase Level Sets is preferred to other segmentation practices as it proves better performance 3.  ...  images for 96 trained images  ...  Ramesh, Asst Professor, Rangaraya Medical College for providing high resolution H&E stained breast cancer histopathological Images.  ... 
doi:10.14429/dlsj.4.11683 fatcat:cmegmixg6rctnbco37joo4ezey

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Fuyong Xing, Lin Yang
2016 IEEE Reviews in Biomedical Engineering  
Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure.  ...  In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.  ...  The top-hat transform used to reduce the effects of color diffusion for cell segmentation is reported in [41] and grayscale reconstruction is applied to cell clump splitting in [58] .  ... 
doi:10.1109/rbme.2016.2515127 pmid:26742143 pmcid:PMC5233461 fatcat:hx5ldvsppvgzxk6rdiok7siyvi

Mitosis Extraction in Breast-Cancer Histopathological Whole Slide Images [chapter]

Vincent Roullier, Olivier Lézoray, Vinh-Thong Ta, Abderrahim Elmoataz
2010 Lecture Notes in Computer Science  
In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images.  ...  At each resolution level, a spatial refinement by semi-supervised clustering is performed to obtain more accurate segmentation around edges.  ...  Our graph-based formulation for image segmentation is presented in Sect. 3 and its integration into a multi-resolution segmentation strategy is detailed in Sect. 4.  ... 
doi:10.1007/978-3-642-17289-2_52 fatcat:kgsynmt2jzcnpetwbnxvmzqnfi

Conceptual data sampling for breast cancer histology image classification

Eman Rezk, Zainab Awan, Fahad Islam, Ali Jaoua, Somaya Al Maadeed, Nan Zhang, Gautam Das, Nasir Rajpoot
2017 Computers in Biology and Medicine  
The results indicate that our method is efficient and generates an illustrative sample of small size.  ...  The proposed sampling technique is applied in classifying the regions of breast cancer histology images as malignant or benign.  ...  Image Segmentation Image segmentation is the process of partitioning an image into a set of pixels based on color, intensity, and texture [16] .  ... 
doi:10.1016/j.compbiomed.2017.07.018 pmid:28783538 fatcat:hqjfbpsjlbgvtomftiqofetblm

A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches [article]

Chen Li, Xintong Li, Md Rahaman, Xiaoyan Li, Hongzan Sun, Hong Zhang, Yong Zhang, Xiaoqi Li, Jian Wu, Yudong Yao, Marcin Grzegorzek
2021 arXiv   pre-print
Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks.  ...  The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists obtain more stable and quantitative analysis results, save labor costs and improve diagnosis  ...  Fig. 29 : 29 Path computation in horizontal and vertical strips, leading to an image partition.  ... 
arXiv:2102.10553v1 fatcat:ve4qkiwfjrb3fg7hal5uvpyxia

Gastric histopathology image segmentation using a hierarchical conditional random field [article]

Changhao Sun, Chen Li, Jinghua Zhang, Muhammad Rahaman, Shiliang Ai, Hao Chen, Frank Kulwa, Yixin Li, Xiaoyan Li, Tao Jiang
2020 arXiv   pre-print
Mainly, Conditional Random Field (CRF), an efficient and stable algorithm for analyzing images containing complicated contents, can characterize spatial relation in images.  ...  in gastric histopathology images obtained by an optical microscope to assist histopathologists in medical work.  ...  Acknowledgments This work is supported by the "National Natural Science Foundation of China" (No. 61806047) and the "Fundamental Research Funds for the Central Universities" (No. N2019003).  ... 
arXiv:2003.01302v4 fatcat:plnf7moofzbivlrwznfwau6wu4

Towards More Reliable Unsupervised Tissue Segmentation Via Integrating Mass Spectrometry Imaging and Hematoxylin-Erosin Stained Histopathological Image [article]

Ang Guo, Zhiyu Chen, Fang Li, Wenbo Li, Qian Luo
2020 bioRxiv   pre-print
Eventually, by integrating the segmentation outcomes based on MSI and H&E image data, the confidence level of the segment assignment was displayed for each pixel, which offered a much more informative  ...  The whole H&E image is split into an array of small patches, which correspond to the spatial pixels of MSI.  ...  TAS features characterize the texture of an image and show the highest AMI score when concatenated with the DCNN features.  ... 
doi:10.1101/2020.07.17.208025 fatcat:52owyestfffl3nhpfgctd762ne

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Yawen Wu, Michael Cheng, Shuo Huang, Zongxiang Pei, Yingli Zuo, Jianxin Liu, Kai Yang, Qi Zhu, Jie Zhang, Honghai Hong, Daoqiang Zhang, Kun Huang (+2 others)
2022 Cancers  
Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis  ...  Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation.  ...  of color and texture.  ... 
doi:10.3390/cancers14051199 pmid:35267505 pmcid:PMC8909166 fatcat:7tfcfh4z45goxbcgf23sncok5a

Cytology Image Analysis Techniques Towards Automation: Systematically Revisited [article]

Shyamali Mitra, Nibaran Das, Soumyajyoti Dey, Sukanta Chakrabarty, Mita Nasipuri, Mrinal Kanti Naskar
2020 arXiv   pre-print
Cytology is the branch of pathology which deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions.  ...  We take a short tour to 17 types of cytology and explore various segmentation and/or classification techniques which evolved during last three decades boosting the concept of automation in cytology.  ...  Authors are also thankful to the members of "Theism Medical Diagnostics Centre", Kolkata, India and "Saroj Gupta Cancer Centre & Research Institute", Thakurpukur, Kolkata, India.  ... 
arXiv:2003.07529v1 fatcat:eossjujftzflbfnfhbsw55tlta

Nuclei Detection Based on Secant Normal Voting with Skipping Ranges in Stained Histopathological Images

XueTing LIM, Kenjiro SUGIMOTO, Sei-ichiro KAMATA
2018 IEICE transactions on information and systems  
However, these methods still encounter the risk of false seeding, especially for the heterogeneous intensity images.  ...  plays a critical role in quantitative cell analysis.  ...  Acknowledgments This work was partially supported by JSPS KAKENHI Grant Number 15K00248 and Waseda University Grant for Special Research Projects (Project number: 2016B-201).  ... 
doi:10.1587/transinf.2017edp7326 fatcat:c4fmanr6qnbdzjuuztn3xb4sbq

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 5662-5676 Variational Bayesian Blind Color Deconvolution of Histopathological Images.  ...  ., +, TIP 2020 5662-5676 Variational Bayesian Blind Color Deconvolution of Histopathological Images.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m
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