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Review of Nuclei Detection, Segmentation in Microscopic Images

Rujuta O, Vyavahare AJ
2017 Journal of Bioengineering and Biomedical Sciences  
Recently some automatic methods have been evolving in digital histopathology with growing application related to nuclear detection and segmentation.  ...  This paper is a review of some recent state-of-art nucleus/cell segmentation approaches on different types of microscopic images.  ...  Conclusion and Future Work Since last some decades, considerable number of articles are published in field of microscopic and histopathology image for nuclei detection and segmentation.  ... 
doi:10.4172/2155-9538.1000227 fatcat:xxvyqhsghzgplddods4bjlulq4

A Systematic Survey on Automatic Classification of Breast Cancer using Histopathology Image

Shwetha G.K
2020 Bioscience Biotechnology Research Communications  
So, automatic classification of breast cancer utilizing image techniques has great application value in the early detection of breast cancer.  ...  Since, extracting non-redundant and informative features from histopathology image is a challenging task, due to heavy noise conditions, and small variant nuclei cell size.  ...  Image denoising: after histopathology image collection, image denoising is employed to improve the visibility level of the collected images for better understanding of nuclei and non-nuclei cells.  ... 
doi:10.21786/bbrc/13.13/54 fatcat:fhcbqtgr4jd7zaqppebrrw24fq

BREAST CANCER GRADING OF H&E STAINED HISTOPATHOLOGY IMAGES

Vijay M. Mane, Nikhil Tagalpallewar
2018 ICTACT Journal on Image and Video Processing  
In this paper a system for automatic detection of breast cancer grading of H&E stained histopathological images is presented.  ...  The segmentation of nuclei in H&E stained image is performed using color thresholding and maximum entropy thresholding. The features are computed according to Bloom Richardson grading criteria.  ...  NUCLEI DETECTION AND SEGMENTATION The automatic detection and segmentation of nuclei is an essential step in the breast cancer grading system.  ... 
doi:10.21917/ijivp.2018.0257 fatcat:ldvfcgi37jbmxecaopd5ibc5oq

Histopathological Image Analysis Using Image Processing Techniques: An Overview

A.D Belsare
2012 Signal & Image Processing An International Journal  
The pathologist examine the tissue structure, distribution of cells in tissue, regularities of cell shapes and determine benign and malignancy in image.  ...  KEYWORDS Image processing, histopathological image analysis, image segmentation, and computer assisted diagnosis.  ...  Also they help in understanding the complete slide preparation procedure for histology image aquicision.  ... 
doi:10.5121/sipij.2012.3403 fatcat:zeqdmbv7vbcptjeyv3uqfgfsru

Automated segmentation technique with self-driven post-processing for histopathological breast cancer images

Chetna Kaushal, Anshu Singla
2020 CAAI Transactions on Intelligence Technology  
Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue.  ...  The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively.  ...  The cell nuclei in histopathological images need to be identified for accurate segmentation of cancerous cells.  ... 
doi:10.1049/trit.2019.0077 fatcat:yafdhjlaorgyfccfjspqsjcroi

Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network

Shanmugham Balasundaram, Revathi Balasundaram, Ganesan Rasuthevar, Christeena Joseph, Annie Grace Vimala, Nanmaran Rajendiran, Baskaran Kaliyamurthy
2021 Journal of ICT Research and Applications  
Based on the segmented and extracted images, classification of benign and malignant breast cancer cells was done to identify tumors.  ...  Automatic detection and classification was carried out by means of the computer analytical tool of deep learning algorithm.  ...  This segmentation preceded automatic detection and extraction of nuclei and lumen regions from H&Estained breast cancer histopathological images.  ... 
doi:10.5614/itbj.ict.res.appl.2021.15.2.3 doaj:1053e191b0214506b04e21e2844cb74d fatcat:vaiz26svenbn5l2q5tupzxh27m

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

RonaldJoe Stanley, Sudhir Sornapudi, WilliamV Stoecker, Haidar Almubarak, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, ShellianeR Frazier
2018 Journal of Pathology Informatics  
Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.  ...  in histopathology images.  ...  In addition, we gratefully acknowledge the medical expertise and collaboration of Dr. Mark Schiffman and Dr.  ... 
doi:10.4103/jpi.jpi_74_17 pmid:29619277 pmcid:PMC5869967 fatcat:z64odumz4fc2rj7zigvx5wgxna

Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review

Xiaoliang Xie, Xulin Wang, Yuebin Liang, Jingya Yang, Yan Wu, Li Li, Xin Sun, Pingping Bing, Binsheng He, Geng Tian, Xiaoli Shi
2021 Frontiers in Oncology  
Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image  ...  Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues.  ...  All authors have approved the final version of the manuscript.  ... 
doi:10.3389/fonc.2021.763527 pmid:34900711 pmcid:PMC8660076 fatcat:g7kyyexwo5evxmrlxg3l3hjqci

Progress of Machine Vision in the Detection of Cancer Cells in Histopathology

Wenbin He, Yongjie Han, Wuyi Ming, Jinguang Du, Yinxia Liu, Yuan Yang, Leijie Wang, Yongqiang Wang, Zhiwen Jiang, Chen Cao, Jie Yuan
2022 IEEE Access  
According to the requirements of medical detection, this review summarizes the applications of machine vision in the detection of cancer cells in histopathological images and analyzes the advantages and  ...  disadvantages of existing methods in image preprocessing, segmentation, feature extraction and recognition.  ...  [34] used a combination of morphology and hysteresis threshold to segment cell nuclei, with an average segmentation accuracy of about 90.24%, but some faintly visible cell nuclei could not be detected  ... 
doi:10.1109/access.2022.3161575 fatcat:uzj3rxfpqjg5xpy2sjdjjk2j5i

Classification of Histopathological Images based on Modified Clump Splitting Approach

Anand Raj, T. N., Nandini Manoli
2018 International Journal of Computer Applications  
Therefore in this research work, a combination of both edge and region-based nuclei segmentation is proposed.  ...  Identifying the number of benign and malignant nuclei in a given area of histopathological tissue is very important for the automated grading system.  ...  MAJOR CHALLENGES IN NUCLEI SEGMENTATION Identifying the number of cells in a given area of the histopathological image is a standard process for the automated grading system.  ... 
doi:10.5120/ijca2018917888 fatcat:odaadodjv5dl7dquu5u5lvgfsm

Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation

Adel Hafiane, Filiz Bunyak, Kannappan Palaniappan
2008 Pattern Recognition (ICPR), Proceedings of the International Conference on  
is robust even in the case of clumped touching nuclei.  ...  Early results show that we can reach a 91% detection rate compared to manual ground truth of cell nuclei centers across a range of prostate cancer grades.  ...  Once the cell nuclei regions are segmented their centers are estimated using the improved iterative voting scheme which also provides an accurate measure of tissue cell count.  ... 
doi:10.1109/icpr.2008.4761744 pmid:35695882 pmcid:PMC9186214 dblp:conf/icpr/HafianeBP08 fatcat:lhhxevziqvdwzef3stixzwtive

Analysis of Nuclei Detection with Stain Normalization in Histopathology Images

K. Sabeena Beevi, G. R. Bindu
2015 Indian Journal of Science and Technology  
This paper proposes a unique detection technique to identify nuclei from microscopy images of breast histopathology slides.  ...  A comparative analysis of different pre-processed images on nuclei detection reveals an accuracy of 97% for Blue Ratio (BR) images.  ...  Acknowledgment The authors acknowledge the help and support rendered for this work by pathologists of KIMS Hospital, Kollam. References  ... 
doi:10.17485/ijst/2015/v8i23/85321 fatcat:ikb3enlchrf4fbchsneeul566u

Mitotic Cell Classification System Based On Supervised Learning for Histopathological Images of Breast Cancer

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Due to the technological advancements in medical science and digital imaging technology, histopathological images are widely utilized for better diagnosis.  ...  As the classification solely depends on the effectiveness of nuclei extraction, the proposed approach employs twin stage segmentation for better nuclei extraction.  ...  This work detects and segments lymphocytes automatically and the work is carried out on HER2+ BC histopathology images.  ... 
doi:10.35940/ijitee.k1552.0981119 fatcat:zyur3g6awbc2bhzg4fxct7xtvy

Automated Nuclei Segmentation of Breast Cancer Histopathology

Vipin Bondre, Amoli Belsare
2013 International journal of computer and communication technology  
Automated detection and segmentation of cell nuclei is an essential step in breast cancer histopathology, so that there is improved accuracy, speed, level of automation and adaptability to new application  ...  The goal of this paper is to develop efficient and accurate algorithms for detecting and segmenting cell nuclei in 2-D histological images.  ...  via manual detection and segmentation of nuclei.  ... 
doi:10.47893/ijcct.2013.1189 fatcat:j6i3sghqjnga3jgriojpwvvheq

Ontology-Driven Image Analysis for Histopathological Images [chapter]

Ahlem Othmani, Carole Meziat, Nicolas Loménie
2010 Lecture Notes in Computer Science  
Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images.  ...  This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibility of the low-level image analysis  ...  In any case, it will take time and money to improve the capacity of automatic annotation of these images.  ... 
doi:10.1007/978-3-642-17289-2_1 fatcat:7l66u6dcovglbamz6idanglxli
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