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

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CAAI Transactions on Intelligence Technology

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Affective computing study of attention recognition for the 3D guide system

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

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

Content Provider	IET Digital Library
Author	Kaushal, Chetna Singla, Anshu
Abstract	Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue. Recent reviews state high accuracy to segment image, but depends on user input, say window area size, time steps, level set, magnification factor and so on. To extract the region of interest effectively, the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say, area opening, fill holes and selects most appropriate enhanced image required for further analysis. The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively. The post-processing method itself determines the value of different parameters for different operations based on segmented results obtained. The proposed technique has the following features: (i) technique is context sensitive; (ii) no prior setting of time step, weighted area coefficient parameters is required; (iii) magnification independent; (iv) post-processing operations are self-driven which enhance segmentation results adaptively. The experimental results are compared with four state-of-the-art techniques: fuzzy C-means, spatial fuzzy C-means, spatial neutrosophic distance regularised level set and convolutional neural network-based PangNet. Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively.
Starting Page	294
Ending Page	300
Page Count	7
Volume Number	5
e-ISSN	24682322
Issue Number	Issue 4, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/trit/5/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0077
Journal	CAAI Transactions on Intelligence Technology
Publisher	The Institution of Engineering and Technology Chongqing University of Technology Chinese Association for Artificial Intelligence
Publisher Date	2020-10-15
Access Restriction	Open
Rights License	Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Subject Keyword	Automated Segmentation Technique Biology And Medical Computing Biomedical Measurement And Imaging Breast Tissue Cancer Cancerous Cell Detection Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network-based PangNet Feature Extraction Fuzzy C-means Health Physics Histopathological Breast Cancer Image Image Enhancement Image Recognition Image Segmentation Magnification Factor Medical And Biomedical Uses of Field Medical Image Processing Neural Computing Technique Patient Diagnostic Method And Instrumentation Post-processing Method Radiations Radioactivity Region of Interest Extraction Self-driven Post-processing Operation Spatial Fuzzy C-means Spatial Neutrosophic Distance Regularised Level Set Time Steps Weighted Area Coefficient Parameter Window Area Size
Content Type	Text
Resource Type	Article

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