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

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

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 i...

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Bibliographic Details
Main Authors: Chetna Kaushal, Anshu Singla
Format: Article
Language:English
Published: Wiley 2020-11-01
Series:CAAI Transactions on Intelligence Technology
Subjects:
cancer
medical image processing
feature extraction
image segmentation
image enhancement
convolutional neural nets
automated segmentation technique
self-driven post-processing operations
cancerous cell detection
post-processing method
weighted area coefficient parameters
spatial neutrosophic distance regularised level set
histopathological breast cancer images
breast tissue
window area size
time steps
magnification factor
region of interest extraction
fuzzy c-means
spatial fuzzy c-means
convolutional neural network-based pangnet
Online Access:https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0077

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https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0077

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