Open AccessVisibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement
Author(s) -
Gupta Bhupendra,
Tiwari Mayank,
Singh Lamba Subir
Publication year - 2019
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2018.1006
Subject(s) - artificial intelligence , histogram , contrast (vision) , visibility , segmentation , computer science , computer vision , quantile , contrast enhancement , image segmentation , pattern recognition (psychology) , mammography , image (mathematics) , mathematics , geography , statistics , medicine , cancer , meteorology , breast cancer , magnetic resonance imaging , radiology
In this work, the authors develop a working software‐based approach named ‘linearly quantile separated histogram equalisation‐grey relational analysis’ for mammogram image (MI). This approach improves overall contrast (local and global) of given MI and segments breast‐region with a specific end goal to acquire better visual elucidation, examination, and grouping of mammogram masses to help radiologists in settling on more precise choices. The fundamental commitment of this work is to demonstrate that results of good quality of breast‐region segmentation can be accomplished from basic breast‐region segmentation if the input image has good contrast and a better interpretation of hidden details. They have evaluated the proposed strategy for MIAS‐MIs. Experimental results have shown that the proposed approach works better than state‐of‐the‐art.