Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement

Abstract

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.

Keywords

• mammography
• image segmentation
• image enhancement
• medical image processing
• mass segmentation
• mammogram image
• local contrast enhancement
• specific end goal
• visual elucidation
• mammogram masses
• basic breast-region segmentation
• software-based approach
• visibility improvement
• linearly quantile separated histogram equalisation
• grey relational analysis
• mamographic image analysis society-MI
• MIAS-MI