A Contrast Enhancement Model for X-Ray Mammograms Using Modified Local s-Curve Transformation Based on Multi-Objective Optimization

Abstract

The biased X-ray field and the subtle difference in X-ray attenuation between normal and abnormal breast tissues prevent the biomedical sensors to generate mammograms with good quality. These are the common imperfections in the acquisition systems (sensor) of Mammography which contribute towards the degradation of mammogram image quality. This paper aims to develop a contrast enhancement model for mammograms so that the human or machine vision can distinguish easily among the variants of breast lesions and predict their severity. The proposed mammogram enhancement model exploits locally the proprieties of the sigmoidal function based on multi-objective Genetic Algorithm for improving the contrast of lesions along with a simultaneous increment in various Image Quality Assessment (IQA) parameters like: $\textit {EME}$ , $\textit {EC}$ , $\textit {AMBEn}$ and $\textit {FSIM}$ . Herein, the multi-objective optimization problem is transformed into a single-objective optimization to find a unique solution for mammogram enhancement. An increase in these IQA metrics is indicative of better contrast, edge content, and conservation of brightness and other diagnostics information. Simulations of the proposed enhancement model are carried out on mammograms from the mini-MIAS database for benchmarking and validation of results.