Cuckoo search-based modified bi-histogram equalisation method to enhance the cancerous tissues in mammography images

Abstract

In this study, novel variants of histogram equalisation (HE) have been proposed by using proper histogram segmentation techniques and then incorporating weighting constraints to each sub histogram independently to maintain the proper contrast. To segment the histogram properly; Otsu method, Kapur's entropy and grey level co-occurrence matrix (GLCM)-based entropy methods have been applied. Optimal weighting constraints have been computed by applying one existing modified cuckoo search (CS) algorithm. All variants are successfully applied to enhance the cancerous tissues of the mammogram images. Fractal dimension (FD), entropy and quality index based on local variance (QILV) have been employed to measure the efficiency of all proposed methods. Experimental results prove the supremacy of the proposed methods over existing methods.