High Contrast Limited Adaptive Histogram Equalization in Digital Mammogram

Abstract

Medical Image processing is a best possible solution for different issues particularly in the medical field. At present, mankind all through the world is influenced with one or different sorts of cancer. It is ubiquitously throughout that the breast cancer is a major cause of death among ladies. Early discoveries and screening of cancer is enormously builds the odds for fruitful treatment. Mammography is generally used test by radiologists for screening and diagnosis of breast cancer. This paper proposed modified ie.  High Contrast Limited Adaptive Histogram Equalization method (HCLAHE). The presented study outcomes were compared with existing techniques CLAHE and generate an image with similarly disseminated brightness intensities above the complete brightness range. New Histogram equalization augments contrast for brightness standards near to histogram maxima, and reduced contrast near minima. Absolute Mean Brightness Error and entropy in evaluation in this study is carried out on the platform of HCLAHE image processing. It has great impact on the feature of decreasing the divergence among input plus output images absolute mean brightness error and it shows higher entropy to extract more possible information lies in variables to reducing visualization problems.