Open AccessEnhanced Mammography image for Breast cancer detection using LC-CLAHE technique
Author(s) -
Shada Omer Khanbari,
Adel Sallam Haider
Publication year - 2020
Publication title -
mağallaẗ ğāmi'aẗ 'adan li-l-'ulūm al-ṭabīyyaẗ wa-al-taṭbīqiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2788-9327
pISSN - 1606-8947
DOI - 10.47372/uajnas.2020.n1.a12
Subject(s) - adaptive histogram equalization , wiener filter , mammography , breast cancer , mean squared error , artificial intelligence , histogram , computer science , filter (signal processing) , segmentation , mathematics , pattern recognition (psychology) , contrast (vision) , peak signal to noise ratio , histogram equalization , computer vision , medicine , statistics , cancer , image (mathematics)
Breast cancer is the greatest challenging health complexities that medical science is facing. Most cases can be prevented by early detection and diagnosis which are the best way to cure breast cancer to decrease the mortality rate. The aim of this research is to obtain a method for enhancing the mammography images by using the proposed method which is incorporating the Local Contrast with Contrast Limited Adaptive Histogram Equalization (LC-CLAHE) to improve the appearance and to increase the contrast of the image and then de-noised by 2D wiener filter techniques. To extract the region of interest (tumor), we used region growing technique for the segmentation process. The standard Mammographic Image Analysis Society (MIAS) database images are considered for the evaluation. Efficiency is measured by Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). It is observed that the proposed method with wiener filter gives higher (PSNR) and lower (RMSE), with a significant filter mask [3 3].