Enhanced Type 2 Triangular Intuitionistic Fuzzy C Means Clustering Algorithm for Breast Cancer Histopathology Images

The theoretical study, focused on contrast specific adaptive histogram equalization (CLAHE) for breast cancer histopathology image enhancement, projected an original form of type 2 triangular intuitionistic fuzzy membership function (TIFM). Digital pathology is one of the most developed fields in medical diagnosis. The detection of cancer nuclei is a very tough process where histopathology images contain hematoxylin and eosin stains. Image processing enhancement algorithms were applied to improve the image quality and diagnostic result of whether cancer nuclei are present or not. CLAHE method was developed based on predefined clip limit value, it is used to decrease the noise and gets a better quality of the histopathology image. The proposed research work based on clips limit value was calculated automatically by using type2 intuitionistic triangular fuzzy membership function. This proposed method has given a better result compared with existing methods of TFMCLAHE and CLAHE. The image quality metric techniques are such as psnr, mse, rmse and entropy. After enhancement, cancer nuclei were segmented through the proposed clustering algorithm name as triangular based spatial type 2 intuitionistic fuzzy c means. This proposed method was measured by ssim and it worked well compared to the existing algorithms such as Fuzzy c means and spatial Intuitionistic fuzzy c means clustering methods.