Tumor Detection in Mammography Images Using Fuzzy C-means and GLCM Texture Features
Moustapha Mohamed Saleck, Abdelmajide Elmoutaouakkil, Mohammed Mouçouf · 2017
The Fuzzy C-means (FCM) is one of the most efficient algorithms used in various studies which aims at segmenting the masses in mammogram images, thus to build a computer aided diagnosis (CAD) system capable of helping the physicians for an early diagnosis of the breast cancer. In this paper, we will introduce a new approach using FCM algorithm, in order to extract the mass from region-of-interested (ROI). The proposed method aims at avoiding problematic of the estimation of the cluster number in FCM by selecting as input data, the set of pixels which are able to provide us the information required to perform the mass segmentation by fixing two clusters only. The Gray Level Occurrence Matrix (GLCM) is used to extract the texture features for getting the optimal threshold, which separate between selected set and the other sets of the pixels that influences on the mass boundary accuracy. The performance of the proposed method is evaluated by specificity, sensitivity and accuracy. The results obtained from experimentations shows a good efficiency at the different measures used, in favor of our method.