Classification Of Breast Tumors As Benign And Malignant Using Textural Feature Descriptor

In this paper we have presented an automated diagnosis of breast cell cancer using histopathological images on the basis of different textural descriptors. In the proposed technique, the images being preprocessed using extended adaptive-top-bottom transform (E-AHET-Bhat) and segmented the nuclei regions from the nonnuclei regions using region growing segmentation. The nuclei regions are then used to extract features and provides texture descriptors using parameter free version of threshold adjacency statistics (PFTAS). The feature vector obtained are then classified as benign tumor feature and malignant tumor features using Rotation Forest (RF) classifier. The proposed technique compared with the other four combination of conventional texture techniques and classifiers. The experimental results and performance metrics values shows that the proposed technique is better than the other conventional techniques.