Breast cancer classification by converging tumour region probability density and texture feature-based clustering

This study presents a novel technique for the segmentation and classification of regions in breast tumour images by integrating a convergence-based density model, coupled with texture feature-based clustering. The segmentation process starts with an active contour that estimates probability densities for foreground, background, and tumour regions. A key advantage of the proposed method is its independence from an annotated training set. Thus, it reduces sensitivity to dataset variability by using intensity-driven convergence. To address overlapping structures in mammographic images, an edge-path contour-splitting methodology is employed for accurate boundary separation. Finally, a probabilistic neural network (PNN) is used to classify the tumour regions based on texture features. Both qualitative and quantitative results are presented to demonstrate the effectiveness of the proposed method. The proposed method achieves an accuracy of 92%, with a sensitivity of 95.4% and specificity of 94.2%. Performance evaluation with ROC analysis confirms the robustness and diagnostic reliability of the proposed approach in identifying breast tumours.