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Computer Aided Segmentation and Classification of Mass in Mammographic Images using ANFIS

Author(s): K. Yuvaraj, U.S. Ragupathy

Background: Breast cancer is one of the leading cancers in woman worldwide both in developed and developing nations as per the records from World Health Organization. Many studies have shown that mammography is very effective tool for the breast cancer diagnosis. Mass segmentation plays an important step for the cancer detection.

Objective: The objective of the proposed method is to segment the mass and to classify the mass with high accuracy.

Methods: The segmentation includes two main steps. First, a rough initial segmentation through iterative thresholding, and second, an active contour based segmentation. The relevant statistical features are extracted and the classification is done by using Adaptive Neuro Fuzzy Inference System (ANFIS).

Results: The proposed mass detection scheme achieves sensitivity of 87.5% and specificity of 100% for a set of twenty two images. The overall segmentation accuracy obtained is 91.30%.

Conclusions: This work appears to be of high clinical significance since the mass detection plays an important role in diagnosis of breast cancer.


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