A Novel Approach for Removal of Pectoral Muscles in Digital Mammogram

Abstract

Removal of noise and pectoral muscles are the two important pre-processing steps in CAD system for the diagnosis of breast cancer. This work combines Robust Outlyingness Ratio (ROR) mechanism with extended NL-Means (ROR-NLM) filter based on Discrete Cosine Transform (DCT) for the detection and removal of noise. This method removes Gaussian and impulse noise very effectively without any loss of desired data. For segmenting and removing pectoral muscles, this paper uses global thresholding to identify pectoral muscles, edge detection processes to identify the edge of the full breast and connected component labelling to identify and remove the connected pixels outside the breast region. The result shows that our approach removes Gaussian and impulse noise effectively without any loss of desired data and overall gives 90.06% accuracy.

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