Neural Network based Automatic Detection of Lesion Neural Network based Automatic Detection of Lesion Neural Network based Automatic Detection of Lesion Neural Network based Automatic Detection of Lesion Diagnosis in Mammogram Using Image Fusion Diagnosis in Mammogram Using Image Fusion Diagnosis in Mammogram Using Image Fusion Diagnosis in Mammogram Using Image Fusion
Disha Rajeshkumar, A. Sivasankar, Madurai Chennai · 2013
The success of treatment of breast cancer patients depends on the early detection of breast cancer. Mammographic screening has been shown to be effective in reducing mortality rates by 30%–70%. The Architectural distortion is the most common mammographic sign of Non-palpable breast cancer. Architectural distortion could appear at the initial stages of the formation of a breast tumor and may closely resemble the appearance of normal breast tissue overlapped in the projected mammographic image. Due to its subtle appearance and variability in presentation, architectural distortion is the most commonly missed abnormality in false-negative cases . It accounts for 12%–45% of breast cancer cases overlooked or misinterpreted in screening mammography. In this paper, we present CAD methods for the detection of sites of architectural distortion in prior mammograms of interval-cancer cases. The methods are based upon Gabor filters, phase portrait analysis, a novel method for the analysis of the angular spread of power, fractal analysis, Laws’ texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick’s texture features .