Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis

As well as mass and microcalcification, architectural distortion is a very important finding for the early detection of breast cancer via mammograms, and such distortions can be classified into three typical types: spiculation, retraction, and distortion. The purpose of this work is to develop an automatic method for detecting areas of architectural distortion with spiculation. The suspect areas are detected by concentration indexes of line-structures extracted by using mean curvature. After that, discrimination analysis of nine features is employed for the classifications of true and false positives. The employed features are the size, the mean pixel value, the mean concentration index, the mean isotropic index, the contrast, and four other features based on the power spectrum. As a result of this work, the accuracy of the classification was 76% and the sensitivity was 80% with 0.9 false positives per image in our database in regard to spiculation. It was concluded that our method was effective in detectiong the area of architectural distortion; however, some architectural distortions were not detected accurately because of the size, the density, or the different appearance of the distorted areas.