Automated detection method for architectural distortion based on distribution assessment of mammary gland on mammogram

The clustered microcalcifications and mass are the important findings in interpreting breast cancer, architectural distortion on mammograms as well We have developed the detection algorithm for distorted area based on concentration of mammary gland. However, it was found that the extraction accuracy of mammary gland was not good enough in the visual evaluation. The purpose of this study is to suggest the improvement of extraction method of mammary gland in order to achieve higher sensitivity. The mean curvature, and the combination of shape index and curvedness were performed for extracting of mammary gland in our previous methods. In our new method, the dynamicrange compression was added as the pre-processing before extracting mammary gland by mean curvature. The detection rate at initial pick-up stage was improved by this improvement. It was concluded that our detection method would be effective.