<title>Mammographic mass classification: initial results</title>

Abstract

Mammography is recognized as an important means to reduce breast cancer mortality. However, its accuracy is limited, both in sensitivity (some cancers are missed) and specificity (many non-cancer cases are referred for invasive procedures). It has been shown that computer classification of expert radiologist's findings can improve the specificity of mammography. Our project is aimed at automatically extracting measurements by computer analysis of digital mammograms, so as to provide automatic inputs for a benign/malignant classifier. We tested classification of radiological findings and found an area under the ROC curve of 0.95, comparable to what has been reported in the literature. Image measurements were taken from manual segmentations of lesions as well as from two different automatic segmentations. We found that segmentation using a fuzzy clustering method with some post-processing gives results comparable to results on manual outlines with a positive predictive value of 73%. The fuzzy clustering strategy has the potential to provide fully automatic classifications comparable to those based on expert radiological findings. This approach may dramatically reduce the false alarm rate currently seen in screening mammography.