Unsupervised tissue segmentation in screening mammograms for automated breast density assessment

This paper describes a computer-assisted algorithm to automatically assess mammographic breast density. The algorithm was applied to 160 cranio-caudal DDSM mammograms (80 Lumisys and 80 Howtek images). The breast region was first segmented from its background using our self-organizing map (SOM) with knowledge-based refinement algorithm (presented previously). A different SOM neural network was subsequently developed to operate within the determined breast region. Multiscale feature vectors from the breast region were used to train the new SOM. The weight vectors of the SOM were then clustered by the K-means method, resulting in a breast region segmented into K different clusters. The prevalence of SOM clusters containing dense tissues was calculated to develop a summary density index. Statistical analysis was applied to optimize the implementation parameters of the summary index. The average summary index was higher in dense breasts than in non-dense breasts. The trend was consistent for both digitizers, though the results were statistically significant for only the Lumisys set. Unsupervised clustering and segmentation of mammograms is a promising approach for automated breast density assessment.