Mammographic mass detection by adaptive thresholding and region growing

We present an efficient method to detect mass lesions on digitized mammograms, which consists of breast region extraction, region partitioning, automatic seed selection, segmentation by region growing, feature extraction, and neural network classification. The method partitions the breast region into a fat region, a fatty and glandular region, and a dense region, so that different threshold values can be applied to each partitioned region during processes of the seed selection and segmentation. The mammographic masses are classified by using four features representing shape, density, and margin of the segmented regions. The method detects subtle mass lesions with various contrast ranges and can facilitate a procedure of mass detection in computer-aided diagnosis systems. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 340–346, 2000