Digital Mammogram Spiculated Mass Detection and Spicule Segmentation using Level Sets

This letter presents an automated mammographic computer aided diagnosis (CAD) system to detect and segment spicules in digital mammograms, termed spiculation segmentation with level sets (SSLS). SSLS begins with a segmentation of the suspicious mass periphery, which is created using a previously developed adaptive level set segmentation algorithm (ALSSM) by the authors. The mammogram is then analyzed using features derived from the Dixon and Taylor line operator (DTLO), which is a method of linear structure enhancement. Features are extracted, optimized, and then the suspicious mass is classified as benign or malignant. To assess the system efficacy, 60 difficult mammographic images from the digital database of screening mammography (DDSM), containing 30 benign non-spiculated cases, 17 malignant spiculated cases, and 13 malignant non-spiculated cases, are analyzed. The initial spiculation detection method found 100% of the spiculated lesions with no false positive detections, and has area under the receiver operating characteristics (ROC) curve Az=1.0. The values using ALSSM (periphery segmentation only) are A_z=0.9687 and 0.9708 for two investigated feature sets, and increases to A_z=0.9862 using SSLS (spiculation segmentation). The best classification results are 93% overall accuracy (OA), with three false positives (FP) and one false negative (FN) using a 1-NN (nearest neighbor) or 2-NN classifier, and 92% OA with three FP and two FN using a maximum likelihood classifier.