AUTOMATED SEEDED LESION SEGMENTATION ON DIGITAL MAMMOGRAMS

Segmenting lesions is a vital step in many computerized mass-detection schemes for digital (or digitized) mammograms, We have developed two novel lesion segmentation techniques-one based on a single feature cal led the radial gradient index (RGI) and one based an simple probabilis tic models to segment mass lesions, or other similar nodular structure s, from surrounding background, In both methods a series of image part itions is created using gray-level information as well as prior knowle dge of the shape of typical mass lesions, With the former method the p artition that maximizes the RGI is selected. In the latter method, pro bability distributions for gray-levels inside and outside the partitio ns are estimated, and subsequently used to determine the probability t hat the image occurred for each given partition, The partition that ma ximizes this probability is selected as the final lesion partition (co ntour), We tested these methods against a conventional region growing algorithm using a database of biopsy-proven, malignant lesions and fou nd that the new lesion segmentation algorithms more closely match radi ologists' outlines of these lesions, At an overlap threshold of 0.30, gray level region growing correctly delineates 62% of the lesions in o ur database while the RGI and probabilistic segmentation algorithms co rrectly segment 92% and 96% of the lesions, respectively.