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9 March 2010 Joint segmentation and spiculation detection for ill-defined and spiculated mammographic masses
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A learning-based approach integrating the use of pixel level statistical modeling and spiculation detection is presented for the segmentation of mammographic masses with ill-defined margins and spiculations. The algorithm involves a multi-phase pixel-level classification, using a comprehensive group of regional features, to generate a pixel level mass-conditional probability map (PM). Then, mass candidate along with background clutters are extracted from the PM by integrating the prior knowledge of shape and location of masses. A multi-scale steerable ridge detection algorithm is employed to detect spiculations. Finally, all the object level findings, including mass candidate, detected spiculations, and clutters, along with the PM are integrated by graph cuts to generate the final segmentation mask. The method was tested on 54 masses (51 malignant and 3 benign), all with ill-defined margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced radiologists. Area overlap ratio of 0.766 (±0.144) and 0.642 (±0.173) were obtained for segmenting the whole mass and only the margin portion, respectively. Williams index of area and contour based measurements indicated that segmentation results of the algorithm well agreed with the radiologists' delineation. Most importantly, the proposed approach is capable of including mass margin and its extension which are considered as key features for breast lesion analyses.
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Yimo Tao, Shih-Chung B. Lo, Matthew T. Freedman, and Jianhua Xuan "Joint segmentation and spiculation detection for ill-defined and spiculated mammographic masses", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762407 (9 March 2010);

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