Mammographic Mass Segmentation with Online Learned Shape and Appearance Priors

Automatic segmentation of mammographic mass is an important yet challenging task. Despite the great success of shape prior in biomedical image analysis, existing shape modeling methods are not suitable for mass segmentation. The reason is that masses have no specific biological structure and exhibit complex variation in shape, margin, and size. In addition, it is difficult to preserve the local details of mass boundaries, as masses may have spiculated and obscure boundaries. To solve these problems, we propose to learn online shape and appearance priors via image retrieval. In particular, given a query image, its visually similar training masses are first retrieved via Hough voting of local features. Then, query specific shape and appearance priors are calculated from these training masses on the fly. Finally, the query mass is segmented using these priors and graph cuts. The proposed approach is extensively validated on a large dataset constructed on DDSM. Results demonstrate that our online learned priors lead to substantial improvement in mass segmentation accuracy, compared with previous systems.

[1]  L. Liberman,et al.  Breast imaging reporting and data system (BI-RADS). , 2002, Radiologic clinics of North America.

[2]  Ying Wu,et al.  Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Huai Li,et al.  A multiple circular path convolution neural network system for detection of mammographic masses , 2002, IEEE Transactions on Medical Imaging.

[5]  Arnau Oliver,et al.  A review of automatic mass detection and segmentation in mammographic images , 2010, Medical Image Anal..

[6]  Chung-Ming Chen,et al.  Computer-Aided Detection and Diagnosis in Medical Imaging , 2013, Comput. Math. Methods Medicine.

[7]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[9]  Dimitris N. Metaxas,et al.  Computer-Aided Diagnosis of Mammographic Masses Using Scalable Image Retrieval , 2015, IEEE Transactions on Biomedical Engineering.

[10]  Ghassan Hamarneh,et al.  Mammography Segmentation with Maximum Likelihood Active Contours , 2022 .

[11]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[12]  Yan Zhou,et al.  Shape Prior Modeling Using Sparse Representation and Online Dictionary Learning , 2012, MICCAI.

[13]  Wiro J. Niessen,et al.  Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts , 2008, NeuroImage.

[14]  Umi Kalthum Ngah,et al.  Computer Aided Detection of Breast Density and Mass, and Visualization of Other Breast Anatomical Regions on Mammograms Using Graph Cuts , 2013, Comput. Math. Methods Medicine.