Image enhancement and edge-based mass segmentation in mammogram

Yu Zhang; Noriko Tomuro; Jacob Furst; Daniela Stan Raicu

doi:10.1117/12.844492

12 March 2010 Image enhancement and edge-based mass segmentation in mammogram

Yu Zhang, Noriko Tomuro, Jacob Furst, Daniela Stan Raicu

Author Affiliations +

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76234P (2010) https://doi.org/10.1117/12.844492
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

This paper presents a novel, edge-based segmentation method for identifying the mass contour (boundary) for a suspicious mass region (Region of Interest (ROI)) in a mammogram. The method first applies a contrast stretching function to adjust the image contrast, then uses a filtering function to reduce image noise. Next, for each pixel in a ROI, the energy descriptor (one of the Haralick descriptors) is computed from the co-occurrence matrix of the pixel; and the energy texture image of a ROI is obtained. From the energy texture image, the edges in the image are detected; and the mass region is identified from the closed-path edges. Finally, the boundary of the identified mass region is used as the contour of the segmented mass. We applied our method to ROI-marked mammogram images from the Digital Database for Screening Mammography (DDSM). Preliminary results show that the contours detected by our method outline the shape and boundary of a mass much more closely than the ROI markings made by radiologists.

Citation Download Citation

Yu Zhang, Noriko Tomuro, Jacob Furst, and Daniela Stan Raicu "Image enhancement and edge-based mass segmentation in mammogram", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76234P (12 March 2010); https://doi.org/10.1117/12.844492

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