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Proceedings Paper

Database-guided breast tumor detection and segmentation in 2D ultrasound images
Author(s): Jingdan Zhang; Shaohua Kevin Zhou; Shelby Brunke; Carol Lowery; Dorin Comaniciu
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Paper Abstract

Ultrasonography is a valuable technique for diagnosing breast cancer. Computer-aided tumor detection and segmentation in ultrasound images can reduce labor cost and streamline clinic workflows. In this paper, we propose a fully automatic system to detect and segment breast tumors in 2D ultrasound images. Our system, based on database-guided techniques, learns the knowledge of breast tumor appearance exemplified by expert annotations. For tumor detection, we train a classifier to discriminate between tumors and their background. For tumor segmentation, we propose a discriminative graph cut approach, where both the data fidelity and compatibility functions are learned discriminatively. The performance of the proposed algorithms is demonstrated on a large set of 347 images, achieving a mean contour-to-contour error of 3.75 pixels with about 4.33 seconds.

Paper Details

Date Published: 9 March 2010
PDF: 7 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762405 (9 March 2010); doi: 10.1117/12.844558
Show Author Affiliations
Jingdan Zhang, Siemens Corporate Research (United States)
Shaohua Kevin Zhou, Siemens Corporate Research (United States)
Shelby Brunke, Siemens Medical Solutions USA, Inc. (United States)
Carol Lowery, Siemens Medical Solutions USA, Inc. (United States)
Dorin Comaniciu, Siemens Corporate Research (United States)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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