Database-guided breast tumor detection and segmentation in 2D ultrasound images

Jingdan Zhang; Shaohua Kevin Zhou; Shelby Brunke; Carol Lowery; Dorin Comaniciu

doi:10.1117/12.844558

9 March 2010 Database-guided breast tumor detection and segmentation in 2D ultrasound images

Jingdan Zhang, Shaohua Kevin Zhou, Shelby Brunke, Carol Lowery, Dorin Comaniciu

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 762405 (2010) https://doi.org/10.1117/12.844558
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

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.

Citation Download Citation

Jingdan Zhang, Shaohua Kevin Zhou, Shelby Brunke, Carol Lowery, and Dorin Comaniciu "Database-guided breast tumor detection and segmentation in 2D ultrasound images", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762405 (9 March 2010); https://doi.org/10.1117/12.844558

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