9 March 2010 Database-guided breast tumor detection and segmentation in 2D ultrasound images
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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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jingdan Zhang, Jingdan Zhang, Shaohua Kevin Zhou, Shaohua Kevin Zhou, Shelby Brunke, Shelby Brunke, Carol Lowery, Carol Lowery, Dorin Comaniciu, 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); doi: 10.1117/12.844558; https://doi.org/10.1117/12.844558

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