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Proceedings Paper

Automatic tumor detection in the constrained region for ultrasound breast CAD
Author(s): Yeong Kyeong Seong; Moon Ho Park; Eun Young Ko; Kyoung-Gu Woo
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Paper Abstract

In this paper we propose a new method to segment a breast image into several regions. Tumor detection region is constrained to the region only in glandular tissue because the tumors usually occur at glandular tissue in the breast anatomy. We extract texture feature for each point and classify them as several layers using a random forest classifier. Classified points are merged into a large region and small regions are removed by postprocessing. The accuracy of glandular tissue detection rate was about 90%. We applied the conventional tumor detection method in this segmented glandular tissue. After several tests we obtained that tumor detection accuracy improved for 14% and detection time was also reduced. With this method, we can achieve the improvement both on tumor detection accuracy and on the processing time.

Paper Details

Date Published: 23 February 2012
PDF: 6 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831529 (23 February 2012); doi: 10.1117/12.911695
Show Author Affiliations
Yeong Kyeong Seong, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Moon Ho Park, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Eun Young Ko, SAMSUNG Medical Ctr., Sungkyunkwan Univ. School of Medicine (Korea, Republic of)
Kyoung-Gu Woo, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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