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Proceedings Paper

Fast microcalcification detection in ultrasound images using image enhancement and threshold adjacency statistics
Author(s): Baek Hwan Cho; Chuho Chang; Jong-Ha Lee; Eun Young Ko; Yeong Kyeong Seong; Kyoung-Gu Woo
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Paper Abstract

The existence of microcalcifications (MCs) is an important marker of malignancy in breast cancer. In spite of the benefits in mass detection for dense breasts, ultrasonography is believed that it might not reliably detect MCs. For computer aided diagnosis systems, however, accurate detection of MCs has the possibility of improving the performance in both Breast Imaging-Reporting and Data System (BI-RADS) lexicon description for calcifications and malignancy classification. We propose a new efficient and effective method for MC detection using image enhancement and threshold adjacency statistics (TAS). The main idea of TAS is to threshold an image and to count the number of white pixels with a given number of adjacent white pixels. Our contribution is to adopt TAS features and apply image enhancement to facilitate MC detection in ultrasound images. We employed fuzzy logic, tophat filter, and texture filter to enhance images for MCs. Using a total of 591 images, the classification accuracy of the proposed method in MC detection showed 82.75%, which is comparable to that of Haralick texture features (81.38%). When combined, the performance was as high as 85.11%. In addition, our method also showed the ability in mass classification when combined with existing features. In conclusion, the proposed method exploiting image enhancement and TAS features has the potential to deal with MC detection in ultrasound images efficiently and extend to the real-time localization and visualization of MCs.

Paper Details

Date Published: 28 February 2013
PDF: 7 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701Q (28 February 2013); doi: 10.1117/12.2007458
Show Author Affiliations
Baek Hwan Cho, Samsung Advanced Institute of Technology (Korea, Republic of)
Chuho Chang, Samsung Advanced Institute of Technology (Korea, Republic of)
Jong-Ha Lee, Keimyung Univ. School of Medicine (Korea, Republic of)
Eun Young Ko, SAMSUNG Medical Ctr. (Korea, Republic of)
Yeong Kyeong Seong, Samsung Advanced Institute of Technology (Korea, Republic of)
Kyoung-Gu Woo, Samsung Advanced Institute of Technology (Korea, Republic of)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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