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Proceedings Paper

Ultrasound breast lesion segmentation using adaptive parameters
Author(s): Baek Hwan Cho; Yeong Kyeong Seong; Junghoe Kim; Zhihua Liu; Zhihui Hao; Eun Young Ko; Kyung-Gu Woo
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Paper Abstract

In computer aided diagnosis for ultrasound images, breast lesion segmentation is an important but intractable procedure. Although active contour models with level set energy function have been proposed for breast ul- trasound lesion segmentation, those models usually select and x the weight values for each component of the level set energy function empirically. The xed weights might a ect the segmentation performance since the characteristics and patterns of tissue and tumor di er between patients. Besides, there is observer variability in probe handling and ultrasound machine gain setting. Hence, we propose an active contour model with adaptive parameters in breast ultrasound lesion segmentation to overcome the variability of tissue and tumor patterns between patients. The main idea is to estimate the optimal parameter set automatically for di erent input images. We used regression models using 27 numerical features from the input image and an initial seed box. Our method showed better results in segmentation performance than the original model with xed parameters. In addition, it could facilitate the higher classi cation performance with the segmentation results. In conclusion, the proposed active contour segmentation model with adaptive parameters has the potential to deal with various di erent patterns of tissue and tumor e ectively.

Paper Details

Date Published: 18 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351E (18 March 2014); doi: 10.1117/12.2041893
Show Author Affiliations
Baek Hwan Cho, Samsung Advanced Institute of Technology (Korea, Republic of)
Yeong Kyeong Seong, Samsung Advanced Institute of Technology (Korea, Republic of)
Junghoe Kim, Samsung Advanced Institute of Technology (Korea, Republic of)
Zhihua Liu, Samsung Advanced Institute of Technology (China)
Zhihui Hao, Samsung Advanced Institute of Technology (China)
Eun Young Ko, Samsung Medical Ctr. (Korea, Republic of)
Kyung-Gu Woo, Samsung Advanced Institute of Technology (China)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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