A robust region-based active contour model with point classification for ultrasound breast lesion segmentation

Zhihua Liu; Lidan Zhang; Haibing Ren; Ji-Yeun Kim

doi:10.1117/12.2006164

28 February 2013 A robust region-based active contour model with point classification for ultrasound breast lesion segmentation

Zhihua Liu, Lidan Zhang, Haibing Ren, Ji-Yeun Kim

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86701P (2013) https://doi.org/10.1117/12.2006164
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Lesion segmentation is one of the key technologies for computer-aided diagnosis (CAD) system. In this paper, we propose a robust region-based active contour model (ACM) with point classification to segment high-variant breast lesion in ultrasound images. First, a local signed pressure force (LSPF) function is proposed to classify the contour points into two classes: local low contrast class and local high contrast class. Secondly, we build a sub-model for each class. For low contrast class, the sub-model is built by combining global energy with local energy model to find a global optimal solution. For high contrast class, the sub-model is just the local energy model for its good level set initialization. Our final energy model is built by adding the two sub-models. Finally, the model is minimized and evolves the level set contour to get the segmentation result. We compare our method with other state-of-art methods on a very large ultrasound database and the result shows that our method can achieve better performance.

Citation Download Citation

Zhihua Liu, Lidan Zhang, Haibing Ren, and Ji-Yeun Kim "A robust region-based active contour model with point classification for ultrasound breast lesion segmentation", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701P (28 February 2013); https://doi.org/10.1117/12.2006164

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