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Proceedings Paper

Robust vessel detection and segmentation in ultrasound images by a data-driven approach
Author(s): Ping Guo; Qiang Wang; Xiaotao Wang; Zhihui Hao; Kuanhong Xu; Haibing Ren; Jung Bae Kim; Youngkyoo Hwang
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Paper Abstract

This paper presents a learning based vessel detection and segmentation method in real-patient ultrasound (US) liver images. We aim at detecting multiple shaped vessels robustly and automatically, including vessels with weak and ambiguous boundaries. Firstly, vessel candidate regions are detected by a data-driven approach. Multi-channel vessel enhancement maps with complement performances are generated and aggregated under a Conditional Random Field (CRF) framework. Vessel candidates are obtained by thresholding the saliency map. Secondly, regional features are extracted and the probability of each region being a vessel is modeled by random forest regression. Finally, a fast levelset method is developed to refine vessel boundaries. Experiments have been carried out on an US liver dataset with 98 patients. The dataset contains both normal and abnormal liver images. The proposed method in this paper is compared with a traditional Hessian based method, and the average precision is promoted by 56 percents and 7.8 percents for vessel detection and classification, respectively. This improvement shows that our method is more robust to noise, therefore has a better performance than the Hessian based method for the detection of vessels with weak and ambiguous boundaries.

Paper Details

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903435 (21 March 2014);
Show Author Affiliations
Ping Guo, Samsung Advanced Institute of Technology (China)
Qiang Wang, Samsung Advanced Institute of Technology (China)
Xiaotao Wang, Samsung Advanced Institute of Technology (China)
Zhihui Hao, Samsung Advanced Institute of Technology (China)
Kuanhong Xu, Samsung Advanced Institute of Technology (China)
Haibing Ren, Samsung Advanced Institute of Technology (China)
Jung Bae Kim, Samsung Advanced Institute of Technology (Korea, Republic of)
Youngkyoo Hwang, Samsung Advanced Institute of Technology (Korea, Republic of)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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