13 March 2013 Learning based ensemble segmentation of anatomical structures in liver ultrasound image
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866947 (2013) https://doi.org/10.1117/12.2006758
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Automatic segmentation of anatomical structure is crucial for computer aided diagnosis and image guided online treatment. In this paper, we present a novel approach for fully automatic segmentation of all anatomical structures from a target liver organ in a coherent framework. Firstly, all regional anatomical structures such as vessel, tumor, diaphragm and liver parenchyma are detected simultaneously using random forest classifiers. They share the same feature set and classification procedure. Secondly, an efficient region segmentation algorithm is used to obtain the precise shape of these regional structures. It is based on level set with proposed active set evolution and multiple features handling which achieves 10 times speedup over existing algorithms. Thirdly, the liver boundary curve is extracted via a graph-based model. The segmentation results of regional structures are incorporated into the graph as constraints to improve the robustness and accuracy. Experiment is carried out on an ultrasound image dataset with 942 images captured with liver motion and deformation from a number of different views. Quantitative results demonstrate the efficiency and effectiveness of the proposed algorithm.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xuetao Feng, Xuetao Feng, Xiaolu Shen, Xiaolu Shen, Qiang Wang, Qiang Wang, Jung-Bae Kim, Jung-Bae Kim, Zhihui Hao, Zhihui Hao, Youngkyoo Hwang, Youngkyoo Hwang, Won-Chul Bang, Won-Chul Bang, James D. K. Kim, James D. K. Kim, Jiyeun Kim, Jiyeun Kim, } "Learning based ensemble segmentation of anatomical structures in liver ultrasound image", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866947 (13 March 2013); doi: 10.1117/12.2006758; https://doi.org/10.1117/12.2006758
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