Learning based ensemble segmentation of anatomical structures in liver ultrasound image
| Autor: | Qiang Wang, Won-Chul Bang, Youngkyoo Hwang, Xiaolu Shen, Xuetao Feng, James D. K. Kim, Jung-Bae Kim, Zhihui Hao, Jiyeun Kim |
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| Rok vydání: | 2013 |
| Předmět: |
Computer science
business.industry Segmentation-based object categorization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Pattern recognition Image segmentation Random forest Level set Robustness (computer science) Graph (abstract data type) Segmentation Computer vision Artificial intelligence business |
| Zdroj: | Medical Imaging: Image Processing |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2006758 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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