Name
Title | ||
|---|---|---|
Learning based ensemble segmentation of anatomical structures in liver ultrasound image |
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 |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2006758 | Proceedings of SPIE |
Keywords | Field | DocType |
detection,segmentation,random forest,level set,shortest path | Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Computer-aided diagnosis,Level set,Segmentation-based object categorization,Robustness (computer science),Image segmentation,Artificial intelligence,Random forest | Conference |
Volume | Issue | ISSN |
8669 | null | 0277-786X |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuetao Feng | 1 | 26 | 4.80 |
| xiaolu shen | 2 | 5 | 1.47 |
| Qiang Wang | 3 | 16 | 4.39 |
| Jung-bae Kim | 4 | 38 | 8.36 |
| Zhihui Hao | 5 | 39 | 4.30 |
| Youngkyoo Hwang | 6 | 21 | 4.48 |
| Won-Chul Bang | 7 | 106 | 14.05 |
| Jay Kim | 8 | 3 | 0.39 |
| Jiyeun Kim | 9 | 23 | 3.66 |