Name
Title | ||
|---|---|---|
ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques. |
Abstract | ||
|---|---|---|
In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/JBHI.2013.2242480 | IEEE J. Biomedical and Health Informatics |
Keywords | Field | DocType |
autocontext model (acm),computerised tomography,region-based image labeling,image fusion,acm-based automatic liver segmentation algorithm,learning (artificial intelligence),liver segmentation,image segmentation,computed tomography,autocontext model,multiple atlases,image classification,acm-based classifier sequence,multiclassifier fusion technique,image sequences,pixel-based image labeling,fuzzy integral,mean shift,liver,multiclassifier fusion,learning-based method,3d ct image segmentation,mean-shift algorithm,medical image processing,feature extraction,learning artificial intelligence,mean shift algorithm,shape | Computer vision,Scale-space segmentation,Pattern recognition,Image texture,Computer science,Segmentation,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Connected-component labeling,Minimum spanning tree-based segmentation | Journal |
Volume | Issue | ISSN |
17 | 3 | 2168-2208 |
Citations | PageRank | References |
7 | 0.46 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong-Wei Ji | 1 | 32 | 3.03 |
| Jiangping He | 2 | 23 | 2.44 |
| Xin Yang | 3 | 153 | 18.24 |
| Rudi Deklerck | 4 | 131 | 12.63 |
| Jan Cornelis | 5 | 711 | 72.52 |