Name
Abstract | ||
|---|---|---|
This paper presents a novel level set approach for medical image segmentation. The main contribution of this work is to formulate a new speed function for the conventional level set method. This function is developed by incorporating the statistical region information into the fundamental level set model to improve the robustness of the segmentation for medical images. The new method has some advantages over classical level set methods in case of images with weak and fuzzy edges. Series of experiments on different modalities of medical images have been carried out to evaluate the new method. The experimental results indicate the proposed method is effective. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.512 | ICNC |
Keywords | Field | DocType |
fundamental level set model,different modality,medical image,novel level set approach,medical image segmentation,classical level set method,region information,new method,level set combining,new speed function,conventional level set method,biomedical imaging,level set method,pixel,statistical analysis,level set,mathematical model,image segmentation | Scale-space segmentation,Computer science,Medical imaging,Level set,Robustness (computer science),Image segmentation,Artificial intelligence,Computer vision,Pattern recognition,Level set method,Segmentation,Fuzzy logic,Machine learning | Conference |
ISSN | Citations | PageRank |
1095-2020 | 2 | 0.37 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Yang | 1 | 128 | 15.65 |
| Shuying Huang | 2 | 139 | 23.46 |
| Pan Lin | 3 | 79 | 12.62 |
| Nini Rao | 4 | 85 | 11.36 |