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An Improved Region Based Active Contour Model for Medical Image Segmentation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chack, Seema Sharma, Poonam |
| Copyright Year | 2015 |
| Abstract | Level set methods have been widely used in image processing specially in image segmentation. This paper presents a new region based active contour model in a variational level set formulation for segmentation of real world images in the presence of intensity in-homogeneity and noise. In this paper, we derive a local intensity clustering property in the image domain with better distance regularization function. The level set methods sometimes develop irregularities during its evolution state, which may cause numerical complexity and destroy the stability of evolution. This distance regularization function is able to maintain the desired shape of level set function smoothly and eliminates the need of re-initialization of LSF. The local clustering criterion function is defined for image intensities in neighborhood of each point. Now, this local clustering criterion of point is then integrated with respect to the neighborhood of entire points for global clustering criterion of image segmentation. In which bias function is also evaluated to intensity inhomogeneity correction. Implementation of our method shows that, it is more robust to initialization, and more accurate than conventional model. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.sersc.org/journals/IJSIP/vol8_no1/12.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Active contour model Cluster analysis Clustering coefficient Contour line Embedded system Embedding Image processing Image segmentation LSF Loss function Matrix regularization Medical Image Medical Records Systems, Computerized Numerical analysis Smoothing Variational principle biologic segmentation statistical cluster |
| Content Type | Text |
| Resource Type | Article |
