Name
Title | ||
|---|---|---|
A fuzzy variational model for segmentation of images having intensity inhomogeneity and slight texture |
Abstract | ||
|---|---|---|
Segmentation of real images having unwanted outliers, inhomogeneity or complex background is always very challenging for active contour models. In this paper, we propose a novel model for segmentation of such type of images. The proposed model is based on fuzzy energy functional, which uses coefficient of variation as a region statistics. The proposed model is convex due to introduction of fuzzy membership functions in the energy functional and hence converges to the absolute minima and avoids local minima. Convexity of the proposed model is proved, and hence, the model is independent of initial placement of the contour. Experimental results of the proposed model are compared with other state-of-the-art existing models both qualitatively and quantitatively. For quantitative comparison, we have used Jaccard similarity index and computational complexity. The proposed model is tested on various data sets containing noisy images, images having intensity inhomogeneity and slight texture. In all experimental results, performance of the proposed model can be seen in the experimental section. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00500-020-04878-9 | SOFT COMPUTING |
Keywords | DocType | Volume |
Image segmentation,Fuzzy sets,Pseudo-level set,Kernel metric | Journal | 24.0 |
Issue | ISSN | Citations |
20.0 | 1432-7643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Ahmad | 1 | 0 | 0.34 |
| Noor Badshah | 2 | 37 | 3.98 |
| Haider Ali | 3 | 84 | 15.04 |