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Active contours driven by edge entropy fitting energy for image segmentation
| Content Provider | Scilit |
|---|---|
| Author | Wang, Lei Chen, Guangqiang Shi, Dai Chang, Yan Chan, Sixian Pu, Jiantao Yang, Xiaodong |
| Copyright Year | 2018 |
| Description | Journal: Signal Processing Active contour models have been widely used for image segmentation purposes. However, they may fail to delineate objects of interest depicted on images with intensity inhomogeneity. To resolve this issue, a novel image feature, termed as local edge entropy, is proposed in this study to reduce the negative impact of inhomogeneity on image segmentation. An active contour model is developed on the basis of this feature, where an edge entropy fitting (EEF) energy is defined with the combination of a redesigned regularization term. Minimizing the energy in a variational level set formulation can successfully drive the motion of an initial contour curve towards optimal object boundaries. Experiments on a number of test images demonstrate that the proposed model has the capability of handling intensity inhomogeneity with reasonable segmentation accuracy. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6615709/pdf |
| Ending Page | 35 |
| Page Count | 9 |
| Starting Page | 27 |
| ISSN | 01651684 |
| DOI | 10.1016/j.sigpro.2018.02.025 |
| Journal | Signal Processing |
| Volume Number | 149 |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2018-08-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Signal Processing Active Contour Models Image Segmentation Intensity Inhomogeneity Local Edge Entropy |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Control and Systems Engineering Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
