Relative Entropy Pre-Fitting Model for Noisy and Intensity Inhomogeneous Image Segmentation
| Autor: | Hussain Nyeem, Chowdhury M Abid Rahman |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Active contour model
Kullback–Leibler divergence business.industry Computer science 05 social sciences ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 050209 industrial relations Initialization Pattern recognition Image segmentation Robustness (computer science) Computer Science::Computer Vision and Pattern Recognition 0502 economics and business Segmentation Artificial intelligence business 050203 business & management |
| Zdroj: | 2019 IEEE International Conference on Telecommunications and Photonics (ICTP). |
| DOI: | 10.1109/ictp48844.2019.9041775 |
| Popis: | This paper reports an improved Active Contour Model (ACM) for image segmentation. Despite the significant development of the ACMs, their performances for the noisy and intensity-inhomogeneous images are still deficient. To tackle the intensity-inhomogeneity and noises in segmentation, new construction of local pre-fitting for evolving active contours is proposed. Two locally pre-fitted images are constructed from the local intensity estimation, and the relative entropy measures are used to define the local energy functional that provides statistical information of these images with the original image. Thereby, the desired contour evolution of the proposed model is expedited, and its noise-immunity is increased. The proposed model has demonstrated more initialization robustness, faster contour evolution and higher segmentation accuracy over the prominent ACMs for all the test images both with and without noise and intensity-inhomogeneity. |
| Databáze: | OpenAIRE |
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