A New Local Region-Based Active Contour Model for Image Segmentation Based on Adaptive Double Potential Well Function
| Autor: | Xiaotian Wang, Zhang Liu, Chencheng Huang, Qi Wang, Jiaxi Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Image Analysis and Stereology, Vol 43, Iss 2 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1580-3139 1854-5165 |
| DOI: | 10.5566/ias.3021 |
| Popis: | In this paper, we present a modified local region-based active contour model employing an adaptive double potential well function for image segmentation. Initially, to circumvent the issue of the potential well function's excessive evolutionary pace within the zero potential well, which could lead to rapid level set evolution and inadvertent targeting of segmentation areas, we introduce an adaptive double potential well function. This function dynamically modulates coefficient by increasing the diffusion rate during the initial phase, decreasing it in the later stages, and mitigating it in the vicinity of the zero potential well. Subsequently, we incorporate a length term and a penalty term, both predicated on the adaptive double well function, into the energy functional derived from the local region-based Chan-Vese (LRCV) model. This integration serves to augment the edge smoothness of the curve and the precision of the segmentation process. Experimental outcomes demonstrate that our proposed model significantly augments segmentation accuracy when benchmarked against certain related models. |
| Databáze: | Directory of Open Access Journals |
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