Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.19946 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915466281222144 |
|---|---|
| author | Guzzetta, Gianluca |
| author_facet | Guzzetta, Gianluca |
| contents | In this paper, we present a comprehensive study and analysis of the Chan-Vese algorithm for image segmentation. We employ a discretized scheme derived from the empirical study of the Chan-Vese model's functional energy and its partial differential equation based on its level set function. We provide a proof of the results and an implementation using MATLAB. Leveraging modern computer vision methodologies, we propose a functional segmentation loss based on active contours, utilizing pytorch.nn.ModuleLoss and a level set based on the Chan-Vese algorithm. We compare our results with common computer vision segmentation datasets and evaluate the performance of classical loss functions against our proposed method. All code and materials used are available at https://github.com/gguzzy/chan_vese_functional_loss. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reimagining Image Segmentation using Active Contour: From Chan Vese Algorithm into a Proposal Novel Functional Loss Framework Guzzetta, Gianluca Computer Vision and Pattern Recognition Medical Physics In this paper, we present a comprehensive study and analysis of the Chan-Vese algorithm for image segmentation. We employ a discretized scheme derived from the empirical study of the Chan-Vese model's functional energy and its partial differential equation based on its level set function. We provide a proof of the results and an implementation using MATLAB. Leveraging modern computer vision methodologies, we propose a functional segmentation loss based on active contours, utilizing pytorch.nn.ModuleLoss and a level set based on the Chan-Vese algorithm. We compare our results with common computer vision segmentation datasets and evaluate the performance of classical loss functions against our proposed method. All code and materials used are available at https://github.com/gguzzy/chan_vese_functional_loss. |
| title | Reimagining Image Segmentation using Active Contour: From Chan Vese Algorithm into a Proposal Novel Functional Loss Framework |
| topic | Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2508.19946 |