Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
On active contour models and balloons
CVGIP: Image Understanding
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
The fast construction of extension velocities in level set methods
Journal of Computational Physics
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
A fast level set method for segmentation of low contrast noisy biomedical images
Pattern Recognition Letters
A fast algorithm for level set-like active contours
Pattern Recognition Letters
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
On simulating 3D fluorescent microscope images
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
IEEE Transactions on Image Processing
A fast level set-like algorithm for region-based active contours
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
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Image segmentation, one of the fundamental task of image processing, can be accurately solved using the level set framework. However, the computational time demands of the level set methods make them practically useless, especially for segmentation of large three-dimensional images. Many approximations have been introduced in recent years to speed up the computation of the level set methods. Although these algorithms provide favourable results, most of them were not properly tested against ground truth images. In this paper we present a comparison of three methods: the Sparse-Field method [1], Deng and Tsui's algorithm [2] and Nilsson and Heyden's algorithm [3]. Our main motivation was to compare these methods on 3D image data acquired using fluorescence microscope, but we suppose that presented results are also valid and applicable to other biomedical images like CT scans, MRI or ultrasound images. We focus on a comparison of the method accuracy, speed and ability to detect several objects located close to each other for both 2D and 3D images. Furthermore, since the input data of our experiments are artificially generated, we are able to compare obtained segmentation results with ground truth images.