International Journal of Computer Vision
An Adaptive Level Set Method for Medical Image Segmentation
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Medical Image Segmentation Using New Hybrid Level-Set Method
MEDIVIS '08 Proceedings of the 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
Level-set segmentation of brain tumors using a threshold-based speed function
Image and Vision Computing
3D brain tumor segmentation using fuzzy classification and deformable models
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
A topology preserving level set method for geometric deformable models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
IEEE Transactions on Image Processing
Computers in Biology and Medicine
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In this paper, we present a new segmentation method using the level set framework for medical volume images. The method was implemented using the surface evolution principle based on the geometric deformable model and the level set theory. And, the speed function in the level set approach consists of a hybrid combination of three integral measures derived from the calculus of variation principle. The terms are defined as robust alignment, active region, and smoothing. These terms can help to obtain the precise surface of the target object and prevent the boundary leakage problem. The proposed method has been tested on synthetic and various medical volume images with normal tissue and tumor regions in order to evaluate its performance on visual and quantitative data. The quantitative validation of the proposed segmentation is shown with higher Jaccard's measure score (72.52%-94.17%) and lower Hausdorff distance (1.2654mm-3.1527mm) than the other methods such as mean speed (67.67%-93.36% and 1.3361mm-3.4463mm), mean-variance speed (63.44%-94.72% and 1.3361mm-3.4616mm), and edge-based speed (0.76%-42.44% and 3.8010mm-6.5389mm). The experimental results confirm that the effectiveness and performance of our method is excellent compared with traditional approaches.