Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution
Autor: | Arturo Hernández-Aguirre, Juan Manuel Lopez-Hernandez, Margarita García-Hernández, H. J. Estrada-Garcia, Juan Gabriel Avina-Cervantes, Miguel Torres-Cisneros, Ivan Cruz-Aceves |
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Jazyk: | angličtina |
Rok vydání: | 2013 |
Předmět: |
Active contour model
Article Subject business.industry Segmentation-based object categorization General Mathematics lcsh:Mathematics General Engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Pattern recognition Image segmentation lcsh:QA1-939 Stability (probability) Estimation of distribution algorithm lcsh:TA1-2040 Computer Science::Computer Vision and Pattern Recognition Computer vision Segmentation Artificial intelligence Range segmentation business lcsh:Engineering (General). Civil engineering (General) Mathematics |
Zdroj: | Mathematical Problems in Engineering, Vol 2013 (2013) |
ISSN: | 1563-5147 |
Popis: | This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability. |
Databáze: | OpenAIRE |
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