Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions
| Autor: | Jing-Hao Xue, Jerome Lapuyade-Lahorgue, Su Ruan |
|---|---|
| Přispěvatelé: | Equipe Quantification en Imagerie Fonctionnelle (QuantIF-LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU) |
| Rok vydání: | 2017 |
| Předmět: |
Iterative method
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation 02 engineering and technology 030218 nuclear medicine & medical imaging Copula (probability theory) 03 medical and health sciences symbols.namesake 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Computer vision Segmentation Hidden Markov model Mathematics Segmentation-based object categorization business.industry Pattern recognition Image segmentation Computer Graphics and Computer-Aided Design [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Computer Science::Computer Vision and Pattern Recognition symbols 020201 artificial intelligence & image processing Artificial intelligence business Software Gibbs sampling |
| Zdroj: | IEEE Transactions on Image Processing IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2017, 26 (7), pp.3187-3195. ⟨10.1109/TIP.2017.2685345⟩ |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2017.2685345 |
| Popis: | International audience; Nowadays, multi-source image acquisition attracts an increasing interest in many fields, such as multi-modal medical image segmentation. Such acquisition aims at considering complementary information to perform image segmentation, since the same scene has been observed by various types of images. However, strong dependence often exists between multi-source images. This dependence should be taken into account when we try to extract joint information for precisely making a decision. In order to statistically model this dependence between multiple sources, we propose a novel multi-source fusion method based on the Gaussian copula. The proposed fusion model is integrated in a statistical framework with the hidden Markov field inference in order to delineate a target volume from multi-source images. Estimation of parameters of the models and segmentation of the images are jointly performed by an iterative algorithm based on Gibbs sampling. Experiments are performed on multi-sequence MRI to segment tumors. The results show that the proposed method based on the Gaussian copula is effective to accomplish multi-source image segmentation. |
| Databáze: | OpenAIRE |
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