Multi-spectral images have the advantage of providing complementary information to resolve some ambiguities. But, the challenge is how to make use of the multi-spectral images effectively. In this thesis, our study focuses on the fusion of multi-spectral images by extracting the most useful features to obtain the best segmentation with the least cost in time. The Support Vector Machine (SVM) classification integrated with a selection of the features in a kernel space is proposed. The selection criterion is defined by the kernel class separability. Based on this SVM classification, a framework to follow up brain tumor evolution is proposed, which consists of the following steps: to learn the brain tumors and select the features from the first magnetic resonance imaging (MRI) examination of the patients; to automatically segment the tumor in new data using a multi-kernel SVM based classification; to refine the tumor contour by a region growing technique; and to possibly carry out an adaptive training. The proposed system was tested on 13 patients with 24 examinations, including 72 MRI sequences and 1728 images. Compared with the manual traces of the doctors as the ground truth, the average classification accuracy reaches 98.9%. The system utilizes several novel feature selection methods to test the integration of feature selection and SVM classifiers. Also compared with the traditional SVM, Fuzzy C-means, the neural network and an improved level set method, the segmentation results and quantitative data analysis demonstrate the effectiveness of our proposed system.

Les images multi-spectrales présentent l’avantage de fournir des informations complémentaires permettant de lever des ambigüités. Le défi est cependant comment exploiter ces informations multi-spectrales efficacement. Dans cette thèse, nous nous focalisons sur la fusion des images multi-spectrales en extrayant des attributs les plus pertinents en vue d’obtenir la meilleure segmentation possible avec le moindre coût de calcul possible. La classification par le Support Vector Machine (SVM), combinée avec une méthode de sélection d’attributs, est proposée. Le critère de sélection est défini par la séparabilité des noyaux de classe. S’appuyant sur cette classification SVM, un cadre pour suivre l’évolution est proposé. Il comprend les étapes suivantes : apprentissage des tumeurs cérébrales et sélection des attributs à partir du premier examen IRM (imagerie par résonance magnétique) ; segmentation automatique des tumeurs dans de nouvelles images en utilisant une classification basée sur le SVM multi-noyaux ; affiner le contour des tumeurs par une technique de croissance de région ; effectuer un éventuel apprentissage adaptatif. L’approche proposée a été évaluée sur 13 patients avec 24 examens, y compris 72 séquences IRM et 1728 images. En comparant avec le SVM traditionnel, Fuzzy C-means, le réseau de neurones, et une méthode d’ensemble de niveaux, les résultats de segmentation et l’analyse quantitative de ces résultats démontrent l’efficacité de l’approche proposée. / Multi-spectral images have the advantage of providing complementary information to resolve some ambiguities. But, the challenge is how to make use of the multi-spectral images effectively. In this thesis, our study focuses on the fusion of multi-spectral images by extracting the most useful features to obtain the best segmentation with the least cost in time. The Support Vector Machine (SVM) classification integrated with a selection of the features in a kernel space is proposed. The selection criterion is defined by the kernel class separability. Based on this SVM classification, a framework to follow up brain tumor evolution is proposed, which consists of the following steps: to learn the brain tumors and select the features from the first magnetic resonance imaging (MRI) examination of the patients; to automatically segment the tumor in new data using a multi-kernel SVM based classification; to refine the tumor contour by a region growing technique; and to possibly carry out an adaptive training. The proposed system was tested on 13 patients with 24 examinations, including 72 MRI sequences and 1728 images. Compared with the manual traces of the doctors as the ground truth, the average classification accuracy reaches 98.9%. The system utilizes several novel feature selection methods to test the integration of feature selection and SVM classifiers. Also compared with the traditional SVM, Fuzzy C-means, the neural network and an improved level set method, the segmentation results and quantitative data analysis demonstrate the effectiveness of our proposed system.