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This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination...
This paper compares the performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven an effective technique for restoration, and has recently been extended to classification. The main issue with histology sections classification is inherent heterogeneity, which is a result of technical and biological...
The objective of medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Classification is an important part in retrieval system. This paper proposed a meticulous classification of MR-brain images using support vector machine (SVM). We used both texture and shape feature to express images, and then applied statistical association...
In this paper, the multi-kernel SVM (Support Vector Machine) classification, integrated with a fusion process, is proposed to segment brain tumor from multi-sequence MRI images (T2, PD, FLAIR). The objective is to quantify the evolution of a tumor during a therapeutic treatment. As the procedure develops, a manual learning process about the tumor is carried out just on the first MRI examination. Then...
Functional Magnetic Resonance Imaging (fMRI) gives vast amount of information on the neural activity of the brain. Researchers analyse fMRI data to investigate the functions and structure of the brain. Using machine learning tools that have been widely used in recent years in fMRI area, has enabled to predict the cognitive states of subject which is called ldquobrain readingrdquo also. In this study,...
In this paper we report our experience using different types of wavelets and different SVM kernel functions for classification of Magnetic Resonance Images to identify those showing symptoms of Alzheimer's Disease. We have developed a novel computational framework for extracting discriminative Gabor wavelet features from the images for classification using Support Vector Machines with various kernel...
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