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Currently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions, particularly glioma. Glioma is one of the most common tumors, and one of the most difficult to detect because of its shape, irregularities, and ambiguous limits. The segmentation of these tumors is one of the most crucial steps for their classification and surgical planning. This article presents a new, accurate, and automatic approach for the precise segmentation of early gliomas (benign tumors), combining the random walk (RW) algorithm and the simple linear iterative clustering algorithm. The study was carried out in four steps. The first step consisted of decomposing the image into superpixels to obtain an initial outline of the tumor. The superpixels were generated using the SLIC algorithm. In the second step, for each superpixel, a set of statistical and multifractal characteristics were calculated (gray‐level co‐occurrence matrix, multifractal detrending moving average). In the third step, the superpixels were classified using a supervised random forest (RF) type classier into healthy or tumorous brain tissue. In the final step, the contour of the detected tumor was enhanced using the customized RW algorithm. The proposed method was evaluated using the Brain Tumor Image Segmentation Challenge 2013 database. The results obtained are competitive compared to other existing methods.

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Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs.The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.

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Diffusion tensor imaging (DTI) is a new technology in magnetic resonance imaging, which allows us to observe the insightful structure of the human body in vivo and non-invasively. It identifies the microstructure of white matter (WM) connectivity by estimating the movement of water molecules at each voxel. This makes possible the identification of the damage to WM integrity caused by Alzheimer’s disease (AD) at its early stage, called mild cognitive impairment (MCI). Furthermore, the brain’s gray matter (GM) atrophy characterizes the main structural changes in AD, which can be sensitively detected by structural MRI (sMRI) modality. In this research, we further develop a novel multi-modality MRI (DTI and sMRI) fusion strategy to detect WM alterations and GM atrophy in AD patients. The latter is based on a 2- dimensional deep convolutional neural network (CNN) features extractor and a Support Vector Machine (SVM) classifier. The fusion framework consists of merging features extracted from DTI scalar metrics (fractional anisotropy (FA) and mean diffusivity (MD)), and GM using 2D-CNN and feeding them to SVM to classify AD vs. cognitively normal (CN), AD vs. MCI, and MCI vs. CN. Our novel multimodal AD method demonstrates a superior performance with an accuracy of 99.79%, 99.6%, and 97.00% for AD/CN, AD/MCI, and MCI/CN respectively.