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Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264
, Article Proceedings of SPIE - The International Society for Optical Engineering, 9 December 2011 through 10 December 2011, Singapore ; Volume 8349 , December , 2012 ; 0277786X (ISSN) ; 9780819490254 (ISBN) ; Kasaei, S ; Sharif University of Technology
Abstract
Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more...
Brain activity estimation using EEG-only recordings calibrated with joint EEG-fMRI recordings using compressive sensing
, Article 13th International Conference on Sampling Theory and Applications, SampTA 2019, 8 July 2019 through 12 July 2019 ; 2019 ; 9781728137414 (ISBN) ; Amini, A ; Ghazizadeh, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Electroencephalogram (EEG) is a noninvasive, low-cost brain recording tool with high temporal but poor spatial resolution. In contrast, functional magnetic resonance imaging (fMRI) is a rather expensive brain recording tool with high spatial and poor temporal resolution. In this study, we aim at recovering the brain activity (source localization and activity-intensity) with high spatial resolution using only EEG recordings. Each EEG electrode records a linear combination of the activities of various parts of the brain. As a result, a multi-electrode EEG recording represents the brain activities via a linear mixing matrix. Due to distance attenuation, this matrix is almost sparse. Using...
Alzheimer’s disease early diagnosis using manifold-based semi-supervised learning
, Article Brain Sciences ; Volume 7, Issue 8 , 2017 ; 20763425 (ISSN) ; Habibollahi Saatlou, F ; Mohammadzade, H ; Sharif University of Technology
Abstract
Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer’s disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests, therefore, an efficient approach for accurate prediction of the...