Sharif Digital Repository

Epilepsy is the most common neurological disorder that is known with uncontrolled seizure. Around 30% of patients with epilepsy resist to all forms of medical treatments and therefore, the removal of epileptic brain tissue is the only solution to get these patients free from chronical seizures. The precise detection of an epileptic zone is key to its treatment. In this paper, we propose a method of epilepsy detection using brain magnetic field. The possibility of superparamagnetic nanoparticle (SPMN) as sensors for the detection of the epileptic area inside the brain is investigated. The aggregation of nanoparticles in the weak magnetic field of epileptic brain is modeled using potential... 

Lumbar disc diseases are the commonest complaint of Lower Back Pain (LBP). In this paper, a new method for automatic diagnosis of lumbar disc herniation is proposed which is based on clinical Magnetic Resonance Images (MRI) data. We use T2-W sagittal and myelograph images. Our method uses Otsu thresholding method to extract the spinal cord from MR images of Lumbar disc. In the next step, a third-order polynomial is aligned on the extracted spinal cords, and in the end of preprocessing step all the T2-W sagittal images are prepared for extracting disc boundary and labeling. After labeling and extracting a ROI for each disc, intensity and shape features are used for classification. The... 

Image segmentation with clustering approach is widely used in biomedical application. Fuzzy c-means (FCM) clustering is able to preserve the information between tissues in image, but not taking spatial information into account, makes segmentation results of the standard FCM sensitive to noise. To overcome the above shortcoming, a modified FCM algorithm for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster by smoothing it by Total Variation (TV) denoising. The proposed algorithm is evaluated with accuracy index in... 

Diffusion imaging with confinement tensor (DICT) is a new model that employs a tensorial representation of the geometry confining the movements of water molecules. The model differs substantially from the commonly employed diffusion tensor imaging (DTI) technique even at small diffusion weightings when the dependence of the signal on the timing parameters of the pulse sequence is concerned. In this work, we assess the accuracy of the two models on a data set acquired from an excised monkey brain. The publicly available data set features differing values for diffusion pulse duration and separation. Our results indicate that the normalized mean squared error is reduced in an overwhelming... 

One crucial part of an image registration algorithm is utilization of an appropriate similarity metric. For common similarity metrics such as CC or MI, it is assumed that the intensities of image pixels are independent from each other and stationary. Accepting these assumptions, one will have difficulty doing image registration in the presence of spatially varying intensity distortion. In Myronenko et al. [5] a solution based on minimization of residual complexity is introduced to solve this problem. In this work, the weakness of RC method is investigated for more complex spatially varying intensity distortions and a modification of this method is presented to improve its performance in such... 

Diffusion weighted imaging is a non-invasive method for investigation of brain fiber bundles. In diffusion tensor imaging (DTI), the diffusion of water molecules is assumed Gaussian, therefore, it can just show a single fiber direction in a voxel. To overcome this limitation, a number of high angular resolution diffusion imaging methods have been proposed. One of these techniques is Q-ball imaging. Using this method, we can extract orientation distribution function (ODF) that shows the orientations of multiple fibers in a voxel. For extracting the fiber directions, the maxima of the ODFs are conventionally determined. However, the results of this approach are sensitive to noise. To improve... 

Epidemiological studies have identified obesity asa possible risk factor for low back disorders. Biomechanical models can help test such hypothesis and shed light on the mechanism involved. A novel subject-specific musculoskeletal-modelling approach is introduced to estimate spinal loads during static activities in five healthy obese (BMI>30kg/m2) and five normal-weight (20