Biomedical Image Processing / Medical Image Processing
Seyed Hani Hojjati; Ataollah Ebrahimzadeh; Ali Khazaee; Abbas Babajani-Fermi
Volume 11, Issue 1 , May 2017, , Pages 29-40
Abstract
Predicting AD based on Brain network analysis has been the subject of much investigation. Here, we aim to identify the changes in brain in patients that conversion from (Mild Cognitive Impariment) MCI to AD (MCI-C) and non conversion from MCI to AD (MCI-NC), to provide an algorithm for classification ...
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Predicting AD based on Brain network analysis has been the subject of much investigation. Here, we aim to identify the changes in brain in patients that conversion from (Mild Cognitive Impariment) MCI to AD (MCI-C) and non conversion from MCI to AD (MCI-NC), to provide an algorithm for classification of these patients by using a graph theoretical approach. In this algorithm we proposed Discriminant Correlation Analysis (DCA), feature level fusion for multimodal biometric recognition method were applied to the original feature sets. An accuracy of 86/167% was achieved for predicting AD using the DCA and the support vector machine classifier. We also performed a hub node analysis and found the number of hubs in progressive AD patients. Indeed, this is the first neuroimaging study that integrates rs-fMRI with sMRI for detecting conversion from MCI to AD. The proposed classification method highlights the potential of using both resting state fMRI and MRI data for identification of the early stage of AD.
Tissue Engineering
Jafar Ai; Saeed Sarkar; Mohammad Ali Oghabian
Volume 4, Issue 2 , June 2010, , Pages 161-166
Abstract
Various reviews have shown that strong electromagnetic fields have negative effects on human health. This study focused on the effect of MRI radiation on liver functional test histometery of liver in adult male rats. For this purpose, we used an MRI device that could produce 1.5 T electromagnetic radiations, ...
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Various reviews have shown that strong electromagnetic fields have negative effects on human health. This study focused on the effect of MRI radiation on liver functional test histometery of liver in adult male rats. For this purpose, we used an MRI device that could produce 1.5 T electromagnetic radiations, and chose 22 Wistar rats as laboratory animal models. Rats were divided into two equal groups. The first group exposed to 1.5T electromagnetic radiation and RF radiation during a 30- minute MRI scan as experimental group. The control group experienced 1.5T electromagnetic radiation exposure without RF radiation by the same MRI device. The rats were anesthetized and blood samples were obtained from cardiac chambers to measure the serum levels of LDL, HDL, ALT, AST, ALP, total cholesterol, total protein, albumin, total billirobin, and direct bilirobin. Livers were then removed and the specimens fixed. Serial sections (5 μm thick) were prepared from livers and the diameter of hepatocytes and their nuclei were measured. The findings of the present study indicate that, there was a significant increase (P<0.5) in amount of HDL, ALT, AST, ALP, total billirobin, direct bilirobin and there was a significant decrease (P<0.5) in amount of total cholesterol, LDL, total protein, and albumin in experimental group by comparison with control group. But no significant differences were seen in the diameter of hepatocytes and their nuclei between both groups. The electromagnetic radiations of MRI device may influence the level of liver enzymes and liver function without any histomorphologically changes. Conducting clinical trial studies with human subjects is recommended.
Biomedical Image Processing / Medical Image Processing
Emadoddin Fatemizadeh; Parisa Shooshtari
Volume 2, Issue 3 , June 2008, , Pages 191-201
Abstract
Nowadays due to the huge capacity and bandwidth essentials for medical images, communications and storage purposes, medical images compression is one of most important concepts in this area. Error free compression techniques have the weakness of low compression ratio. On the other hand, lossy techniques ...
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Nowadays due to the huge capacity and bandwidth essentials for medical images, communications and storage purposes, medical images compression is one of most important concepts in this area. Error free compression techniques have the weakness of low compression ratio. On the other hand, lossy techniques with high compression ratio result in low quality of the images. In recent years, some special compression schemes have been suggested by splitting the original image into two regions: Region of Interest (ROI) with lossless compression and the Region of Background (ROB) with lossy compression and a lower quality. In this paper, we proposed a novel selective compression approach to compress 3D brain MR images. For this purpose, an adaptive mesh for the first slice was designed and estimation of the gray levels of the next slices was performed through deformations of the mesh elements. After residual image determination, the error between the original image and the approximated image was transformed to the wavelet domain using a region-based discrete wavelet transform (RBDWT). Finally, the wavelet coefficients were coded by an object-based SPIHT coder.
