Sharif Digital Repository

Purpose: Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in radiation therapy, MRI-guided radiation treatment planning is limited by the fact that MRI does not directly provide the electron density map required for absorbed dose calculation. In this work, a new deep convolutional neural network model with efficient learning capability, suitable for applications where the number of training subjects is limited, is proposed to generate accurate synthetic computed tomography (sCT) images from MRI. Methods: This efficient convolutional neural network (eCNN) is built upon a combination of the SegNet architecture (a 13-layer encoder-decoder structure similar to the... 

Synthetic CT (sCT) generation from MR images is yet one of the major challenges in the context of MR-guided radiation planning as well as quantitative PET/MR imaging. Deep convolutional neural networks have recently gained special interest in large range of medical imaging applications including segmentation and image synthesis. In this study, a novel deep convolutional neural network (DCNN) model is presented for synthetic CT generation from single T1-weighted MR image. The proposed method has the merit of highly accelerated convergence rate suitable for applications where the number of training da-taset is limited while highly robust model is required. This algorithm exploits a Visual... 

Background: Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD... 

The aim of the present study is to provide an Atlas of IRAN Offshore Renewable Energy Resources (hereafter called 'the Atlas') to map out wave and tidal resources at a national scale, extending over the area of the Persian Gulf and Sea of Oman. Such an Atlas can provide necessary tools to identify the areas with greatest resource potential and within reach of present technology development. To estimate available tidal energy resources at the site, a two-dimensional tidally driven hydrodynamic numerical model of Persian Gulf was developed using the hydrodynamic model in the MIKE 21 Flow Model (MIKE 21HD), with validation using tidal elevation measurements and tidal stream diamonds from... 

Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions. The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use... 

Iran is home to a treasury of languages representing diverse language families: Iranic, Turkic, Semitic, Indic, Dravidian, Armenian, and Kartvelian, as well as sign languages. Despite valuable research carried out by Iranian and western scholars, there is still no comprehensive publication depicting the geographic distribution and linguistic status of language varieties in Iran. In order to work toward this goal, the Atlas of the Languages of Iran (ALI) (www.iranatlas.net) was officially launched in 2015 as an international, online research programme. The present study opens with a historical overview of the research context and underlines the ongoing necessity of constructing such an atlas... 

Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a...