Sharif Digital Repository

Cardiovascular disease is considered as the most important cause of death in the world. The coronary vessels, with three main arteries, has quite important. Coronary angiography is still the most common modality for physicians to assess the severity of vessel narrowing or stenosis during percutaneous coronary intervention procedure. For this procedure, a thin, flexible tube called a catheter is put into a blood vessel in your arm, groin (upper thigh), or neck. The tube is threaded into your coronary arteries, and the dye is released into your bloodstream. X-ray pictures are taken while the dye is flowing through the coronary arteries. Physicians evaluate angiographic images visually with... 

Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuroimaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions. To obtain these goals the analysis of fMRI is the first condition which should be met. First methods were linear and assumed the... 

Content Based Image Retrieval (CBIR) is in fact an image search engine which Operates on image Context . in this thesis (project) the aim was to use the Visual attention of humans in detecting the objects in image. in this ability first a salient image of the most important things in the image would be created And after an initial separation , for the final recognition the other features (details) in the image will be used It’s a while that the use of Visual attention models and saliency maps in designing the interfaces between humans and machines has been considered widely. This fact in the design of CBIR systems has not a good background (satisfying history). In this thesis I have... 

Image Registration is spatially alignment of two images in a wide range of applications such as remote sensing, computer assisted surgery, and medical image analysis and processing. In general, registration algorithms can be categorized as either intensity based or feature based. The feature based methods use the alignment between the extracted features in two images. The simplest feature is images intensity which is directly used in the intensity based method via similarity measure. This similarity measure quantifies the matching of two images.Similarity measure is main core of image registration algorithms. Spatially varying intensity dis-tortion is an important challenge in a wide range... 

Based on Shannon theory, continuous-time band-limited signals are guaranteed to be recovered per-fectly subject to sampling with Nyquist rate. Due to inherently slow MRI sensors, sampling with Nyquist rate excruciatingly increases the scan time. This leads to patient inconvenience along with degradation in image quality caused by geometrical distortions.In recent years, Compressed Sensing (CS) has been introduced as an alternative to the Nyquist theory for the acquisition of sparse or compressible signals that can be well approximated by K ≪ N coeffi-cients from a N-dimensional basis. In CS theory, measurements are actually inner products of signal x with a base vector ϕi. In Fourier encoded... 

Evidence shows that people with depressive disorder show altered functional connectivity in some of the parts of the brain. The functional characteristics of these brain areas in people with this disorder have not been completely determined. On the other hand, some researchers have rejected the static nature of functional connectivity and stated that functional connectivity changes over time. Measuring brain activity non-invasively with functional magnetic resonance imaging increases our understanding of brain organizations and functional mechanisms, so in this study, we used the functional magnetic resonance imaging data of 18 healthy subjects and 18 subjects with depression. Method: The... 

Image registration is the process of matching the coordinate systems of two or more images. Medical image registration has been used in a variety of applications such as segmentation, motion tracking and etc. Recently, the use of deep neural networks has been demonstrated as a useful approach to registration problems. In this work, we propose two separate novel Convolutional Neural Network (CNN) architectures for multi-modal rigid and affine registration of the CT-MRI images of the brain. A dataset consisting of CT-MRI images of 37 subjects was used for training and evaluation of the networks. For both networks, the proposed models achieved high mutual information value between predicted CT... 

Content-based image retrieval (CBIR) is becoming an important field with the advance of multimedia and imaging technology everincreasingly. It makes use of image features, such as color, shape and texture, to index images with minimal human intervention. Among many retrieval features associated with CBIR, texture retrieval is one of the most powerful. Content-based image retrieval can also be utilized to locate medical images in large databases. In this research, we introduce a content-based approach to medical image retrieval. A case study, which describes the methodology of a CBIR system for retrieving digital human brain MRI database based on textural features retrieval, is then... 

Extracting the minutia of a fingerprint image and then hiding this information in the original image in order to increase the security has been explained and implemented in this thesis. One of the methods which can embed the predetermined digital data in the image is digital watermarking. In this thesis, the host image is a fingerprint image whose minutia has been embedded in that image. Embedding can be done in two spatial and frequency domains. In this thesis, embedding is implemented by combining two domains. Embedding in the frequency domain is applied to transforming coefficients directly from the image then by spatial method the information is embedded. Actually combining DWT and LSB... 

Nowadays, image registration is considered as one of usual issues in medical researches whose new findings are expanding outstandingly and it has reached a high level of maturity. Generally speaking, image registration is a task to reliably estimate the geometric transformation such that two images can be precisely aligned. With respect to different uses of image registration in medical applications, it has attracted the attention of many scholars and there has been made significant improvement in this realm. Image registration is still one of the active branches in medical image processing due to its wide applications and problems. Graphs, thanks to their geometric structures and intuitive... 

Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disorders that cause repetitive behaviors and social and communication skills abnormalities. Autistic Disorder(AD) is one of the disorders in ASD that is being investigated in this study. There has been an increase in research about AD in recent years due to the increasing AD prevalence and the high autistic living costs. The dynamic functional connectivity between healthy and autistic groups has been analyzed by using graph theory. The brain is modeled as a dynamic graph using resting-state fMRI. The graph theory metric is calculated in the dynamic graph of each subject, and the distinction of the two groups is checked using... 

Image registration is the process of estimating and applying a spatial transformation to a moving image with the aim of spatially aligning it with a fixed image. This allows for the combination of images with complementary information, such as images with different modalities, acquisition times, and even coming from separate individuals, with the purpose of producing more information-rich results. Image registration is a crucial step in many medical applications, such as analyzing the growth and changes of tissue and tumors, preoperative planning, image-guided surgery, radiation therapy planning and various segmentation tasks. Reinforcement learning is a science and mathematical paradigm for... 

Genetic differences among patients and cancer types result in different responses to treatments and care from patients. Using personalized medicine, treatments and care can be designed with the specific needs of the patient in mind. To achieve this goal, the informative characteristics of the patient and the disease should be quantified. Quantitative Imaging or Radiomics are concerned with the characterization and quantification of the phenotypical characteristics of the tumors from medical images. Developing handcrafted features is time-consuming and requires the 3D volume of the tumor to be segmented before extracting the features. The segmentation task is considered an open problem and... 

Motifs are patterns which can be extracted from specific subsequences of promoter region of some related genes. Transcription factor proteins bind to these subsequences and play a significant role in gene expression regulation.
Motif discovery is a challenging problem in molecular biology and has been attracting researcher’s attention for years. Different kind of data and computational methods have been used to unravel this problem, but there is still room for improvement. In this study, our goal was to develop a method with the ability to identify all the TFBS signals, including known and unknown, inside the input set of sequences. We developed a clustering method specialized as part... 

In recent years, due to the structural need of most medical data for graphic models such as the graphic model of patients and the loss of data correlation in previous methods, graphic methods have been designed and developed. On the other hand, with the growing presence of magnetic resonance imaging devices in various medical centers, a large amount of functional magnetic resonance images of healthy and sick people have become available to researchers. In this study, our goal is to use a new method in the field of graphic modeling so that we can extract functional connectivity graphs from functional magnetic resonance images and measure the performance of these graphs in different groups of... 

Functional Magnetic Resonance Imaging (fMRI) is one of the imaging techniques that are used to study human brain function and neurological disease diagnosis. Popular techniques in fMRI utilize the blood oxygenation level dependent (BOLD) contrast, which is based on the differing magnetic properties of oxygenated (diamagnetic) and deoxygenated (paramagnetic) blood. In order to analyze fMRI data, hypothesis-driven or data-driven methods can be used. Among data-driven techniques, Independent Component Analysis (ICA) provides a powerful method for the exploratory analysis of fMRI data. In this thesis, we use ICA on fMRI data for detecting active regions in brain, without a-priori knowledge of... 

The functional network of the human brain is altered in many neurological and psychiatric disorders. Characterizing brain activity in terms of functionally segregated regions does not reveal anything about the communication among different brain regions and how such inter-communication could influence neural activity in each local region. The aim of this project is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the simulated, realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral... 

Diffusion Weighted Imaging (DWI) is a non-invasive method for investigating the brain white matter. Assuming the Gaussian model for diffusion process, diffusion tensor is constructed and Diffusion Tensor Images (DTI) are obtained. White matter is constructed from fiber bundles which have crossing in most of the regions. In the crossing regions, the Gaussian model cannot work. In this situation, DTI cannot reconstruct the fiber structures correctly. Therefore, High Angular Resolution Diffusion Imaging (HARDI) was proposed to solve this problem. Q-ball imaging is a new technique for HARDI reconstruction which is useful for estimating diffusion Orientation Distribution Function (ODF). ODF is a... 

The current gold standard for clinical diagnosis of melanoma is excisional biopsy and histopathologic analysis. Approximately 15–30 benign lesions are biopsied to diagnose each melanoma. In addition, biopsies are invasive and result in pain, anxiety, scarring, and disfigurement of patients, which can add additional burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT), with its highresolution and intermediate penetration depth, can potentially provide required diagnostic information noninvasively.
an image analysis algorithm, "optical radiomic melanoma detection (ORMD)" has been presented which...