Sharif Digital Repository

The registration of images is a fundamental task in numerous applications in medical image processing. The importance of medical image registraiton due to the imaging systems development in last decades is obvious to every one. Especially the wide employment and different capabilities of these systems has caused more attention to this field of image processing. Th e application of medical image registraiton is extended from clinical diagnosis and treatment evaluation, to image guided surgery. The dimension of images as well as modalities of imaging and imaging subjectshas made a wide variety of problems in this branch of image processing. Registration, briefly speaking, is a geometrical... 

Image registration is fundamental in medical imaging, enabling the accurate alignment and analysis of images from different modalities. This process is essential for various applications, including the assessment of tissue growth and tumor evolution, preoperative and intraoperative planning, and segmentation. While recent advances in deep learning have improved unsupervised monomodal medical image registration, these methods typically rely on transforming images and computing a similarity loss function between the transformed and target images. However, for multimodal image registration, traditional similarity functions often lack expressiveness and are prone to numerous local optima. To... 

In this research a novel method is proposed which uses the concept of image registration to forecast frames as a new frame up conversion method. This frame rate up conversion method is specially designed for medical videos because of the medical structure of the none rigid image registration used as a field to forecast new frames which causes better results for these kinds of videos. In this method we interpolate new frames in the field of none rigid transformation grid in order to improve video parameters such as blocking, blurriness, etc. which can help doctors to detect abnormalities in such videos much preciously. By analyzing existing methods, we can find that this field is bereft of a... 

Efficient feature selection from images would lead to a lower operational cost in image processing procedures. Recently, alpha stable filters have been used to extract significant features from medical images.
A Feature-based method is introduced for image registration. The feature extraction is based on Bidimensional Empirical Mode Decomposition (BEMD). We have proposed two approach. The first one is a feature selection and reduction method. The registration is performed based on selected features and Bspline interpolation. The second approach is a hierarchically method based on BEMD features. Both methods improved the registration based on intensity of the images, especially for the... 

Predicting protein function accurately is an important issue in the post genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co expression profiles, and other information. Among these methods, the Global Optimization Method is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interaction network. To boost both the accuracy and speed of global optimization method, a new prediction method, Accurate Global Optimization Method (AGOM), is presented in this thesis, which employs optimal repetition method enhanced with frequency of... 

Gene regulatory modeling in different species is one of the main aims of Bioinformatics. Regarding the limitations of the data available and the perspectives which should be taken into account for modeling such networks, proposed methods up to now have not yet been successful in yielding a comprehensive model. In one of the recent researches, the Gene regulation process is considered as a nonlinear dynamic stochastic process and described by state space equations. Afterwards, in order for the unknown parameters to be estimated, Extended Kalman Filtering is used. In this thesis, first of all, Gene complexes are taken into consideration instead of genes and afterwards, Extended Kalman... 

This thesis presents a novel approach for detection and recognition of multi-view faces whose location is unknown and the illumination conditions are varying. The illumination is a big problem in the face detection and recognition aspects. Two completely different methods are proposed for face detection in this thesis. Our proposed methods do not use the skin colour information for face detection. The detection of faces is accomplished after cancelling the effect of the various illumination conditions. Because of the independency of the approaches to the face’s skin colour, persons with every kind of skin colours are detected even in completely dark environments. Next, the detected faces are... 

Cerebrovascular Attack has been ranked the second or third of top 10 death causes in Taiwan. It has caused about 13,000 deaths every year since 1986. Once Cerebrovascular Attack (CVA) occurs, it not only leads to the huge cost of medical care, but even death. All developed countries in the world put CVA prevention and treatment in high priority. However, it is necessary to build a detective model to enhance the accurate diagnosis of CVA. From this detective model, CVA classification rules were extracted and used to improve the diagnosis and detection of CVA. This study acquired 2449 valid samples from this CVA prevention and treatment program, and adopted three classification algorithms,...