Sharif Digital Repository

Background: The issue of medial image resolution enhancement is one of the most important topics for medical imaging that helps improve the performance of many post-processing aspects like classification and segmentation towards medical diagnosis. Objective: Our aim in this paper is to evaluate different types of pixel selection models in terms of pixel originality in medical image reconstruction problems. A previous investigation showed that selecting far original pixels has highly better performance than using near unoriginal/estimated pixels while magnifying some benchmarks in digital image processing. Material and Methods: In our technical study, we apply two classical inter-polators,... 

A new method for the shape reconstruction of conducting cylinders from the measured data based on the cubic uniform B-splines was discussed. B-Spline was defined by an ordered set of control points which determined the shape of the curve. A direct problem was formulated using standard integral equations that were solved using method of moments. Principal polarization were treated separately. The method was found less sensitive to noise than those using Fourier expansions  

In this paper, a Kalman filter technique which is operated in time is introduced for noise reduction on CT set of projections to reconstruct medical images. The experiments were done on medical image of kidneys and the simulated projections are captured by CT scanner. Evaluation results indicated that as the number of projections increase in the collected ray sums corrupted by noise the quality of reconstructed image becomes better in terms of contrast and transparency. However, for the comparison issue, the same conditions are applied for reconstruction of medical image in frequency domain using filter back projection technique. It observes that filter back projection technique does not... 

Inpainting refers to the task of filling in the missing or damaged regions of an image in an undetectable manner. We have an image to be reconstructed in a user-defined region. We use a fast decomposition method to obtain two components of the image, namely structure and texture. Reconstruction of each component is performed separately. The missing information in the structure component is reconstructed using a structure inpainting algorithm, while the texture component is repaired by a texture synthesis technique. To obtain the inpainted image, the two reconstructed components are composed together. Taking advantage of both the structure inpainting methods and texture synthesis techniques,... 

A novel CT imaging structure based on Compressive Sensing (CS) is proposed. The main goal is to mitigate the CT imaging time and, thus, X-ray radiation dosage without compromising the image quality. The utilized compressive sensing approach is based on radial Fourier sampling. Thanks to the intrinsic relation between captured radon samples in a CT imaging process and the radial Fourier samples, partial Fourier sampling could be implemented systematically. This systematic compressive sampling helps in better control of required conditions such as incoherence and sparsity to guarantee adequate image quality in comparison to previous CS-based CT imaging structures. Simulation results prove the... 

Continuous-domain visual signals are usually captured as discrete (digital) images. This operation is not invertible in general, in the sense that the continuous-domain signal cannot be exactly reconstructed based on the discrete image, unless it satisfies certain constraints (e.g., bandlimitedness). In this paper, we study the problem of recovering shape images with smooth boundaries from a set of samples. Thus, the reconstructed image is constrained to regenerate the same samples (consistency), as well as forming a shape (bilevel) image. We initially formulate the reconstruction technique by minimizing the shape perimeter over the set of consistent binary shapes. Next, we relax the... 

In this study, a thorough analysis of image reconstruction in an active millimetre-wave (MMW) cylindrical imaging system is performed. To improve the system's performance in terms of the total cost as well as the coupling effect, sparse multistatic arrays are desired. Nevertheless, the existing reconstruction methods fail to operate in sparse multi-static antenna-array systems. Therefore, to address these shortcomings, in this study, at first, the existing MMW mono-static cylindrical image reconstruction methods are modified in both single-frequency and wideband reconstruction platforms. Moreover, in order to further improve the image quality and the system cost, a sparse multi-static... 

Computed tomography (CT) is one of the most valuable diagnostic imaging tools in the clinic and is widely used worldwide. One of the main motivations driving research and development in CT is to achieve better image quality while keeping the radiation dose to the patient as low as possible. In this regard, computer simulations play a key role in the optimization of CT design. In this work, a fan-beam source model (FBSM) for the simulation of multi-slice fan-beam CT scanners using the MCNP Monte Carlo code, has been developed and implemented. The use of this model removes the need for using the collimator in the system configuration and thus to overcome the perennial problem of particle... 

Few-view or sparse-view computed tomography has been recently introduced as a great potential to speed up data acquisition and alleviate the amount of patient radiation dose. This study aims to present a method for high-quality ultra-fast image reconstruction from sparsely sampled projections to overcome problems of previous methods, missing and blurring tissue boundaries, low-contrast objects, variations in shape and texture between the images of different individuals, and their outcomes. To this end, a new deep learning (DL) framework based on convolution neural network (CNN) models is proposed to solve the problem of CT reconstruction under sparsely sampled data, named the multi-receptive... 

In this paper a new method for out-of-focus blur estimation and restoration in presence of Gaussian noise is proposed. This method is based on Bispectrum transformation. It has been proved that functions with Gaussian distribution are suppressed in Bispectrum domain. The image blur criterion is estimated using information in image Bispectrum transformation. Then, an inverse filter is used for blurred image restoration. Experimental results show satisfactory performance of presented method in degradation function parameter estimation in comparison with other algorithms. This superiority is especially obvious in restoration of noisy images, since most of the methods do not take into account... 

The accurate measurement of surface roughness is essential in ensuring the desired quality of machined parts. The most common method of measuring the surface roughness of machined parts is using a surface profile-meter with a contact stylus, which can provide direct measurements of surface profiles. This method has its own disadvantageous such as workpiece surface damage due to mechanical contact between the stylus and the surface, In this paper we proposed a eontactless method using image processing and artificial neural network as a pattern classifier. Having trained the network for any specific workpiece with 10 sample patterns, the system would learn how to approximate the actual surface... 

Nowadays, OCR (Optical Character Recognition) is widely used for converting written documents to digital documents. One of the OCR phases is page segmentation. In page segmentation, text regions must be found in input image. In addition, text parts like text columns must be separated. In this paper, a new method for segmenting Persian/Arabic printed text is proposed. This method is based on Ink Spread Effect idea, a new idea that has particular features. Main features of Persian/Arabic scripts are considered in designing this method. This method is skew resistant and can segment text within frames and tables or regions with gray background. © 2006 ICASE  

Recent years have seen an increasing trend in developing 3D action recognition methods. However, despite the advances, existing models still suffer from some major drawbacks including the lack of any provision for recognizing action sequences with some missing frames. This significantly hampers the applicability of these methods for online scenarios, where only an initial part of sequences are already provided. In this paper, we introduce a novel sequence-To-sequence representation-based algorithm in which a query sample is characterized using a collaborative frame representation of all the training sequences. This way, an optimal classifier is tailored for the existing frames of each query...