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The aim of this study is to develop a score calculation method for expressing directional dominance in the images. It is predicted that this score will give an information of how much improvement in system performance can be achieved when using a directional total variation-based regularization instead of a total variation. For this purpose, 5 images, which have directional dominance in different...
The importance of learning important features in an automatic manner is growing exponentially as the volume of data and number of systems using pattern recognition techniques continue to increase. In this paper, arousal recognition from multi channels EEG signals was conducted using human crafted statistical features and learned features from 32 different EEG source channels. We have obtained 98.99%...
Emotions play a significant and powerful role in everyday life of human beings. Developing algorithms for computers to recognize emotional expression is a widely studied area. In this study, emotion recognition from Galvanic signals was performed using time domain and wavelet based features. Feature extraction has been done with various feature set attributes. Various length windows have been used...
Between cancer types, Melanom is the one of the most deadliest form. Although, dermoscopy increased rate of diagnosis of the disease, most of the time detection of the disease depends on experience of physicist. There is great effort to diagnosis of the disease using computerized techniques. In this paper, for that purpose, we propose new method that uses fourier descriptors. After the segmentation...
Total variation (TV) minimization has become an important tool for the sparse image reconstruction. In this study a realistic 3D digital breast tomosynthesis (DBT) data was reconstructed and compared using two different forms of TV: (i) 2D reconstruction by applying TV layer by layer (ii) 3D reconstruction of the entire data. It can be assumed that a 3D reconstruction should perform better. However,...
Digital breast tomosynthesis (DBT) generates 3D images of breast by using 2D projections taken from a limited view angle. Due to the ill-posed nature of the image reconstruction in DBT alternative image reconstruction methods have been introduced for better image quality. In this study, an efficient DBT image reconstruction algorithm has been introduced. The proposed method was formulated as combination...
Patient radiation dose is a major issue in computerized tomography (CT) imaging. Therefore, many improvements to the classical reconstruction algorithms are suggested to achieve reasonable image quality with less patient dose. The aim of this work is to improve the well-known algebraic reconstruction algorithm (ART) in order to obtain good image quality with less or limited projection angles. We achieve...
This paper proposes a new 3D iterative reconstruction method for reducing out-of-focus slice blur in tomosynthesis imaging by combining two powerful denoising methods. The methods used in the reconstruction are based on Total Variation (TV) minimization and Non-Local Means (NLM) filtering. A new method (ART+TV)NLM is introduced by adapting both methods to Algebraic reconstruction technique (ART) which...
In limited angle tomographic imaging, artifacts arise due to missing data during the acquisition. To deal with this problem, iterative image reconstruction algorithms have been developed. In iterative reconstruction algorithms, the initial image guess which is often neglected is very crucial and plays an important role as it directly affects the convergence rate. This paper presents a comparison of...
Recently, medical modalities such as low dose CT, MRI and tomosynthesis have focused on generating noise-free images by using fewer measurements. However acquiring or using less data to reconstruct an image increases the noise level in the image. Thus, image denoising has been one of the most active research areas due to the noise existence in most medical imaging modalities. Due to its virtue of...
Digital Breast Tomosynthesis (DBT) is an innovative 3D imaging technique implemented using a limited number of low dose projections, which are taken with the x-ray source moving in a limited angle of rotation around the breast. These low dose projection images should become processed using mathematical methods, to reconstruct tomographic images, resulting in a 3D representation of the imaged breast...
Digital Breast Tomosynthesis (DBT) is an innovative 3D imaging technique implemented using a limited number of low dose projections, which are taken with the x-ray source moving in a limited angle of rotation around the breast. These low dose projection images should become processed using mathematical methods, to reconstruct tomographic images, resulting in a 3D representation of the imaged breast...
Digital breast tomosynthesis (DBT) is a promising imaging modality that provides a 3D reconstructed object from 2D projections acquired over a limited angular range. The diagnostic quality of tomosynthesis depends on multiple variables, each of which needs to be optimized. This study investigates the effects of number of angular projections and the total angular span of those projections on the reconstructed...
In tomosynthesis imaging, a small number of projections are acquired from a limited scan angle which is insufficient to reconstruct the image without undesired artifacts. Iterative reconstruction algorithms have been widely used in order to combat this problem. In this study, an effective compressed sensing (CS) based iterative reconstruction algorithm was implemented by applying total variation minimization...
We present an object-oriented simulator for 3D digital breast tomosynthesis (DBT) system using C++ programming language. The simulator is capable of implementing different iterative reconstruction and total variation (TV) regularization methods on the real world phantom models. A user friendly graphical user interface (GUI) helps users to select and run the desired methods on the designed phantom...
Background Digital breast tomosynthesis (DBT) is an emerging imaging modality which produces three-dimensional radiographic images of breast. DBT reconstructs tomographic images from a limited view angle, thus data acquired from DBT is not sufficient enough to reconstruct an exact image. It was proven that a sparse image from a highly undersampled data can be reconstructed via compressed sensing (CS)...
In tomosynthesis imaging, out-of-focus slice blur problem arises due to incomplete sampling problem. Several approaches have been proposed to deal with this problem. Algebraic reconstruction technique (ART) is one of the most commonly used methods. Total variation (TV) minimization has recently been applied to improve performance of the classical approaches. Though it is able to provide improved results,...
In recent years, several studies have shown the relationship between snoring and obstructive sleep apnea syndrome (OSAS). Instead of time domain analysis of snoring signal, the spectral features and shapes of snores have been found different in simple snorers and OSAS patients. In this study, we propose a method to differentiate simple snorers and OSAS patients based on spectral envelope estimation...
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