Mustafa E. Kamasak

A directional total variation

This paper introduces a “directional total variation” (TV) where the gradients are weighted depending on their direction. The introduced directional TV has increased (and tunable) sensitivity to variations at a selected direction. In order to demonstrate the utility of the directional TV, we consider an image denoising formulation. This formulation requires the realization of the “proximal map” of the directional TV. Therefore, it is relevant for more general inverse problem settings as well. We derive an algorithm that solves the problem and use the algorithm to study the effects of the parameters of the directional TV.

Direct reconstruction of kinetic parameter images from dynamic PET data

It is often necessary to estimate the parameters of a compartmental model from PET image data. These kinetic parameters are important because they quantify physiological processes. Existing methods for computing kinetic parametric images work by first reconstructing a sequence of PET images, and then estimating the kinetic parameters for each voxel location in the images. We propose a novel iterative tomographic reconstruction algorithm for directly computing a MAP estimate of the kinetic parameter image directly from dynamic PET sinogram data. This MAP reconstruction process estimates a vector of kinetic parameters at each voxel using explicit models of measurement noise, temporal tracer concentration, and spatial parameter variation. Experimental simulations using a two tissue compartment model show that our method can substantially reduce parameter estimation error.

Digital breast tomosynthesis image reconstruction using 2D and 3D total variation minimization

BackgroundDigital 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) techniques. This can be done by minimizing the l1 norm of the gradient of the image which can also be defined as total variation (TV) minimization. In tomosynthesis imaging problem, this idea was utilized by minimizing total variation of image reconstructed by algebraic reconstruction technique (ART). Previous studies have largely addressed 2-dimensional (2D) TV minimization and only few of them have mentioned 3-dimensional (3D) TV minimization. However, quantitative analysis of 2D and 3D TV minimization with ART in DBT imaging has not been studied.MethodsIn this paper two different DBT image reconstruction algorithms with total variation minimization have been developed and a comprehensive quantitative analysis of these two methods and ART has been carried out: The first method is ART + TV2D where TV is applied to each slice independently. The other method is ART + TV3D in which TV is applied by formulating the minimization problem 3D considering all slices.ResultsA 3D phantom which roughly simulates a breast tomosynthesis image was designed to evaluate the performance of the methods both quantitatively and qualitatively in the sense of visual assessment, structural similarity (SSIM), root means square error (RMSE) of a specific layer of interest (LOI) and total error values. Both methods show superior results in reducing out-of-focus slice blur compared to ART.ConclusionsComputer simulations show that ART + TV3D method substantially enhances the reconstructed image with fewer artifacts and smaller error rates than the other two algorithms under the same configuration and parameters and it provides faster convergence rate.

Support Vector Selection and Adaptation for Remote Sensing Classification

Classification of nonlinearly separable data by nonlinear support vector machines (SVMs) is often a difficult task, particularly due to the necessity of choosing a convenient kernel type. Moreover, in order to get the optimum classification performance with the nonlinear SVM, a kernel and its parameters should be determined in advance. In this paper, we propose a new classification method called support vector selection and adaptation (SVSA) which is applicable to both linearly and nonlinearly separable data without choosing any kernel type. The method consists of two steps: selection and adaptation. In the selection step, first, the support vectors are obtained by a linear SVM. Then, these support vectors are classified by using the K-nearest neighbor method, and some of them are rejected if they are misclassified. In the adaptation step, the remaining support vectors are iteratively adapted with respect to the training data to generate the reference vectors. Afterward, classification of the test data is carried out by 1-nearest neighbor with the reference vectors. The SVSA method was applied to some synthetic data, multisource Colorado data, post-earthquake remote sensing data, and hyperspectral data. The experimental results showed that the SVSA is competitive with the traditional SVM with both linearly and nonlinearly separable data.

A mobile application for cognitive screening of dementia

Neuropsychological assessment tests have an important role in early detection of dementia. Therefore, we designed and implemented a test battery for mobile devices that can be used for mobile cognitive screening (MCS). This battery consists of 33 questions from 14 type of tests for the assessment of 8 different cognitive functions: Arithmetic, orientation, abstraction, attention, memory, language, visual, and executive functions. This test battery is implemented as an application for mobile devices that operates on Android OS. In order to validate the effectiveness of the neuropsychological test battery, it was applied on a group of 23 elderly persons. Within this group, 9 (of age 81.78±4.77) were healthy and 14 (of age 72.55±9.95) were already diagnosed with dementia. The education level of the control group (healthy) and dementia group were comparable as they spent 13.66±5.07 and 13.71±4.14 years at school respectively. For comparison, a validated paper-and-pencil test (Montreal Cognitive Test - MoCA) was applied along with the proposed MCS battery. The proposed test was able to differentiate the individuals in the control and dementia groups for executive, visual, memory, attention, orientation functions with statistical significance (p<0.05). Results of the remaining functions; language, abstraction, and arithmetic were statistically insignificant (p>0.05). The results of MCS and MoCA were compared, and the scores of individuals from these tests were correlated (r(2)=0.57).

An iterative tomosynthesis reconstruction using total variation combined with non-local means filtering

BackgroundAfter the release of compressed sensing (CS) theory, reconstruction algorithms from sparse and incomplete data have shown great improvements in diminishing artifacts of missing data. Following this progress, both local and non-local regularization induced iterative reconstructions have been actively used in limited view angle imaging problems.MethodsIn this study, a 3D iterative image reconstruction method (ART + TV)NLM was introduced by combining local total variation (TV) with non-local means (NLM) filter. In the first step, TV minimization was applied to the image obtained by algebraic reconstruction technique (ART) for background noise removal with preserving edges. In the second step, NLM is used in order to suppress the out of focus slice blur which is the most existent image artifact in tomosynthesis imaging. NLM exploits the similar structures to increase the smoothness in the image reconstructed by ART + TV.ResultsA tomosynthesis system and a 3D phantom were designed to perform simulations to show the superior performance of our proposed (ART + TV)NLM over ART and widely used ART + TV methods. Visual inspections show a significant improvement in image quality compared to ART and ART + TV.ConclusionsRMSE, Structure SIMilarity (SSIM) value and SNR of a specific layer of interest (LOI) showed that by proper selection of NLM parameters, significant improvements can be achieved in terms of convergence rate and image quality.

Clustering Dynamic PET Images on the Projection Domain

Segmentation of dynamic PET images is an important preprocessing step for kinetic parameter estimation. A single time activity curve (TAC) is extracted for each segmented region. This TAC is then used to estimate the kinetic parameters of the segmented region. Current methods perform this task in two independent steps; first dynamic positron emission tomography (PET) images are reconstructed from the projection data using conventional tomographic reconstruction methods, then the TAC of the pixels are clustered into a predetermined number of clusters. In this paper, we propose to cluster the regions of dynamic PET images directly on the projection data and simultaneously estimate the TAC of each cluster. This method does not require an intermediate step of tomographic reconstruction for each time frame. Therefore, the dimensionality of the estimation problem is reduced. The proposed method is compared with image-domain clustering methods based on weighted least squares (WLS) and expectation maximization with Gaussian mixtures methods (GMM-EM). Iterative coordinate descent (ICD) is used to reconstruct the emission images required by these methods. Simulation results show that the proposed method can substantially decrease the number of mislabeled pixels and reduce the root mean squared error (RMSE) of the cluster TACs.

Iterative image reconstruction using non-local means with total variation from insufficient projection data

In this work, algebraic reconstruction technique (ART) is extended by using non-local means (NLM) and total variation (TV) for reduction of artifacts that are due to insufficient projection data. TV and NLM algorithms use different image models and their application in tandem becomes a powerful denoising method that reduces erroneous variations in the image while preserving edges and details. Simulations were performed on a widely used 2D Shepp-Logan phantom to demonstrate performance of the introduced method (ART + TV) NLM and compare it to TV based ART (ART + TV) and ART. The results indicate that (ART + TV) NLM achieves better reconstructions compared to (ART + TV) and ART.

Computation of variance in compartment model parameter estimates from dynamic PET data.

This paper investigates the validity of the analytical framework for bias and variance in kinetic parameter estimations. Analytical computation of bias and variance is compared against Monte Carlo simulations for two different compartment models at different noise levels. Difference between the estimated and measured variance increases with the level of noise and complexity of the compartment model. The standard deviation of the computed variance also increases with the increasing noise-level and model complexity. The difference between the estimated (from the formulation) and measured variance (from Monte Carlo simulations) is less than 1.5% for 1-tissue (1T) compartment model and less than 15% for 2-tissue (2T) compartment model at all noise levels. In addition, the standard deviation in the computed variance is less than 1% for 1T compartment model and less than 10% for 2T compartment model at all noise levels. These results indicate that the proposed framework for the variance in the kinetic parameter estimations can be used for 1-T and 2-T compartment models even in the existence of high noise.

Automated nanofiber diameter measurement in SEM images using a robust image analysis method

Due to the high surface area, porosity, and rigidity, applications of nanofibers and nanosurfaces have developed in recent years. Nanofibers and nanosurfaces are typically produced by electrospinning method. In the production process, determination of average fiber diameter is crucial for quality assessment. Average fiber diameter is determined by manually measuring the diameters of randomly selected fibers on scanning electron microscopy (SEM) images. However, as the number of the images increases, manual fiber diameter determination becomes a tedious and time consuming task as well as being sensitive to human errors. Therefore, an automated fiber diameter measurement system is desired. In the literature, this task is achieved by using image analysis algorithms. Typically, these methods first isolate each fiber in the image and measure the diameter of each isolated fiber. Fiber isolation is an error-prone process. In this study, automated calculation of nanofiber diameter is achieved without fiber isolation using image processing and analysis algorithms. Performance of the proposed method was tested on real data. The effectiveness of the proposed method is shown by comparing automatically and manually measured nanofiber diameter values.