Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning.

Objective: This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms.

Materials And Methods: High-resolution radiographs of 200 patients aged 20-77 (41.0 ± 12.

Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network.

The uncontrollable growth of cells in the breast tissue causes breast cancer which is the second most common type of cancer affecting women in the United States. Normally, human epidermal growth factor receptor 2 (HER2) proteins are responsible for the division and growth of healthy breast cells. HER2 status is currently assessed using immunohistochemistry (IHC) as well as in situ hybridization (ISH) in equivocal cases.

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.

A compressed sensing based approach on Discrete Algebraic Reconstruction Technique.

Discrete tomography (DT) techniques are capable of computing better results, even using less number of projections than the continuous tomography techniques. Discrete Algebraic Reconstruction Technique (DART) is an iterative reconstruction method proposed to achieve this goal by exploiting a prior knowledge on the gray levels and assuming that the scanned object is composed from a few different densities. In this paper, DART method is combined with an initial total variation minimization (TvMin) phase to ensure a better initial guess and extended with a segmentation procedure in which the threshold values are estimated from a finite set of candidates to minimize both the projection error and the total variation (TV) simultaneously.

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.

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

Background: After 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.

Methods: In 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.

Quality and precision of parametric images created from PET sinogram data by direct reconstruction: proof of concept.

We have previously implemented the direct reconstruction of dense kinetic model parameter images ("parametric images") from sinogram data, and compared it to conventional image domain kinetic parameter estimation methods . Although it has been shown that the direct reconstruction algorithm estimates the kinetic model parameters with lower root mean squared error than the conventional image domain techniques, some theoretical obstacles remain. These obstacles include the difficulty of evaluating the accuracy and precision of the estimated parameters.

An object-oriented simulator for 3D digital breast tomosynthesis imaging system.

Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging.

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

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) techniques.

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

Purpose: This paper investigates the validity of the analytical framework for variance ("analytical variance") in kinetic parameter and macroparameter estimations. Analytical variance is compared against the variance obtained from Monte Carlo simulations ("MC variance") for two different compartment models at different noise levels.

Methods: Kinetic parameters for one-tissue (1T) and two-tissue (2T) compartment models are used to generate time-activity curves (TAC).

Protein biomarkers for multiple sclerosis: semi-quantitative analysis of cerebrospinal fluid candidate protein biomarkers in different forms of multiple sclerosis.

Background: The complex pathogenesis of multiple sclerosis, combined with an unpredictable prognosis, requires identification of disease-specific diagnostic and prognostic biomarkers.

Objective: To determine whether inflammatory proteins, such as neurofilament light chain, myelin oligodendrocyte glycoprotein and myelin basic protein, and neurodegenerative proteins, such as tau and glial fibrillary acidic protein, can serve as biomarkers for predicting the clinical subtype and prognosis of MS.

Methods: Cerebrospinal fluid and serum samples were collected from patients with a diagnosis of clinically isolated syndrome (n = 46), relapsing-remitting MS (n = 67) or primary-progressive MS (n = 22) along with controls having other non-inflammatory neurological disease (n = 22).

Clustering dynamic PET images on the Gaussian distributed sinogram domain.

Segmentation of dynamic PET images is an important preprocessing step for kinetic parameter estimation. The time activity curve (TAC) of individual pixels has very low signal-to-noise ratio (SNR). Therefore, the kinetic parameters estimated from the TAC of an individual pixel may not be accurate, and these estimations may have very high spatial variance.