Comparison of tomographic kidney volumes measured by semiautomatic segmentation method with scintigraphic split renal function in predicting posttransplant kidney functions.

Introduction: In this study, it was aimed to compare scintigraphic split renal function (SRF) and computed tomographic (CT) kidney volumes by semiautomatic segmentation method in predicting graft functions after kidney transplantation.

Methods: One hundred and twelve patients (77 males, 35 females) who had a living-donor kidney transplant between 2015 and 2017 in our centre were included in the study. While SRF was calculated with technetium-99m-diethylenetriaminepentaacetic acid ( Tc-DTPA) scintigraphy, CT angiography was used for volumetric calculations.

Effect of brightness of visual stimuli on EEG signals.

The aim of this study was to examine brightness effect, which is the perceptual property of visual stimuli, on brain responses obtained during visual processing of these stimuli. For this purpose, brain responses of the brain to changes in brightness were explored comparatively using different emotional images (pleasant, unpleasant and neutral) with different luminance levels. In the study, electroencephalography recordings from 12 different electrode sites of 31 healthy participants were used.

Automated segmentation of the injured kidney due to abdominal trauma.

The objective of this study is to propose and validate a computer-aided segmentation system which performs the automated segmentation of injured kidney in the presence of contusion, peri-, intra-, sub-capsular hematoma, laceration, active extravasation and urine leak due to abdominal trauma. In the present study, total multi-phase CT scans of thirty-seven cases were used; seventeen of them for the development of the method and twenty of them for the validation of the method. The proposed algorithm contains three steps: determination of the kidney mask using Circular Hough Transform, segmentation of the renal parenchyma of the kidney applying the symmetry property to the histogram, and estimation of the kidney volume.

A New Practical Intracerebral Hematoma Volume Calculation Method and Comparison to simple ABC/2.

Aim: To find a more practical and effective formula than simple ABC/2 (sABC/2) to calculate the hematoma volume in patients with subdural and parenchymal haemorrhage.

Material And Methods: We reviewed the records of 157 patients who underwent brain computed tomography examinations for stroke from January to October 2017. Our method, sABC/2 formula, and the planimetric method (the gold standard) were used for measuring the volumes of hematoma.

A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans.

Purpose: Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives.

Computer-Aided Detection (CADx) for Plastic Deformation Fractures in Pediatric Forearm.

Bowing fractures are incomplete fractures of tubular long bones, often observed in pediatric patients, where plain radiographic film is the non-invasive imaging modality of choice in routine radiological workflow. Due to weak association between bent bone and distinct cortex disruption, bowing fractures may not be diagnosed properly while reading plain radiography. Missed fractures and dislocations are common in accidents and emergency practice, particularly in children.

The effect of recording site on extracted features of motor unit action potential.

Motor unit action potential (MUAP), which consists of individual muscle fiber action potentials (MFAPs), represents the electrical activity of the motor unit. The values of the MUAP features are changed by denervation and reinnervation in neurogenic involvement as well as muscle fiber loss with increased diameter variability in myopathic diseases. The present study is designed to investigate how increased muscle fiber diameter variability affects MUAP parameters in simulated motor units.

Automated segmentation of the injured spleen.

Purpose: To develop a novel automated method for segmentation of the injured spleen using morphological properties following abdominal trauma. Average attenuation of a normal spleen in computed tomography (CT) does not vary significantly between subjects. However, in the case of solid organ injury, the shape and attenuation of the spleen on CT may vary depending on the time and severity of the injury.

A novel method for pulmonary embolism detection in CTA images.

In this paper, we propose a new computer-aided detection (CAD) - based method to detect pulmonary embolism (PE) in computed tomography angiography images (CTAI). Since lung vessel segmentation is the main objective to provide high sensitivity in PE detection, this method performs accurate lung vessel segmentation. To concatenate clogged vessels due to PEs, the starting region of PEs and some reference points (RPs) are determined.

Classification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithms.

In this paper, classification of Juvenile Myoclonic Epilepsy (JME) patients and healthy volunteers included into Normal Control (NC) groups was established using Feed-Forward Neural Networks (NN), Support Vector Machines (SVM), Decision Trees (DT), and Naïve Bayes (NB) methods by utilizing the data obtained through the scanning EMG method used in a clinical study. An experimental setup was built for this purpose. 105 motor units were measured.

Computerized lung nodule detection using 3D feature extraction and learning based algorithms.

In this paper, a Computer Aided Detection (CAD) system based on three-dimensional (3D) feature extraction is introduced to detect lung nodules. First, eight directional search was applied in order to extract regions of interests (ROIs). Then, 3D feature extraction was performed which includes 3D connected component labeling, straightness calculation, thickness calculation, determining the middle slice, vertical and horizontal widths calculation, regularity calculation, and calculation of vertical and horizontal black pixel ratios.

Colonic polyp detection in CT colonography with fuzzy rule based 3D template matching.

In this paper, we introduced a computer aided detection (CAD) system to facilitate colonic polyp detection in computer tomography (CT) data using cellular neural network, genetic algorithm and three dimensional (3D) template matching with fuzzy rule based tresholding. The CAD system extracts colon region from CT images using cellular neural network (CNN) having A, B and I templates that are optimized by genetic algorithm in order to improve the segmentation performance. Then, the system performs a 3D template matching within four layers with three different cell of 8 x 8, 12 x 12 and 20 x 20 to detect polyps.

A preliminary study on computerized lesion localization in MR mammography using 3D nMITR maps, multilayer cellular neural networks, and fuzzy c-partitioning.

Cellular neural networks (CNNs) are massively parallel cellular structures with learning abilities. They can be used to realize complex image processing applications efficiently and in almost real time. In this preliminary study, we propose a novel, robust, and fully automated system based on CNNs to facilitate lesion localization in contrast-enhanced MR mammography, a difficult task requiring the processing of a large number of images with attention paid to minute details.

Nodule detection in a lung region that's segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding.

Objective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels.

Materials And Methods: Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN).

Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching.

A novel fully automated system is introduced to facilitate lesion detection in dynamic contrast-enhanced, magnetic resonance mammography (DCE-MRM). The system extracts breast regions from pre-contrast images using a cellular neural network, generates normalized maximum intensity-time ratio (nMITR) maps and performs 3D template matching with three layers of 12x12 cells to detect lesions. A breast is considered to be properly segmented when relative overlap >0.

Lung nodule diagnosis using 3D template matching.

In this paper, to utilize the third dimension of Computed Tomography, regions of interest (ROI) slices were combined to form 3D ROI image and a 3D template was determined to find the structures with similar properties of nodules. Convolution of 3D ROI image with the proposed template strengthens the shapes similar to the template and weakens the other ones. False-positive (FP) per nodule and per slice versus diagnosis sensitivity were obtained.

Mammographic mass detection using a mass template.

Objective: The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using templates.

Materials And Methods: Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and regions of interest (ROI) were identified using various thresholds.