Extraction of Blood Vessels Geometric Shape Features with Catheter Localization and Geodesic Distance Transform for Right Coronary Artery Detection.

X-ray angiography is considered the standard imaging sensory system for diagnosing coronary artery diseases. For automated, accurate diagnosis of such diseases, coronary vessels’ detection from the captured low quality and noisy angiography images is challenging. It is essential to detect the main branch of the coronary artery, to resolve such limitations along with the problems due to the sudden changes in the lumen diameter, and the abrupt changes in local artery direction. Accordingly, this paper solved these limitations by proposing a computer-aided detection system for the right coronary artery (RCA) extraction, where geometric shape features with catheter localization and geodesic distance transform in the angiography images through two parts. In part 1, the captured image was initially preprocessed for contrast enhancement using singular value decomposition-based contrast adjustment, followed by generating the vesselness map using Jerman filter, and for further segmentation the K-means was introduced. Afterward, in part 2, the geometric shape features of the RCA, as well as the skeleton gradient transform, and the start/end points were determined to extract the main blood vessel of the RCA. The analysis of the skeletonize image was performed using Geodesic distance transform to examine all branches starting from the predetermined start point and cover the branching till the predefined end points. A ranking matrix, and the inverse of skeletonization were finally carried out to get the actual main branch. The performance of the proposed system was then evaluated using different evaluation metrics on the angiography images...

Optimized Adaptive Frangi-based Coronary Artery Segmentation using Genetic Algorithm

Diseases of coronary artery are deliberated as one of the most common heart diseases leading to death worldwide. For early detection of such disease, the X-ray angiography is a benchmark imaging modality for diagnosing such illness. The acquired X-ray angiography images usually suffer from low quality and the presence of noise. Therefore, for developing a computer-aided diagnosis (CAD) system, vessel enhancement and segmentation play significant role. In this paper, an optimized adapter filter based on Frangi filter was proposed for superior segmentation of the angiography images using genetic algorithm (GA). The original angiography image is initially preprocessed to enhance its contrast followed by generating the vesselness map using the proposed optimized Frangi filter. Then, a segmentation technique is applied to extract only the artery vessels, where the proposed system for extracting only the main vessel was evaluated. The experimental results on angiography images established the superiority of the vessel regions extraction showing 98.58% accuracy compared to the state-of-the-art

A novel optimized neutrosophic k-means using genetic algorithm for skin lesion detection in dermoscopy images

This paper implemented a new skin lesion detection method based on the genetic algorithm (GA) for optimizing the neutrosophic set (NS) operation to reduce the indeterminacy on the dermoscopy images. Then, k-means clustering is applied to segment the skin lesion regions

Synthesis, X-ray, Hirshfeld, and AIM Studies on Zn(II) and Cd(II) Complexes with Pyridine Ligands

The synthesis and crystal structures of three heteroleptic complexes of Zn(II) and Cd(II) with pyridine ligands (ethyl nicotinate (EtNic), N,N-diethylnicotinamide (DiEtNA), and 2-amino-5-picoline (2Ampic) are presented. The complex [Zn(EtNic)2Cl2] (1) showed a distorted tetrahedral coordination geometry with two EtNic ligand units and two chloride ions as monodentate ligands. Complexes [Zn(DiEtNA)(H2O)4(SO4)]·H2O (2) and [Cd(OAc)2(2Ampic)2] (3) had hexa-coordinated Zn(II) and Cd(II) centers. In the former, the Zn(II) was coordinated with three different monodentate ligands, which were DiEtNA, H2O, and SO42−. In 3, the Cd(II) ion was coordinated with two bidentate acetate ions and two monodentate 2Ampic ligand units. The supramolecular structures of the three complexes were elucidated using Hirshfeld analysis. In 1, the most important interactions that governed the molecular packing were O···H (15.5–15.6%), Cl···H (13.6–13.8%), Cl···C (6.3%), and C···H (10.3–10.6%) contacts. For complexes 2 and 3, the H···H, O···H, and C···H contacts dominated. Their percentages were 50.2%, 41.2%, and 7.1%, respectively, for 2 and 57.1%, 19.6%, and 15.2%, respectively, for 3. Only in complex 3, weak π-π stacking interactions between the stacked pyridines were found. The Zn(II) natural charges were calculated using the DFT method to be 0.8775, 1.0559, and 1.2193 for complexes 1–3, respectively. A predominant closed-shell character for the Zn–Cl, Zn–N, Zn–O, Cd–O, and Cd–N bonds was also concluded from an atoms in molecules (AIM) study

An integrated real-time traffic signal system for transit signal priority, incident detection and congestion management

This paper presents a traffic control system that can work standalone to handle various boundary conditions of the recurrent, non-recurrent congestion, transit signal priority and downstream blockage conditions to improve the overall traffic network vehicular productivity and efficiency. The control system uses field detectors’ data to determine the boundary conditions of all incoming and exit links. The developed system is interfaced with CORSIM micro-simulation for rigorous evaluations with different types of signal phase settings. The comparative performance of this control logic is quite satisfactory for some of the most frequently used phase settings in the network with a high number of junctions under highly congested conditions

A Threshold-Based Real-Time Incident Detection System for Urban Traffic Networks

AbstractAs incident detection on a typical busy urban road link or intersection still demands more efficient algorithms, this paper introduces a methodology that can be used to characterize the various traffic patterns (incident or no incident) using typical link passage detectors. Offline urban incident scenarios are generated using a microscopic simulation model assuming varying traffic link flows, signal green phase and cycle times, link lengths. Similar scenarios are also generated for non-incident cases. Three detectors were assumed on each link to extract traffic measures. Comparative numerical statistical analyses were conducted to identify the traffic measures (such as the average speed and flow) that are likely to be affected by the incidents. And further analysis was conducted to quantify the most probable thresholds to be used in the proposed urban incident detection model. The proposed model is validated using simulation data. The performance of the proposed model is assessed using dynamic performance indicators such as the success rate of detecting an incident at a specific cycle time, and the false alarm rate