Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile.

It is vital to develop an appropriate prediction model and link carefully to measurable events such as clinical parameters and patient outcomes to analyze the severity of the disease. Timely identifying retinal diseases is becoming more vital to prevent blindness among young and adults. Investigation of blood vessels delivers preliminary information on the existence and treatment of glaucoma, retinopathy, and so on. During the analysis of diabetic retinopathy, one of the essential steps is to extract the retinal blood vessel accurately. This study presents an improved Gabor filter through various enhancement approaches. The degraded images with the enhancement of certain features can simplify image interpretation both for a human observer and for machine recognition. Thus, in this work, few enhancement approaches such as Gamma corrected adaptively with distributed weight (GCADW), joint equalization of histogram (JEH), homomorphic filter, unsharp masking filter, adaptive unsharp masking filter, and particle swarm optimization (PSO) based unsharp masking filter are taken into consideration. In this paper, an effort has been made to improve the performance of the Gabor filter by combining it with different enhancement methods and to enhance the detection of blood vessels. The performance of all the suggested approaches is assessed on publicly available databases such as DRIVE and CHASE_DB1. The results of all the integrated enhanced techniques are analyzed, discussed, and compared. The best result is delivered by PSO unsharp masking filter combined with the Gabor filter with an accuracy of 0.9593 for the DRIVE database and 0.9685 for the CHASE_DB1 database. The results illustrate the robustness of the recommended model in automatic blood vessel segmentation that makes it possible to be a clinical support decision tool in diabetic retinopathy diagnosis.

A Hybrid Method to Enhance Thick and Thin Vessels for Blood Vessel Segmentation.

Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature.

First- and second-trimester uterine artery pulsatility index as a combination factor in predictive diagnosis of pregnancy-induced hypertension.

OBJECTIVE: Hypertensive disorder of pregnancy is a major cause of fetal and maternal morbidity and mortality. The current approach for pregnancy-induced hypertension (PIH) screening is complex and expensive. The present prospective cohort study assesses the advantage of combining first- and second-trimester uterine artery pulsatility index (UAPI) for predictive diagnosis of PIH. METHODS: A total of 151 prenatal cases in their first trimester were studied and followed up till delivery. The mean UAPI was calculated for the first and second trimesters during the nuchal translucency and anomaly scans. Receiver operating characteristic analysis was used to calculate the cut-off of UAPI for first-trimester, second-trimester, and both trimesters combined. RESULTS: Twenty-seven (17.9%) pregnant women developed PIH. Mean ± SD UAPI values for first and second trimesters were 1.92 ± 0.60 and 1.23 ± 0.36, respectively. The cut-offs for abnormal UAPI were ≥2.51, ≥1.32, and ≥1.91 for first trimester, second trimester, and both trimesters combined, respectively. The sensitivity and specificity of UAPI in predictive diagnosis of PIH were 82% and 95% for first trimester, 93% and 85% for the second trimester, and 93% and 98% for both trimesters combined. CONCLUSION: Combining UAPI of first and second trimesters improves the predictive diagnosis of PIH, which can be carried out during the nuchal translucency and anomaly scans without imparting extra cost to the patient.