Extraction of blood vessels in retinal images is helpful for ophthalmologists to screen a large number of medical disorders. The changes in the retinal vessels due to pathologies can be easily identified by the retinal vessel segmentation. Therefore, in this paper, we propose an automatic method to extract the blood vessels from various normal and abnormal retinal images. Our proposed method uses the advantages of the optimal Gabor filter and morphological reconstruction to employ robust performance analysis to evaluate the accuracy and sensitivity. Moreover, unsharp filter is used which sharpens the edges of the vessels without increasing noise. Our proposed algorithm proves its better performance by achieving the greatest accuracy, sensitivity, and specificity for the DRIVE and the STARE databases respectively. The results illustrate the superior performance of the proposed algorithm when they compared to other existing vessel segmentation methods. Manuscript profile

Epilepsy is a brain disorder which stems from the abnormal activity of neurons and recording of seizures has primary interest in the evaluation ‎of epileptic patients. A seizure is the phenomenon of rhythmicity discharge from either a ‎local area or the whole brain and the individual behavior usually ‎lasts from seconds to minutes. In this work, empirical wavelet transform (EWT) is applied to ‎decompose signals into Electroencephalography (EEG) rhythms. ‎EEG signals are separated into the delta, theta, alpha, beta and gamma ‎rhythms using EWT.‎ The proposed method has been evaluated by the benchmark dataset which is freely downloadable from the Bonn University website. Ellipse area (A) and shortest distance to 45 and 135-degree lines are computed from the 2D projection of reconstructed phase space (RPS) of rhythms as features. After that, the genetic algorithm is used as feature selection. Finally, selected features are fed to the K-nearest neighbor (KNN) classifier for the detection of the seizure (S) and seizure-free (SF) EEG signals. Our proposed method archived 98.33% accuracy in the classification of S and SF EEG signals with a tenfold cross-validation strategy that is higher than previous techniques. Manuscript profile