Liver cancer emerges as a mass in the right upper of the abdomen with general symptoms such as jaundice and ‎weakness. In recent years, the liver cancer has been responsible for increasing the rate of deaths. Due to some discrepancies in the ‎analytical results of CT images and the disagreement among specialists about different parts of the liver, ‎accurate diagnosis of possible conditions requires skill, experience, and precision. In this paper, a new ‎integrative model based on image processing techniques and machine learning is provided, which is used for ‎segmentation of damages caused by the liver disease on CT images. The implementation process consists of three ‎steps: (1) using discrete wavelet transform to remove noise and separate the region of interest (ROI) in the image; (2) ‎creating the recognition pattern based on feature extraction by Gray-Level Co-occurrence matrix and ‎hierarchical visual HMAX model; reducing the feature dimensions is also optimized by principle ‎component analysis and support vector machine (SVM) classification, and finally (3) evaluating the algorithm performance by using K-fold method. The results of implementation were satisfactory both in performance evaluation and use of ‎features selection. The mean recognition accuracy on test images was 91.7%. The implementation was in the ‎presence of both descriptors irrespective of feature dimension ‎reduction; with unique HMAX model and feature ‎dimension reduction and application of both ‎descriptors and reduction of feature dimensions and their effect ‎on recognition were measured.‎ تفاصيل المقالة

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. تفاصيل المقالة

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. تفاصيل المقالة

The eye is one of the sensitive organs of the body that is affected by various factors. One of these diseases is glaucoma. Glaucoma is one of the most common ophthalmic diseases that affects the optic disc area and changes this area in terms of size, color and texture. For this reason, the detection of the optic disc area in retinal fundus images is one of the most basic steps in the process of automatic diagnosis of ocular diseases, including glaucoma. Due to the importance of eye diseases and their high incidence, the introduction of new methods in the process of automatic detection of optic disc area by analysis of retinal color images can reduce the volume and computational load, and it helps us to improve the process of early diagnosis of eye diseases. For the reasons mentioned, in this paper, a new method based on the graph-based visual saliency model, along with the watershed algorithm and region growing algorithm to detect optic disc area in retinal fundus images have been suggested to help diagnose eye diseases including glaucoma. According to the proposed method, in this paper, we were able to detect the optic disc area with a 99.1% standard success rate in DRIONS database. تفاصيل المقالة