Objective Intravascular ultrasound (IVUS) is a diagnostic imaging technique for tomographic visualization of coronary arteries. Automatic analysis of IVUS images is difficult due to speckle noise, artifacts of the catheter, and shadows generated by calcifications. We designed and implemented a system for automated segmentation of coronary artery IVUS images. Methods Two methods for automatic detection of the intima and the media-adventitia borders in IVUS coronary artery images were developed and compared. The first method uses the parametric deformable models, while the second method is based on the geometric deformable models. The initial locations of the borders are approximated using two different edge detection methods. The final borders are then defined using the two deformable models. Finally, the calcified regions between the extracted borders are identified using a Bayesian classifier. The performance of the proposed methods was evaluated using 60 different IVUS images obtained from 7 patients. Results Segmented images were compared with manually outlined contours. We compared the performance of calcified region characterization methods using ROC analysis and

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Biometrics is the technique of uniquely recognizing a person among a group of people. It is usually performed based on one or more of human's intrinsic physical or behavioral traits. One such trait is the electroencephalogram (EEG) signal. In this paper, the feasibility of Visual Evoked Potential (VEP) in the gamma band of EEG signal, as a physiological trait, is studied, and used to identify individuals in a group of 20 people. To this end, the parameters of the AR model together with the peak of the power spectrum density (PSD) of the gamma band VEP signal (GMVEP) are considered as main useful features. Next, the Fisher's Linear Discriminant (FLD) is used to reduce the feature vector dimensions. Finally, the K Nearest Neighborhood (KNN) technique is employed to classify the data and the leave-one-out cross validation method is used for accuracy assessment. A correct classification rate of 100% is achieved.

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Objective:Overweight and obesity in children is a global problem. Besides physical effects, obesity has harmful psychological effects on children.Methods:We carried out cross-sectional community-based study to investigate the relation between body mass index (BMI) and cognitive functioning in preschool children. Thirteen socioeconomical elements of 1151 children were measured and analyzed based on their intelligence quotient (IQ) test results. Thirteen out of 33 provinces were selected randomly, and schools were selected as clusters in rural and urban areas. Descriptive statistics, t-test, analysis of variance and regression were used when appropriate.Results:Our analysis showed that IQ was associated with household income, place of residence, delivery type, type of infant feeding and father's and mother's education level (P<0.001 for all). Using penalized linear regression for eliminating the impact of confounding factor, our study shows that, living in metropolitan (β=2.411) and urban areas (β=2.761), the level of participants' father's education (β=5.251) was positively and BMI (β=−0.594) was negatively related with IQ test results.ConclusionsThe findings of the present study showed that a lower IQ score is associated with higher BMI. However, this relation appears to be largely mediated when the socioeconomic status was considered.

Retinal images acquired using a fundus camera often contain low grey, low level contrast and are of low dynamic range. This may seriously affect the automatic segmentation stage and subsequent results; hence, it is necessary to carry-out preprocessing to improve image contrast results before segmentation. Here we present a new multi-scale method for retinal image contrast enhancement using Contourlet transform. In this paper, a combination of feature extraction approach which utilizes Local Binary Pattern (LBP), morphological method and spatial image processing is proposed for segmenting the retinal blood vessels in optic fundus images. Furthermore, performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) is investigated in the classification section. The performance of the proposed algorithm is tested on the publicly available DRIVE database. The results are numerically assessed for different proposed algorithms.

IVUS-derived virtual histology (VH) permits the assessment of atherosclerotic plaque morphology by using radiofrequency analysis of ultrasound signals. However, it requires the acquisition to be ECG-gated, which is a major limitation of VH. Indeed, its computation can only be performed once per cardiac cycle, which significantly decreases the longitudinal resolution of VH. To overcome this limitation, the introduction of an image-based plaque characterization is of great importance. Current IVUS image processing techniques do not allow adequate identification of the coronary artery plaques. This can be improved by defining appropriate features for the different kinds of plaques. In this paper, a novel feature extraction method based on Run-length algorithm is presented and used for improving the automated characterization of the plaques within the IVUS images. The proposed feature extraction method is applied to 200 IVUS images obtained from five patients. As a result an accuracy rate of 77% was achieved. Comparing this to the accuracy rates of 75% and 71% obtained using co-occurrence and local binary pattern methods respectively indicates the superior performance of the proposed feature extraction method.

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