Random number generation is one of the human abilities. It is proven that the sequence of random numbers generated by people do not follow full randomness criteria. These numbers produced by brain activity seem to be completely nonstationary. In this paper, we show that there is a distinction between the random numbers generated by different people who provide the discrimination capability, and can be used as a biometric signature. We considered these numbers as a signal, and their complexity for various time-frequency sections was calculated. Then with a proper structure of a support vector machine, we classify the features. The error rate, obtained in this study, shows high discrimination capabilities for this biometric characteristic.

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In this paper, a novel fuzzy scheme for medical X-ray image classification is presented. In this method, each image is partitioned into 25 overlapping subimages and then, we extracted the shape-texture features from shape and directional information of each subimage. In the classification step, we apply a fuzzy membership to each subimage considering the Euclidean distance between feature vector of each subimage and average of feature vectors of training subimages. At last, a hard classification of the test image can be obtained by performing a max operation on the summation of fuzzy memberships. The proposed method is evaluated for image classification on 2655 radiographic images from IRMA dataset with 300 training samples and 2355 test samples. Classification accuracy rates obtained by fuzzy classifier are higher than that of obtained by multilayer perceptron or even SVM classifier.

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Analyzing the features of the chromosomes can be very useful for diagnosis of many genetic disorders or prediction of possible abnormalities that may occur in the future generations. For this purpose, karyotype is often used, for which to be made, it is necessary to identify each one of the 24 chromosomes from the microscopic images. Definition and extraction of the morphological and band pattern-based features for each chromosome is the first step to identify them. Centromere location is an important morphological feature. In this paper, a novel algorithm for centromere localization is presented. The procedure is based on the calculation and analyzing the concavity degree of the chromosome's boundary pixels. In this method, the centerline of the chromosome is computed and the score of each pixel on the centerline is considered as the sum of the concavity degree of two pixels on the chromosome's boundary that are perpendicular to it. Finally, location of the centromere is estimated as one pixel on the centerline which is corresponding to the maximum score. When applied the proposed algorithm on 50 images, an average error of 2.25 pixels for centromere localization is achieved.

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Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy.

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In this paper, we try to find a particular combination of wavelet shrinkage and nonlinear diffusion for noise removal in dental images. We selected the wavelet diffusion and modified its automatic threshold selection by proposing new models for speckle-related modulus. The Laplacian mixture model, Rayleigh mixture model, and circular symmetric Laplacian mixture models were evaluated and, as it could be expected, the latter provided a better model because of its compatibility with heavy tailed structure of the wavelet coefficients besides their interscale dependence. The numerical evaluation of contrast-to-noise ratio (CNR) along with simple observation of the results showed reasonably acceptable improvement of CNR from 2.9149 to 38.8813 in anterior--posterior images, from 41.6131 to 86.3141 in cephal-lateral images, from 13.6414 to 43.4711 in intraoral pictures, and from 6.0102 to 31.8771 in panoramic datasets. Furthermore, technical ability of the proposed filtering method in retaining the possible cavities on dental images was evaluated in two datasets with natural and artificially applied cavities.

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Reduced blood flow due to obstruction is in most cases a primary factor in pressure ulcer formation and creation of bedsores. The aim of this study is to design and manufacture a care system for tissue under pressure, based on variations in blood flow at different depths of tissue. In the manufacture of the system two infrared light transmitters and receivers were located between 5 and 10 mm depth to measure the flow of blood at different in the under- pressure heel tissue. In addition, blood flow was evaluated in an unloaded and loaded condition, with 30 mmHg and 60.0 mmHg. A total of 15 people participated with a mean age of 50. Of these 15; 9 (60%) were men and 6 (40%) were women. Primary measurement results showed different individual differences in variation of blood flow in the tissue. To study signal amplitude changes significantly influenced by external pressure the PPG, P-value was measured. It was noted that there were significant changes in PPG signal amplitude during loading both pressures of 30 and 60 mmHg. Further development of this system would be possible with the use of a more flexible probe and by using a stronger optical receiver and transmitter to access more depth.

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