Journal of Medical Signals and Sensors
Volume:2 Issue: 2, Apr-Jun 2012

In this paper a novel fuzzy scheme for medical x-ray image classification is presented. In this method, any image is partitioned in to 25 overlapping subimages and then shape-texture features are extracted from shape and directional information extracted from any subimage. In the classification stage, we apply a fuzzy membership to any subimage with respect to Euclidean distance between feature vector of any subimage and average of feature vectors of training subimages. At last, the summation of fuzzy memberships in any test image is obtained and its maximum can be used to classify the test image. The proposed method is evaluated for image classification on 2215 radiographic images from IRMA dataset with 195 training samples and 2020 test samples. Classification accuracy rate obtained by fuzzy classifier is much higher than.

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 non stationary. In this paper, we show that there is a distinction between the random numbers generated by different people who provides 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 (SVM), we classify the features. The error rate obtained in this study, shows high discrimination capabilities for this biometric characteristic.

Many genetic disorders or possible abnormalities that may occur in the future generations can be predicted through analyzing the features of the chromosomes. For this purpose, karyotype is often used which to make it, there is necessary to identify each one of the 24 chromosomes from the microscopic images. The first step of this process is to define the morphological and band pattern based features for each chromosome. An important class of morphological features is the location of the chromosome’s centromere. Thus, centromere localization is an initial step in designing an automatic karyotyping system. 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 centromere is estimated as one pixel on the centerline which is corresponding to maximum score. When applied the proposed algorithm on 50 images, an average error of 2.25 pixels for centromere localization is achieved.

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 hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, bees algorithm (BA) is proposed for selecting of appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer (WBC) database and simulation results show that the recommended system has a high accuracy.

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 and circular symmetric Laplacian mixture models were evaluated and as it could be expected, the latter could make a better model of data because of its compatibility with heavy tailed structure of wavelet coefficients besides their interscale dependency.

Different methods arising from scientific investigation for removing noise from chaotic signals have been introduced and one of their best is Local Projection approach. The local projection approach projects the chaotic data in the neighborhood onto the hyper-plane. Selection of neighborhood radius has a direct impact on its performance. A noticeable feature associated with chaotic trajectories is their stretching and folding. An idea would be making use of the large neighborhood radius in those points of the trajectory where the behavior is in the form of stretching. On the other hand in folding points where the trajectory changes direction abruptly, we should use small neighborhood radius. We think using this method may improve local projection approach efficiency.

Reduced blood flow due to obstruction in most cases is as a primary factor in pressure ulcer formation and create of bedsore. The aim of this study is design and manufacture a care system tissue under pressure based on variation blood flow at different depths in the tissue. In manufacture of system two infrared light transmitter were located between of 5 and 10 mm receivers to measurement of blood flow at two different depths in the under pressure heel tissue. Also blood flow was evaluated in unload condition and after loading with 30 mmHg respectively 60.0 mmHg. 15 participates with mean age 50 were evaluated. 9 (60%) were men and 6 (40%) of them were women. Primary measurement results had shown different individual differences in variation blood flow tissue. To study signal amplitude changes significantly influenced by external pressure PPG, P-value was measured. There is significant changes in PPG signal amplitude during loading both pressure 30 and 60 mmHg. Development of this system would be possible with increases flexibility probe and using potent optical receiver and transmitter to access more depth