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    A biomedical system based on fuzzy discrete hidden Markov model for the diagnosis of the brain diseases
    (PERGAMON-ELSEVIER SCIENCE LTD, 2008) Uguz, Harun; Oeztuerk, Ali; Saracoglu, Ridvan; Arslan, Ahmet
    Because it is a non-invasive, easy to apply and reliable technique, transcranial doppler (TCD) study of the adult intracerebral circulation has increased enormously in the last 10 years. In this study, a biomedical system has been implemented in order to classify the TCD signals recorded from the temporal region of the brain of 82 patients as well as of 24 healthy people. The diseases were investigated cerebral aneurysm, brain hemorrhage, cerebral oedema and brain tumor. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, the linear predictive coding analysis and cepstral analysis were applied in order to extract the cepstral and delta-cepstral coefficients in frame level as feature vectors. In the classification stage, discrete hidden Markov model (DHMM) based methods were used. In order to avoid loosing information due to vector quantization and to increase the classification performance, a fuzzy approach based similarity was applied to implement the DHMM. The performance of the proposed Fuzzy DHMM (FDHMM) was compared with some methods such as DHMM, artificial neural network (ANN), neuro-fuzzy approaches and obtained better classification performance than these methods. (C) 2007 Elsevier Ltd. All rights reserved.
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    Texture segmentation using fractal dimension and second order statistics
    (IEEE, 2007) Oeztuerk, Ali; Arslan, Ahmet
    In this study, segmentation of textured images using four different textural features is examined. The first three features are fractal dimension (FD) of the original image, contrast-stretched image and top-hat transformed image, respectively. Contrast-stretching and top-hat transform are known as detail enhancement techniques in the presence of shading or poor illumination, thus it is assumed that the hidden structures in textures will be apparent after these transformations. The fourth feature, e.g. entropy, is one of the parameters estimated from spatial gray level co-occurence matrix statistics. For comparison purposes, two different feature smoothing methods are applied to the feature space before running k-ortalama clustering. The median smoothing gives more accurate segmentation results than EPNSQ (Edge Preserving Noise Smoothing Quadrant) approach. The experimental results are obtained by applying the proposed method on various natural texture mosaics. For mosaics of four textures the average segmetation accuracies are %96.8 and %96 for median smoothing and EPNSQ approach, respectively. The average segmentation accuracy for five textured mosaics is %95.5 with median smoothing, while it is %89 with EPNSQ approach. The experiments carried out with median smoothing for six and nine textured images give the segmentation accuracies as %94 and %92, while they are %84 and %87 with EPNSQ approach.