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The purpose of this study was to explore the neural mechanism in Chronic prostatitis/Chronic pelvic pain syndrome (CP/CPPS) using resting-state functional magnetic resonance imaging. The functional magnetic resonance imaging was performed on 31 male CP/CPPS-patients and 31 age and education matched male healthy controls on a 3-T magnetic resonance imaging unit. A two-sample t-test was adopted to reveal the regional homogeneity between the patients and healthy controls. The mean regional homogeneity values in the alerted brain regions of patients were correlated with the clinical measurements by using Pearson's correlation analyses. The CP/CPPS-patients had significantly decreased regional homogeneity in the bilateral anterior cingulate cortices, insular cortices and right medial prefrontal cortex, while significantly increased regional homogeneity in the brainstem and right thalamus compared with the healthy controls. In the CP/CPPS-patients, the mean regional homogeneity value in the left anterior cingulate cortex, bilateral insular cortices and brainstem were respectively correlated with the National Institutes of Health Chronic Prostatitis Symptom Index total score and pain subscale. These brain regions are important in the pain modulation process. Therefore, an impaired pain modulatory system, either by decreased descending pain inhibition or enhanced pain facilitation, may explain the pain symptoms in CP/CPPS. [ABSTRACT FROM AUTHOR]

Glioma detection and segmentation is a challenging task for radiologists and clinicians. The research reported in this paper seeks to develop a better clinical decision support algorithm for clinicians diagnosis. This paper presents a probabilistic method for detection and segmentation between abnormal tissue regions and brain tumour (tumour core and edema) portions from Magnetic Resonance Imaging (MRI). A framework is constructed to learn structure of undirected graphical models that can represent the spatial relationships among variables and apply it to glioma segmentation. Compared with the pixel of image, the superpixel is more consistent with human visual cognition and contains less redundancy, thus, the superpixels are considered as the basic unit of structure learning and glioma segmentation scheme. ℓ1-regularization techniques are applied to learn the appropriate structure for modeling graphical models. Conditional Random Fields (CRF) are used to model the spatial interactions among image superpixel regions and their measurements. A number of features including statistics features, the combined features from the local binary pattern as well as gray level run length, curve features, and fractal features were extracted from each superpixel. The features are then passed by ℓ1-regularization to ensure a robust classification. The proposed method is compared with support vector machine and Fuzzy c-means to classify each superpixel into normal and abnormal tissue. The proposed system is tested for the presence of low grade as well as high grade glioma tumors on images collected from BRATS2013, BRATS2015 data set and Henan Provincial People’s Hospital (HNPPH) data set. The experiments performed provides similarity between segmented and truth image up to 91.5% by correlation method. [ABSTRACT FROM AUTHOR]