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A quantitative graduation system based on Grey Relational Analysis is proposed to recognize fatty livers in B-scan ultrasonic images. We evaluated ultrasonography liver images from 95 subjects having fatty livers (Grade I, II, III) and 45 normal subjects, as diagnosed by an expert radiologist. In practice, ultrasonographical findings of fatty liver are based on the brightness level of the liver in comparison to the renal parenchyma. The development of a non-invasive and accurate method would be of great clinical value as an alternative to diagnosing fatty liver based on the radiologist's visual perception. In this study, we also evaluated AST and ALT liver enzymes for fatty liver having different grades. A high correlation between enzymes and Grey Relational Grades were found. The Receiver Operating Characteristic (ROC) curves were obtained and yielded satisfactory classification results using sensitivity, specificity and area under the curve for computing graduation and distinguishing fatty livers from healthy livers. With the proposed method based on Grey Relational Analysis, not only misdiagnosis caused by subjective differences in clinical evaluation will be reduced, but also the early diagnosis fatty liver and quantitative assessment of its degree will be achieved.

It is known that patients with Attention Deficit and Hyperactivity disorder (ADHD) and Conduct disorder (CD) commonly shows greater symptom severity than those with ADHD alone and worse outcomes. This study researches whether Default mode network (DMN) is altered in adolescents with ADHD + CD, relative to ADHD alone and controls or not. Ten medication-naïve boys with ADHD + CD, ten medication-naïve boys with ADHD and 10-age-matched typically developing (TD) controls underwent functional magnetic resonance imaging (fMRI) scans in the resting state and neuropsychological tasks such as the Wisconsin Card Sorting Test (WCST), Stroop Test TBAG Form (STP), Auditory Verbal learning Test (AVLT), Visual Auditory Digit Span B (VADS B) were applied to all the subjects included. fMRI scans can be used only nine patients in each groups. The findings revealed group differences between cingulate cortex and primary mortor cortex; cingulate cortex and somatosensory association cortex; angular gyrus (AG) and dorsal posterior cingulate cortex, in these networks increased activity was observed in participants with ADHD + CD compared with the ADHD. We found that lower resting state (rs)-activity was observed between left AG and dorsal posterior cingulate cortex, whereas higher rs-activity connectivity were detected between right AG and somatosensory association cortex in ADHD relative to the ones with ADHD + CD. In neuropsyhcological tasks, ADHD + CD group showed poor performance in WISC-R, WCST, Stroop, AVLT tasks compared to TDs. The ADHD + CD group displayed rs-functional abnormalities in DMN. Our results suggest that abnormalities in the intrinsic activity of resting state networks may contribute to the etiology of CD and poor prognosis of ADHD + CD.

We investigated the association between the textural features obtained from F-FDG images, metabolic parameters (SUVmax SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

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