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Gabriele Turci
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Computerized medical image segmentation is a challenging area because of poor resolution and weak contrast. The predominantly used conventional clustering techniques and the thresholding methods suffer from limitations owing to their heavy dependence on user interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The performance further deteriorates when the images are corrupted by noise, outliers and other artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering for segmenting vertebral body from magnetic resonance images. The motivation for this work is that spine appearance, shape and geometry measurements are necessary for abnormality detection and thus proper localisation and labelling will enhance the diagnostic output of a physician. The method is compared with Otsu thresholding and K-means clustering to illustrate the robustness. The reference standard for validation was the annotated images from the radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation.
Segmentation and Labelling of Human Spine MR Images Using Fuzzy Clustering
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Computerized medical image segmentation is a challenging area because of poor resolution and weak contrast. The predominantly used conventional clustering techniques and the thresholding methods suffer from limitations owing to their heavy dependence on user interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The performance further deteriorates when the images are corrupted by noise, outliers and other artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering for segmenting vertebral body from magnetic resonance images. The motivation for this work is that spine appearance, shape and geometry measurements are necessary for abnormality detection and thus proper localisation and labelling will enhance the diagnostic output of a physician. The method is compared with Otsu thresholding and K-means clustering to illustrate the robustness. The reference standard for validation was the annotated images from the radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation.
Segmentation and Labelling of Human Spine MR Images Using Fuzzy Clustering
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Web usage mining is the method of extracting interesting patterns from Web usage log file. Web usage mining is subfield of data mining uses various data mining techniques to produce association rules. Data mining techniques are used to generate association rules from transaction data. Most of the time transactions are boolean transactions, whereas Web usage data consists of quantitative values. To handle these real world quantitative data we used fuzzy data mining algorithm for extraction of association rules from quantitative Web log file. To generate fuzzy association rules first we designed membership function. This membership function is used to transform quantitative values into fuzzy terms. Experiments are carried out on different support and confidence. Experimental results show the performance of the algorithm with varied supports and confidence.
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The second presentation in the series called "Simply Politics". Political Ideologies - Left-Centre-Right is suitable for History and International Relations from Year 9 to university level. It contains the following: ideology, liberal, conservatives, socialist, fascists, ecologists and religious perspectives. Examples of countries, political ideas and ideologies.
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