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The Glowworm Swarm Optimization for Training the Radial Basis Function Network in Ultrasonic Supraspinatus Image Classification

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This article proposes a study on applying the glowworm swarm optimization for training the radial basis function network for classifying the different supraspinatus disease groups that are normal, tendon inflammation, calcific tendonitis and tendon tears of the ultrasound supraspinatus images. In conventional diagnosis, the physicians observe the micro/macro structures of images to judge the severity of rotator cuff disease; however, it is not reliable because the accuracy of visual observation depends on the expertise of physicians. Four texture analysis methods—gray-level co-occurrence matrix, texture spectrum, fractal dimension and texture feature coding method—are used to extract features of tissue characteristic of supraspinatus. The F-score measurement are used to select powerful features that are generated from the four texture analysis methods for comparison in the training stage, meanwhile, the proposed trained radial basis function network is used to discriminate test images into one of the four disease groups in the classification stage. The percentage of correct classification was more than 95.0%, and experimental results showed that the proposed method performs very well for the classification of ultrasonic supraspinatus images.
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Document Type: Research Article

Publication date: September 1, 2013

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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