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Focal and diffused liver disease classification from ultrasound images based on isocontour segmentation

Publication Type : Journal Article

Publisher : IET Image Processing

Source : IET Image Processing, Institution of Engineering and Technology, Volume 9, Number 4, p.261-270 (2015)

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Keywords : Automatic classification, Classification accuracy, Computer graphics, Diagnosis, Fractal feature, Fractals, Graphics algorithms, image classification, Image segmentation, Liver disease, Neural networks, Ultrasonics, Ultrasound images, Ultrasound scanning

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2015

Abstract : Preliminary diagnosis based on ultrasound scanning is the first step in the treatment of many abdominal diseases. The noisy nature of the ultrasound image coupled with minimal contrasting features complicates the task of automatic classification if not impossible. This study presents a segmentation-based approach to automatic classification of ten types of diffused and focal liver diseases from ultrasound images. A novel approach using Isocontour Segmentation based on Marching Squares, a computer graphics algorithm is presented. GLCM and fractal features are extracted from the segmented ultrasound images and classified using support vector machines and artificial neural networks (ANN) and the results are analysed. An overall classification accuracy of 92% is achieved using fractal features and ANN. © The Institution of Engineering and Technology 2015.

Cite this Research Publication : K. Raghesh Krishnan and Radhakrishnan, S., “Focal and diffused liver disease classification from ultrasound images based on isocontour segmentation”, IET Image Processing, vol. 9, pp. 261-270, 2015.

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