Qualification: 
M.Tech
Email: 
k_raghesh@cb.amrita.edu

Raghesh Krishnan K. currently serves as Assistant Professor in the department of Computer science, Amrita School of Engineering, Coimbatore Campus. His areas of research include Image Processing and Biomedical Image Classification. .

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

K. Raghesh Krishnan and Radhakrishnan, S., “Hybrid approach to classification of focal and diffused liver disorders using ultrasound images with wavelets and texture features”, IET Image Processing, vol. 11, pp. 530-538, 2017.[Abstract]


This study presents a computer-based approach to classify ten different kinds of focal and diffused liver disorders using ultrasound images. The diseased portion is isolated from the ultrasound image by applying active contour segmentation technique. The segmented region is further decomposed into horizontal, vertical and diagonal component images by applying biorthogonal wavelet transform. From the above wavelet filtered component images, grey level run-length matrix features are extracted and classified using random forests by applying ten-fold cross-validation strategy. The results are compared with spatial feature extraction techniques such as intensity histogram, invariant moment features and spatial texture features such as grey-level co-occurrence matrices, grey-level run length matrices and fractal texture features. The proposed technique, which is an application of texture feature extraction on transform domain images, gives an overall classification accuracy of 91% for a combination of ten classes of similar looking diseases which is appreciable than the spatial domain only techniques for liver disease classification from ultrasound images. © The Institution of Engineering and Technology.

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2015

Journal Article

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.[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.

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