Ultrasound imaging is considered to be one of the most cost-effective and non-invasive techniques for conclusive diagnosis in some cases and preliminary diagnosis in others. Automatic liver tissue characterization and classification from ultrasonic scans have been for long, the concern of many researchers, and has been made possible today by the availability of the most powerful and cost effective computing facilities. Automatic diagnosis and classification systems are used both for quick and accurate diagnosis and as a second opinion tool for clarifications. This paper analyzes the effect of various linear, non linear and diffusion filters in improving the quality of the liver ultrasound images before proceeding to the subsequent phases of feature extraction and classification using Gray Level Run Length Matrix Features and Support Vector Machines respectively. © 2013 Springer-Verlag Berlin Heidelberg.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@694ad1aa ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@2f0b25e7 Through org.apache.xalan.xsltc.dom.DOMAdapter@4e578d2; Conference Code:95091
KaRaghesh Krishnan and Sudhakar, Rb, “Automatic classification of liver diseases from ultrasound images using GLRLM texture features”, Advances in Intelligent Systems and Computing, vol. 195 AISC, pp. 611-624, 2013.