Open AccessLiver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments
Author(s) -
Aravinda H.L,
M.V Sudhamani
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.6.11269
Subject(s) - autocorrelation , segmentation , pattern recognition (psychology) , cirrhosis , legendre polynomials , mathematics , artificial intelligence , cluster analysis , classifier (uml) , computer science , medicine , statistics , mathematical analysis
The major reasons for liver carcinoma are cirrhosis and hepatitis. In order to identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.