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Applied Computational Intelligence and Soft Computing
Volume 2017 (2017), Article ID 9571262, 9 pages
Research Article

A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images

University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka Sector 16C, New Delhi 110078, India

Correspondence should be addressed to Varun Srivastava

Received 28 August 2017; Accepted 13 November 2017; Published 24 December 2017

Academic Editor: Francesco Carlo Morabito

Copyright © 2017 Varun Srivastava and Ravindra Kumar Purwar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a two-dimensional wavelet based decomposition algorithm for classification of biomedical images. The two-dimensional wavelet decomposition is done up to five levels for the input images. Histograms of decomposed images are then used to form the feature set. This feature set is further reduced using probabilistic principal component analysis. The reduced set of features is then fed into either nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. The proposed algorithm has been found better up to 3.3%, 12.75%, and 13.75% on average over the first, second, and third algorithm, respectively, using KNN and up to 6.22%, 13.9%, and 14.1% on average using ANN. The dataset used for comparison consisted of CT Scan images of lungs and MR images of heart as obtained from different sources.