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

Varun Srivastava,Ravindra Kumar Purwar

doi:10.1155/2017/9571262

Abstract

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 K 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.

Highlights

Biomedical images like Magnetic Resonance Imaging (MRI), Computed Tomography- (CT-) Scan, ultrasound images, and so forth have been recognized as a powerful tool for the detection of diseases in recent times
The results are obtained for two datasets, one of CT Scan images and another of MR Images
A multilevel wavelet transform based feature matrix has been proposed for classification of CT Scan images and MR images

Summary

Introduction

Biomedical images like Magnetic Resonance Imaging (MRI), Computed Tomography- (CT-) Scan, ultrasound images, and so forth have been recognized as a powerful tool for the detection of diseases in recent times. Authors in [11] proposed a scheme to identify pathological brain by using a simplified pulsecoupled neural network (SPCNN) for the region of interest (ROI) segmentation and fast discrete curvelet transform (FDCT) for feature extraction. Authors in [20] compared various artificial neural network based classifiers which can be used for classification and clustering in biomedical images. The various wavelet decomposition based approaches did not consider the feature set extracted by concatenating histograms of five different images obtained by wavelet decomposition of a biomedical image up to five levels.

Methodology

Experimental Results and Performance Analysis

Conclusion and Future Scope