An MR image classification scheme based on fourier moment analysis and linear support vector machine

Abstract

The classification of tomography images into non-healthy or healthy is a key pre-clinical state for patients. The training and feature extraction comprise the most time and memory consuming processes in a classifier. The aim of this paper is to provide a moment based scheme to improve the operation of classifying a given brain image as normal or abnormal. The moment features are used as input vectors to the SVM algorithm. The image is analyzed in orthogonal Fourier basis, prior to the feature extraction method. Image representation by a subset of coefficients facilitates image component selectivity, leading to extra robustness and accuracy. The proposed method has been applied on both training and classification procedures. To demonstrate the potential of the technique presented in this paper in comparison with other state-of-the-art methods, numerical simulations were performed.

Subject Classification:

• 94A08
• 68U10

Keywords:

• Computer-aided diagnosis
• Image moments
• Fourier coefficients
• Support vector machine