A NOTE ON DAS'S PCA IN ONLINE PHASES

Abstract

PCA was effective and helpful in developing a classification system. However, it was inappropriate to perform two independent PCA models on ground truth images and query image, which was described in Figure 1 in reference “Brain MR image classification using multiscale geometric analysis of ripplet,” Progress In Electromagnetics Research, Vol. 137, 1–17, 2013. In this note, we analyze the reason and revise Figure 1. In an interesting and useful paper [1] in the journal Progress In Electromagnetics Research, Das, Chowdhury, and Kundu proposed an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. They used Ripplet transform Type-I (RT) to extract features, then the decomposed low frequency subband (LFS) was sent to principal component analysis (PCA) for feature reduction, and least square support vector machine (LS-SVM) for classification. The proposed system consisted of two phases: an offline phase and an online phase. The results over k-fold cross validation were excellent and superior to existing methods. However, they did not clearly describe the implementation of PCA in the online phase. Hence, this note is to remind readers some key issues of PCA in offline and online phases. Figure 1. Flowchart of Das’s MR image classification [1]. Received 21 January 2015, Accepted 17 February 2015, Scheduled 23 February 2015 * Corresponding author: Yudong Zhang (zhangyudong@njnu.edu.cn). 1 School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China. 2 Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.