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, 137, 1-17, 2013''. In this note, we analyze the reason and revise Figure 1.
2. Zhang, Y. and L. Wu, "An MR brain images classifier via principal component analysis and kernel support vector machine," Progress In Electromagnetics Research, Vol. 130, 369-388, 2012.
doi:10.2528/PIER12061410
3. Zhang, Y., S. Wang, and L. Wu, "A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO," Progress In Electromagnetics Research, Vol. 109, 325-343, 2010.
doi:10.2528/PIER10090105
4. Zhang, Y., et al., "Fruit classification using computer vision and feedforward neural network," Journal of Food Engineering, Vol. 143, 167-177, 2014.
doi:10.1016/j.jfoodeng.2014.07.001
5. Zhang, Y., et al., "A hybrid method for MRI brain image classification," Expert Systems with Applications, Vol. 38, No. 8, 10049-10053, 2011.
doi:10.1016/j.eswa.2011.02.012
Submit
