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PIER PIER B PIER C PIER M PIER Letters
2013-02-06
Brain Mr Image Classification Using Multiscale Geometric Analysis of Ripplet
By
Sudeb Das,

    Mr. Sudeb Das

    Machine Intelligence Unit

    Indian Statistical Institute

    India

    Homepage

Manish Chowdhury,

    Dr. Manish Chowdhury

    Indian Statistical Institute

    India

    Homepage

Malay Kumar Kundu

    Dr. Malay Kumar Kundu

    Indian Statistical Institute

    India

    Homepage

Progress In Electromagnetics Research, Vol. 137, 1-17, 2013
doi:10.2528/PIER13010105
Abstract
We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an efficient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 × 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classification accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is efficient in brain MR image classification.
Citation
Sudeb Das, Manish Chowdhury, and Malay Kumar Kundu, "Brain Mr Image Classification Using Multiscale Geometric Analysis of Ripplet," Progress In Electromagnetics Research, Vol. 137, 1-17, 2013.
doi:10.2528/PIER13010105
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