International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 6, Issue 7 (July 2019), Pages: 89-98

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 Original Research Paper

 Title: Enhanced feature extraction technique for brain MRI classification based on Haar wavelet and statistical moments

 Author(s): Zahid Ullah 1, Su-Hyun Lee 1, *, Muhammad Fayaz 2

 Affiliation(s):

 1Department of Computer Engineering, Changwon National University, Changwon, South Korea
 2Department of Computer Engineering, Jeju National University, Jeju, South Korea

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-6966-1569

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.07.012

 Abstract:

Many methods have been proposed to classify the MR brain images automatically. We have proposed a method based on a Neural Network (NN) to classify the normality and abnormality of a given MR brain image. This method first employs a median filter to minimize the noise from the image and converted the image to RGB. Then applies the technique of Discrete Wavelet Transform (DWT) to extract the important features from the image and color moments have been employed in the feature reduction stage to reduce the dimension of the features. The reduced features are sent to Feed-Forward Artificial neural network (FF-ANN) to discriminate the normal and abnormal MR brain images. We applied this proposed method on 70 images (45 normal, 25 abnormal). The accuracy of the proposed method of both training and testing images are 95.48%, while the computation time for feature extraction, feature reduction, and neural network classifier is 4.3216s, 4.5056s, and 1.4797s, respectively. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: MRI classification, Discrete wavelet transform, Color moments, Principal component analysis, Feature extraction, Approximation component, Artificial neural network

 Article History: Received 14 January 2019, Received in revised form 20 May 2019, Accepted 25 May 2019

 Acknowledgement:

No Acknowledgement.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Ullah Z, Lee SH, and Fayaz M (2019). Enhanced feature extraction technique for brain MRI classification based on Haar wavelet and statistical moments. International Journal of Advanced and Applied Sciences, 6(7): 89-98

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 Fig. 13 Fig. 14 

 Tables

 Table 1 Table 2

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