Authors:
Irshad Khalil,Sami Ur Rahman,Samad Baseer,Adnan Khalil,Fakhre Alam,DOI NO:
https://doi.org/10.26782/jmcms.2020.09.00028Keywords:
Legendre Polynomials,Shifted Legendre Polynomials,Classification,MRI Images,Image Processing,Abstract
In this paper, we study the computational strategy for the implementation of orthogonal moments to two-dimensional images. Automatic and accurate classification of Magnetic Resonance Images is of importance for the interpretation and analysis of these images and for this purpose different techniques have been proposed. In this paper, we present Legendre Polynomial and two different classification-based methods for the classification of normal and abnormal MRI Images. In the first step, we apply Legendre polynomial to extract features from MRI images. In the second stage, two classifiers have been used which are employed to classify these images as normal and abnormal images. The proposed method was tested on tests with 75 images in which 15 images belong to the normal category images and the remaining 60 are abnormal images. The result derived from the confusion matrix test yielded a classification accuracy of 100.0% for these images.Refference:
Ban N Dhannoon and Loay E George, Color image compression using polynomial and quadtree coding techniques, International Journal of Scientific & Engineering Research 4 (2013), no. 11.
II. EA El-Dahshan, Abdel-Badeeh M Salem, and Tamer H Younis, A hybrid technique for automatic mri brain images classification, Studia Univ. Babes-Bolyai, Informatica 54 (2009), no. 1, 55–67.
III. Exact legendre moment computation for gray level images, Pattern Recognition 40 (2007), no. 12, 3597–3605.
IV. Florin Gorunescu, Data mining techniques in computer-aided diagnosis: Non-invasive cancer detection, Pwaset 25 (2007), 427–430.
V. Harris Drucker, Christopher JC Burges, Linda Kaufman, Alex J Smola, and Vladimir Vapnik, Support vector regression machines, Advances in neural information processing systems, 1997, pp. 155–161.
VI. Hashem Kalbkhani, Mahrokh G Shayesteh, and Behrooz Zali-Vargahan, Robust algorithm for brain magnetic resonance image (mri) classification based on garch variances series, Biomedical Signal Processing and Control 8 (2013), no. 6, 909–919.
VII. Irshad Khalil, Adnan Khalil, Sami Ur Rehman, Hammad Khalil, Rahmat Ali Khan, and Fakhre Alam, Classification of ecg signals using legendre moments, International Journal of Bioinformatics and Biomedical Engineering 1 (2015), no. 3, 284–291.
VIII. Khalid M Hosny, Efficient computation of legendre moments for gray level images, International Journal of Image and Graphics 7 (2007), no. 04, 735–747.
IX. Kemal Polat, Bayram Akdemir, and Salih Gu¨ne¸s, Computer-aided diagnosis of ecg data on the least square support vector machine, Digital Signal Processing 18 (2008), no. 1, 25–32.
X. K. Laxmi Narayanamma, R. V. Krishnaiah, P. Sammulal, An Efficient
Statistical Feature Selection Based Classification, J. Mech. Cont.& Math.
Sci.,Vol.-14, No.-4, JulyAugust (2019) , pp 27-40
XI. Michael Reed Teague, Image analysis via the general theory of moments, JOSA 70 (1980), no. 8, 920–930.
XII. Madhubanti Maitra and Amitava Chatterjee, Hybrid multiresolution slantlet transform and fuzzy c-means clustering approach for normal-pathological brain mr image segregation, Medical engineering & physics 30 (2008), no. 5, 615–623.
XIII. M. K. Kundum S. Das, M. Chowdhury, An mr brain images classifier via principal component analysis and kernel support vector machine, Progress in Electromagnetics Research 137 (2013), 1–17
XIV. Sandeep Chaplot, LM Patnaik, and NR Jagannathan, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network, Biomedical Signal Processing and Control 1 (2006), no. 1, 86–92.
XV. Xingxing Zhou, Shuihua Wang, Wei Xu, Genlin Ji, Preetha Phillips, Ping Sun, and Yudong Zhang, Detection of the pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier, International Conference on Bioinformatics and Biomedical Engineering, Springer, 2015, pp. 201–209.
XVI. Yudong Zhang, Zhengchao Dong, Lenan Wu, and Shuihua Wang, A hybrid method for MRI brain image classification, Expert Systems with Applications 38 (2011), no. 8, 10049–10053.
XVII.Vasanthselvakumar R, Balasubramanian M, Palanivel S, “Detection and
Classification of Kidney Disorders using Deep Learning Method”,
J. Mech.Cont.& Math. Sci.,Vol.-14,No.2, March-April (2019), pp 258-270.