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A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Optimal Statistical Texture Features
A.Padma, R. Sukanesh
Pages - 552 - 563     |    Revised - 01-11-2011     |    Published - 15-12-2011
Volume - 5   Issue - 5    |    Publication Date - November / December 2011  Table of Contents
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KEYWORDS
Discrete Wavelet Transform(DWT), , Genetic Algorithm(GA), , Spatial Gray Level Dependence Method (SGLDM), Probabilistic Neural Network(PNN)., Receiver Operating Characteristic (ROC) analysis
ABSTRACT
This paper presents an automated segmentation of brain tumors in computed tomography images (CT) using combination of Wavelet Statistical Texture features (WST) obtained from 2-level Discrete Wavelet Transformed (DWT) low and high frequency sub bands and Wavelet Co-occurrence Texture features (WCT) obtained from two level Discrete Wavelet Transformed (DWT) high frequency sub bands. In the proposed method, the wavelet based optimal texture features that distinguish between the brain tissue, benign tumor and malignant tumor tissue is found. Comparative studies of texture analysis is performed for the proposed combined wavelet based texture analysis method and Spatial Gray Level Dependence Method (SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection (iv) Classification and evaluation. The combined Wavelet Statistical Texture feature set (WST) and Wavelet Co-occurrence Texture feature (WCT) sets are derived from normal and tumor regions. Feature selection is performed by Genetic Algorithm (GA). These optimal features are used to segment the tumor. An Probabilistic Neural Network (PNN) classifier is employed to evaluate the performance of these features and by comparing the classification results of the PNN classifier with the Feed Forward Neural Network classifier(FFNN).The results of the Probabilistic Neural Network, FFNN classifiers for the texture analysis methods are evaluated using Receiver Operating Characteristic (ROC) analysis. The performance of the algorithm is evaluated on a series of brain tumor images. The results illustrate that the proposed method outperforms the existing methods.
CITED BY (12)  
1 Neethu, S., & Venkataraman, D. (2015). Stroke Detection in Brain Using CT Images. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (pp. 379-386). Springer India.
2 El-Khamy, S. E., Sadek, R. A., & El-Khoreby, M. A. (2015, October). An efficient brain mass detection with adaptive clustered based fuzzy C-mean and thresholding. In 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 429-433). IEEE.
3 Karthik, R., Menaka, R., & Chellamuthu, C. (2015). A comprehensive framework for classification of brain tumour images using SVM and curvelet transform. International Journal of Biomedical Engineering and Technology, 17(2), 168-177.
4 Ma, J., Xu, C., Dai, M., You, F., Shi, X., Dong, X., & Fu, F. (2014). Exploratory study on the methodology of fast imaging of unilateral stroke lesions by electrical impedance asymmetry in human heads. The Scientific World Journal, 2014.
5 Jawahar, M., Chandra Babu, N. K., & Vani, K. (2014, December). Leather texture classification using wavelet feature extraction technique. In Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on (pp. 1-4). IEEE.
6 Kyaw, M. M. (2013). Pre-segmentation for the computer aided diagnosis system. Int. J. Comput. Sci. Inf. Technol, 5(1).
7 Nanthagopal, A. P., & Sukanesh, R. (2013). Wavelet statistical texture features-based segmentation and classification of brain computed tomography images. IET image processing, 7(1), 25-32.
8 Ramya, V., & Rameshkumar, G. P. An Efficient MR Image Brain Tumor Segmentation Based on Discrete Wavelet Transform and Region Growing Algorithm.
9 Tacadena, J. D. (2013). Lung Nodule Detector and Classifier Tool (Doctoral dissertation).
10 Dahab, D. A., Ghoniemy, S. S., & Selim, G. M. (2012). Automated Brain Tumor Detection and Identification Using Image Processing and Probabilistic Neural Network Techniques. International journal of image processing and visual communication, 1(2), 1-8.
11 Nanthagopal, A. P., & Rajamony, R. S. (2012). A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier. Journal of medical engineering & technology, 36(5), 271-277.
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Mr. A.Padma
Thiyagarajar college of engg - India
giri_padma2000@yahoo.com
Dr. R. Sukanesh
Thiyagarajar college of engg - India


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