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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
Discrete Wavelet Transform(DWT), , Genetic Algorithm(GA), , Spatial Gray Level Dependence Method (SGLDM), Probabilistic Neural Network(PNN)., Receiver Operating Characteristic (ROC) analysis
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.
12 Samantaray, R. K., Panda, S. B., & Pradhan, B. Automated Brain Tumor Detection and Identification Using Image Processing.
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Clark M C., Hall L O., Goldgof D B., Velthuzien R., Murtagh F R., and Silbiger M S.,“Automatic tumor segmentation using knowledge based techniques”, IEEE Transactions on Medical Imaging, Vol 17,pp.187-192,1998.
Dubravko Cosic ,Sven Loncaric, “Rule based labeling of CT head image”; 6th conference on Artificial Intelligence in Medicine, Europe: Springer ; p.453 – 456, 1997 March 23-26.
Duncan J.S.,Ayache N, “Medical Image Analysis Progress Over two decade and challenges ahead”, IEEE Trans on PAMI ,Vol 22,pp. 85 – 106,2000.
Fausett L, “Fundamentals of Neural Networks: Architectures Algorithms and Applications”, Englewood cliffs, NJ: Prentice Hall International, p. 289-293,1994.
Frank Z. Brill., Donald E. Brown., and Worthy N. Martin, “Fast Genetic Selection of features for Neural Network Classifiers”, IEEE Trans. Neural Networks, Vol 3,pp. 324-328,1993.
Haddon J F, Boyce J F, “Co-occurrence Matrices for Image analysis”, IEE Electronic and Communications Engineering Journal , Vol 5,pp. 71 – 83,1993.
Haralick R M, Shanmugam K and Dinstein I, “Texture features for Image classification”,IEEE Transaction on System, Man, Cybernetics ,Vol 3,pp. 610 – 621,1973.
Kaiping Wei, Bin He, Tao Zhang, Xianjun Zhen, “A Novel Method for Segmentation of CT Head Images”, 1st International conference on Bioinformatics and Biomedical Engineering; Wuhan: IEEE Explore; p 717 – 720, 2007 July 6-8.
Loncaric S and Kova Cevic D, “A Method for segmentation of CT head images”, Lecture Notes on Computer Science ,Vol 1311,pp.1388 – 305,1997.
Matesn Milan, Loncaric Sven, Petravic Damir, “ Rule based approach to stroke lesion analysis from CT brain Images”, 2nd International Symposium on Image and Signal Processing and Analysis; Pula, Crotia : IEEE Explore ; p.219 – 2231, , 2001 June 19-21.
Nathalii Richards, Michael Dujata, Catherine Garbay, ”Distributed Markovian segmentation : Application to MR brain scans”, Journal of Pattern Recognition , Vol 40,pp. 3467 – 3478,2007.
Ruthmann,V.E, Jayce E.M, Reo D.E, Eckardit M.J, “Fully Automated segmentation of cerebrospinal fluid in computed tomography “, Psychiatry Research : Neuro Imaging , Vol 50 ,pp. 101 – 119,1993.
Tom Fawcett ,”An introduction to ROC analysis”, Pattern Recognition Letters , Vol 27,pp.861-874,2006.
Tong Hau Lee, Mohammad Faizal, Ahmad Fauzi and Ryoichi Komiya, “ Segmentation of CT brain images using unsupervised clusterings”, Journal of Visualization, Vol 12,pp.131-138,2009.
Tourassi G D, “Journey towards computer aided Diagnosis – Role of Image Texture Analysis”, Radiology , Vol 213,pp.317 – 320,1999.
Van G., Wouver P.,Scheunders and D.Van Dyck, “Statistical texture characterization from discrete wavelet representation”, IEEE Trans. Image processing,Vol 8,pp.592-598,1999.
Xiukun Yang., Finger print Smear Detection based on sub band Feature representation.Eurasip Journal on Advances in Signal processing , Vol 5,pp. 325-339,2011.
Zhang Y, Brady M, Smith S, “Segmentation of Brain MR images through Hidden Markov Random field Model and the expectation maximization algorithm”, IEEE Transactions on Medical Imaging , Vol 20,pp. 45-57,2001.
Mr. A.Padma
Thiyagarajar college of engg - India
Dr. R. Sukanesh
Thiyagarajar college of engg - India