Abstract
Automated and accurate classification of computed tomography (CT) images is an integral component of the analysis and interpretation of neuro imaging. In this paper, we present the wavelet-based statistical texture analysis method for the classification of brain tissues into normal, benign, malignant tumor of CT images. Comparative studies of texture analysis method are performed for the proposed texture analysis method and spatial gray level dependence matrix method (SGLDM). Our proposed system consists of five phases (i) image acquisition, (ii) discrete wavelet decomposition (DWT), (iii) feature extraction, (iv) feature selection, and (v) analysis of extracted texture features by classifier. A wavelet-based statistical texture feature set is derived from two level discrete wavelet transformed approximation (low frequency part of the image) sub image. Genetic algorithm (GA) and principal component analysis (PCA) are used to select the optimal texture features from the set of extracted features. The support vector machine (SVM) is employed as a classifier. The results of SVM for the texture analysis methods are evaluated using statistical analysis and receiver operating characteristic (ROC) analysis. The experimental results show that the proposed system is able to achieve higher classification accuracy effectiveness as measured by sensitivity and specificity.
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References
Andreas W, Hornik K (2000) Local PCA algorithms. IEEE Trans Neural Netw 11:1242–1250
Brill FZ, Brown DE, Martin WN (1992) Fast genetic selection of features for neural network classifiers. IEEE Trans Neural Netw 3:324–328
Chang T, Kuo C (1993) Texture analysis and classification with tree structured wavelet transform. IEEE Trans Image Process 2:429–441
Chang RF, Wu WJ, Moon WK, Chou YH, Chen DR (2003a) Support vector machines for diagnosis of breast tumors on US images. Acad Radiol 10:189–197
Chang RF, Wu WJ, Moon WK, Chou YH, Chen DR (2003b) Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound Med Biol 29:679–686
Chaplet S, Patnaik LM, Jaganathan NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1:86–92
Clark MC, Hall LO, Goldgof DB, Velthuzien R, Murtagh FR, Silbiger MS (1998) Automatic tumor segmentation using knowledge based techniques. IEEE Trans Med Imaging 17:187–192
Duncan JS, Ayache N (2000) Medical image analysis: progress over two decade and challenges ahead. IEEE Trans Pattern Analysis Mach Intelligence 22:85–106
Dunn C, Higgins WE (1995) Optimal Gabor filters for texture segmentation. IEEE Trans Image Process 4:947–964
El-Naqa I, Yang Y, Wernick MN, Galatsanos NP, Nishikawa RM (2002) A support vector machine approach for detection of micro calcifications. IEEE Trans Med Imaging 21:1552–1563
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–864
Free borough PA, Fox NC (1998) MR image texture analysis applied to the diagnosis and tracking of Alzheimer’s disease. IEEE Trans Med Imaging 17:475–479
Gering DT, Eric W, Grimson L, Kikins R (2002) Recognizing deviations from normalcy for brain tumor segmentation. LNCS 2488:388–395
Haddon JF, Boyce JF (1993) Co-occurrence matrices for image analysis. IEEE Electr Commun Eng J 5:71–83
Haralick RM, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans Syst Man Cybern 3:610–621
Liao YY, Tsui PH, Yeh CK (2009) Classification of benign and malignant breast tumors by ultrasound B-scan and Nakagami-based images. J Med Biol Eng 30:307–312
Reddick WE, Glass JO, Cook EN, Elkin TD, Deaton RJ (2009) Automated artificial segmentation and classification of multispectral magnetic resonance images of brain using neural networks. IEEE Trans Med Imaging 16:911–918
Schad LR, Bluml S, Zuna I (1993) MR tissue characterization of intracranial tumors by means of texture analysis. Magn Reson Imaging 11:889–896
Shubhangi DC, Hiremath PS (2009) Support vector machine (SVM) classifier for brain tumor detection. Int Conf Adv Comput Commun Control 12:444–448
Tong HL, Mohammed FA, Fauzi, Su-cheng Haw (2011) Automatic hemorrhage slices detection for CT brain images. LNCS 7066:268–279
Tourassi GD (1999) Journey towards computer aided diagnosis—role of image texture analysis. Radiology 213:317–320
Van deWouver G, Scheunders P, Van Dyck D (1999) Statistical texture characterization from discrete wavelet representation. IEEE Trans Image Process 8:592–598
Acknowledgments
The authors are grateful to Dr. S. Alagappan Chief Consultant and Radiologist, Devaki Scan Centre, Madurai for providing CT images and validation.
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Padma Nanthagopal, A., Sukanesh Rajamony, R. Automatic classification of brain computed tomography images using wavelet-based statistical texture features. J Vis 15, 363–372 (2012). https://doi.org/10.1007/s12650-012-0140-3
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DOI: https://doi.org/10.1007/s12650-012-0140-3