Abstract
Endoscope is important for detecting gastric lesions. Computer aided analysis of endoscope images is helpful to improve the accuracy of endoscope tests. In this paper, Mean Shift-Gray Level Co-occurrence Matrix algorithm (MS-GLCM), an improved algorithm for computing Gray Level Co-occurrence Matrix (GLCM) based on Mean Shift, is presented to solve the problem that computing GLCM costs too much time. MS-GLCM is used in Color Wavelet Covariance(CWC) as a substitute for classical GLCM. The new CWC algorithm is applied to extract texture features, which are classified by AdaBoost, in endoscope images. Experiment shows that MS-GLCM saves the time cost and partly prevents from data redundancy, with a similar output like GLCM. And it decreases the final error rate in lesion detection of endoscope images.
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References
Rong, W., Wu, J.: The Research in Progress of Diagnosis of Early Gastric Cancer. Chinese Journal of Clinical Oncology and Rehabilitation 13, 469 (2006)
Jain, A.K., Karu, K.: Learning Texture Discrimination Masks. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 195–205 (1996)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics smc 3, 610–621 (1973)
Ulaby, F.T., Kouyate, F., Brisco, B., Williams, T.H.L.: Textural Information in Sar Images. IEEE Transactions on Geoscience and Remote Sensing GE-24, 235–245 (1986)
Baraldi, A., Parmiggiani, F.: An Investigation of the Textural Characteristics Associated with Gray Level Cooccurrence Matrix Statistical Parameters. IEEE Transactions on Geoscience and Remote Sensing 33, 293–304 (1995)
Kandaswamy, U., Adjeroh, D.A., Lee, M.C.: Efficient Texture Analysis of Sar Imagery. IEEE Transactions on Geoscience and Remote Sensing 43, 2075–2083 (2005)
Javier, A., Papa Neucimar, J., Silva Torres Alexandre, X.F.: Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images. EURASIP journal on advances in signal processing 2008, 18 (2008)
Liu, B., Cheng, H.D., Huang, J., Tian, J., Liu, J., Tang, X.: Automated Segmentation of Ultrasonic Breast Lesions Using Statistical Texture Classification and Active Contour Based on Probability Distance. Ultrasound in Medicine & Biology 35, 1309–1324 (2009)
El Naqa, I., Grigsby, P.W., Apte, A., Kidd, E., Donnelly, E., Khullar, D., Chaudhari, S., Yang, D., Schmitt, M., Laforest, R., Thorstad, W.L., Deasy, J.O.: Exploring Feature-Based Approaches in Pet Images for Predicting Cancer Treatment Outcomes. Pattern Recognition 42, 1162–1171 (2009)
Chen, C.-Y., Chiou, H.-J., Chou, S.-Y., Chiou, S.-Y., Wang, H.-K., Chou, Y.-H., Chiang, H.K.: Computer-Aided Diagnosis of Soft-Tissue Tumors Using Sonographic Morphologic and Texture Features. Academic Radiology 16, 1531–1538 (2009)
Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 790–799 (1995)
Comaniciu, D., Meer, P.: Robust Analysis of Feature Spaces: Color Image Segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA, pp. 750–755 (1997)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)
Iakovidis, D.K., Maroulis, D.E., Karkanis, S.A., Flaounas, I.N.: Color Texture Recognition in Video Sequences Using Wavelet Covariance Features and Support Vector Machines. In: Proceedings of 29th Euromicro Conference 2003, pp. 199–204 (2003)
Cacoullos, T.: Estimation of a Multivariate Density. Annals of the Institute of Statistical Mathematics 18, 179–189 (1966)
Siew, L.H., Hodgson, R.M., Wood, E.J.: Texture Measures for Carpet Wear Assessment. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 92–105 (1988)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. International Conference on Machine Learning, ICML 1996 (1996)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on pattern analysis and machine intelligence 24, 971–987 (2002)
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Wu, Y., Sun, K., Lin, X., Cheng, S., Zhang, S. (2010). An Mean Shift Based Gray Level Co-occurrence Matrix for Endoscope Image Diagnosis. In: Zhang, D., Sonka, M. (eds) Medical Biometrics. ICMB 2010. Lecture Notes in Computer Science, vol 6165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13923-9_43
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DOI: https://doi.org/10.1007/978-3-642-13923-9_43
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