Article

An mean shift based gray level co-occurrence matrix for endoscope image diagnosis

Authors:

Su ZhangAuthors Info & Claims

ICMB'10: Proceedings of the Second international conference on Medical Biometrics

Pages 403 - 412

https://doi.org/10.1007/978-3-642-13923-9_43

Published: 28 June 2010 Publication History

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.

References

[1]

Rong, W., Wu, J.: The Research in Progress of Diagnosis of Early Gastric Cancer. Chinese Journal of Clinical Oncology and Rehabilitation 13, 469 (2006)

[2]

Jain, A.K., Karu, K.: Learning Texture Discrimination Masks. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 195-205 (1996)

Digital Library

[3]

Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics smc 3, 610-621 (1973)

[4]

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)

[5]

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)

[6]

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)

[7]

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)

[8]

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)

[9]

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)

Digital Library

[10]

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)

[11]

Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 790-799 (1995)

Digital Library

[12]

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)

Digital Library

[13]

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)

Digital Library

[14]

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)

Digital Library

[15]

Cacoullos, T.: Estimation of a Multivariate Density. Annals of the Institute of Statistical Mathematics 18, 179-189 (1966)

[16]

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)

Digital Library

[17]

Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. International Conference on Machine Learning, ICML 1996 (1996)

[18]

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)

Digital Library

Cited By

Sun KWu YLin XCheng SZhu YZhang S(2011)Mean shift-based lesion detection of gastroscopic imagesProceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering10.1007/978-3-642-31919-8_22(167-174)Online publication date: 23-Oct-2011
https://dl.acm.org/doi/10.1007/978-3-642-31919-8_22

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ICMB'10: Proceedings of the Second international conference on Medical Biometrics

June 2010

422 pages

ISBN:3642139221

Editors:
David Zhang
Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong,
,
Milan Sonka
Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA,

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 28 June 2010

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Cited By

Sun KWu YLin XCheng SZhu YZhang S(2011)Mean shift-based lesion detection of gastroscopic imagesProceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering10.1007/978-3-642-31919-8_22(167-174)Online publication date: 23-Oct-2011
https://dl.acm.org/doi/10.1007/978-3-642-31919-8_22

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