Abstract
Local Binary Patterns (LBP) is a widely used approach for medical image analysis. Limitations of the LBP operator are its sensitivity to noise and its boundedness to first derivative information. These limitations are usually balanced by extensions of the classical LBP operator (e.g. the Local Ternary Pattern operator (LTP) or the Extended LBP (ELBP) operator). In this paper we present a generic framework that is able to overcome this limitations by frequency filtering the images as pre-processing stage to the classical LBP. The advantage of this approach is its easier adaption and optimization to different application scenarios and data sets as compared to other LBP variants. Experiments are carried out employing two endoscopic data sets, the first from the duodenum used for diagnosis of celiac disease, the second from the colon used for polyp malignity assessment. It turned out that high pass filtering combined with LBP outperforms classical LBP and most of its extensions, whereas low pass filtering effects the results only to a small extent.
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References
Liedlgruber, M., Uhl, A.: Computer-aided decision support systems for endoscopy in the gastrointestinal tract: A review. IEEE Reviews in Biomedical Engineering (2011) (in press)
Häfner, M., Liedlgruber, M., Uhl, A., Vécsei, A., Wrba, F.: Color treatment in endoscopic image classification using multi-scale local color vector patterns. Medical Image Analysis 16(1), 75–86 (2012)
Vécsei, A., Amann, G., Hegenbart, S., Liedlgruber, M., Uhl, A.: Automated marsh-like classification of celiac disease in children using an optimized local texture operator. Computers in Biology and Medicine 41(6), 313–325 (2011)
Sousa, A., Dinis-Ribeiro, M., Areia, M., Coimbra, M.: Identifying cancer regions in vital-stained magnification endoscopy images using adapted color histograms. In: Proceedings of the 16th International Conference on Image Processing (ICIP 2009), Cairo, Egypt, pp. 681–684 (November 2009)
Li, B., Meng, M.Q.: Texture analysis for ulcer detection in capsule endoscopy images. Image and Vision Computing 27(9), 1336–1342 (2009)
Li, B., Meng, M.Q.H.: Small bowel tumor detection for wireless capsule endoscopy images using textural features and support vector machine. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA, pp. 498–503 (October 2009)
Li, B., Meng, M.Q.: Computer aided detection of bleeding regions for capsule endoscopy images. IEEE Transactions on Bio-Medical Engineering 56(4), 1032–1039 (2009)
Zhang, B.: Breast cancer diagnosis from biopsy images by serial fusion of random subspace ensembles. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), vol. 1, pp. 180–186 (October 2011)
Xu, Z., Guo, H., Chen, W.: Gastritis cold or heat image research based on lbp. In: 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), vol. 5, pp. 331–333 (August 2010)
Tan, X., Triggs, B.: Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007)
Wang, Y., Chun Mu, Z., Zeng, H.: Block-based and multi-resolution methods for ear recognition using wavelet transform and uniform local binary patterns, pp. 1–4 (December 2008)
Huang, X., Li, S., Wang, Y.: Shape localization based on statistical method using extended local binary pattern. In: Proceedings of the 3rd International Conference on Image and Graphics (ICIG 2004), Hong Kong, China, pp. 1–4 (2004)
Liu, X., You, X., Cheung, Y.: Texture image retrieval using non-separable wavelets and local binary patterns. In: International Conference on Computional Intelligence and Security, CIS 2009, pp. 287–291. IEEE Computer Society (2009)
Su, Y., Tao, D., Li, X., Gao, X.: Texture representation in aam using gabor wavelet and local binary patterns. In: SMC 2009: Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, pp. 3274–3279. IEEE Press, Piscataway (2009)
Butterworth, S.: On the theory of filter amplifiers. Wireless Engineer 7, 536–541 (1930)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Mäenpää, T.: The local binary pattern approach to texture analysis - extensions and applications. PhD thesis, University of Oulu (2003)
Hegenbart, S., Uhl, A., Vécsei, A.: Systematic Assessment of Performance Prediction Techniques in Medical Image Classification A Case Study on Celiac Disease. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 498–509. Springer, Heidelberg (2011)
McNemar, Q.: Note on the sampling error of the difference between correlated proportions of percentages. Psychometrika 12(2), 153–157 (1947)
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Hegenbart, S., Maimone, S., Uhl, A., Vécsei, A., Wimmer, G. (2013). Customised Frequency Pre-filtering in a Local Binary Pattern-Based Classification of Gastrointestinal Images. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2012. Lecture Notes in Computer Science, vol 7723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36678-9_10
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DOI: https://doi.org/10.1007/978-3-642-36678-9_10
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