Similarity Based Retrieval in Case Based Reasoning for Analysis of Medical Images

Authors: M. Das Gupta, S. Banerjee

Abstract:

Content Based Image Retrieval (CBIR) coupled with Case Based Reasoning (CBR) is a paradigm that is becoming increasingly popular in the diagnosis and therapy planning of medical ailments utilizing the digital content of medical images. This paper presents a survey of some of the promising approaches used in the detection of abnormalities in retina images as well in mammographic screening and detection of regions of interest in MRI scans of the brain. We also describe our proposed algorithm to detect hard exudates in fundus images of the retina of Diabetic Retinopathy patients.

Keywords: Case based reasoning, Exudates, Retina image, Similarity based retrieval.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1100022

References:

[1] A. Aamodt, “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” AI Communications, vol. 7, no. 1, pp. 39–59, March 1994.
[2] A. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Contentbased image retrieval at the end of the early years,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no.12, pp.1349– 1380, December 2000.
[3] L. G. Shapiro , I. Atmosukarto , H. Cho , H. J. Lin , S. Ruiz-Correa, and J. Yuen, “Similarity-Based Retrieval for Biomedical Applications”, in Case Based Reasoning in Images and Signals, by Petra Perner, Springer- Verlag, Berlin, Heidelberg, Studies in Computational Intelligence, Vol. 73 (2008)
[4] Gw´enol´e Quellec, Mathieu Lamard, Lynda Bekri, Guy Cazuguel, B´eatrice Cochener, Christian Roux, “Multimedia medical case retrieval using decision trees”, 29th Annual International Conference of the IEEE EMBS , Lyon, France, August 23-26, 2007.
[5] M. Lamard, W. Daccache, G. Cazuguel, C. Roux, and B. Cochener, “Use of jpeg-2000 wavelet compression scheme for content-based ophtalmologic retinal retrieval,” in Proceedings of the 27th annual international conference of IEEE engineering in medecine and biology society, september 2005.
[6] J. Bezdek, “Fuzzy mathemathics in pattern classification,” Ph.D. dissertation, Applied Math. Center, Cornell University, Ithaca, 1973.
[7] J. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
[8] L. Breiman, J. Friedman, R. Olshen, , and C. Stone, “Classication and regression trees,” Wadsworth, Belmont, Ca., 1984.
[9] Gw´enol´e Quellec, Mathieu Lamard, Guy Cazuguel, Member, IEEE, Christian Roux, Fellow Member, IEEE and B´eatrice Cochener, “Case Retrieval in Medical Databases by Fusing Heterogeneous Information”, IEEE Transaction on medical imaging 2010, pp.1-11.
[10] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
[11] A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-based image retrieval at the end of the early years,” IEEE Trans Pattern Anal Mach Intell, vol. 22, no. 12, pp. 1349–1380, 2000.
[12] S. L. Lauritzen and D. J. Spiegelhalter, “Local computations with probabilities on graphical structures and their application to expert systems,” J R Stat Soc, vol. 50, no. 2, pp. 157–224, 1988.
[13] F. Smarandache and J. Dezert, Advances and Applications of DSmT for Information Fusion I. American Research Press Rehoboth, 2004, http://fs.gallup.unm.edu/DSmT-book1.pdf.
[14] G. Shafer, A Mathematical Theory of Evidence. Princeton University Press, 1976.
[15] M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer, “The digital database for screening mammography,” in Proceedings of the Fifth International Workshop on Digital Mammography, Toronto, Canada, 2000, pp. 212–218.
[16] Ashraf Elsayed, Mohd Hanafi Ahmad Hijazi, Frans Coenen, Marta Garcia-Finana, Vanessa Sluming, Yalin Zheng, “Image Categorisation Using Time Series Case Based Reasoning”, Internatinal conference on case based reasoning (ICCBR 2011), London, UK, Springer 2011, pp. 423-436.
[17] Bagnall, A., Janacek, G.: Clustering Time Series with Clipped Data. Machine Learning, vol. 58, pp. 151-17 (2005)
[18] Zuiderveld, K.: Contrast Limited Adaptive Histogram Equalization. Academic Press Graphics Gems Series, pp. 474-485 (2001)
[19] Felzenszwalb, P., Huttenlocher, D.: E_cient Graph-based Image Segmentation. Int. journal of Computer Vision, vol. 59(2), pp. 167-181 (2004)
[20] Akara Sopharak,Bunyarit Uyyanonvara 1 and Sarah Barman, “Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering”, Sensors 2009, pp.2148-2161.
[21] B. Ramasubramanian, G. Prabhakar, “An Early Screening System for the detection of Diabetic Retinopathy using image processing”, International Journal of Computer Applications (0975 – 8887) Volume 61– No.15, January 2013.