Similarity Based Retrieval in Case Based Reasoning for Analysis of Medical Images
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:69422", author = "M. Das Gupta and S. Banerjee", title = "Similarity Based Retrieval in Case Based Reasoning for Analysis of Medical Images", 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.", volume = "8", number = "3", pages = "539-7", }
