Publications Developed Under NEI R01 EY017065

This NEI Grant titled "Automated Screening for Diabetic Retinopathy by Content" was begun in September 2005. Publications being produced under this grant are listed below.

Tobin, K.W., Chaum, E., Govindasamy, V.P., Karnowski, T.P., “Detection of Anatomic Structures in Human Retinal Imagery,” Submitted to IEEE Transactions on Medical Imaging, February 2006.

Karnowski, T.P., Govindasamy, V.P., Tobin, K.W., Chaum, E., “Locating the Optic Nerve in Retinal Images: Comparing Model-Based and Bayesian Decision Methods,” 28th Annual International Conf. of the IEEE EMBS, August 2006.

Tobin, K.W., Chaum, E., Govindasamy, V.P., Karnowski, T.P., Sezer, O., “Characterization of the Optic Disk in Retinal Imagery using a Probabilistic Approach”, SPIE International Symposium on Medical Imaging, San Diego, California, Proceedings of SPIE, Vol. 6144, February 2006.

Publications and Issued Patents Relating to the Background of this Research

Tobin, K.W., Bhaduri, B.L., Bright, E.A., Cheriyadat, A., Karnowski, T.P., Palathingal, P.J., Potok, T.E., Price, J.R., “Automated Feature Generation in Large-Scale Geospatial Libraries for Content-Based Indexing,” Journal of Photogrammetric Engineering and Remote Sensing, Vol. 72, No. 5, May 2006.

Bingham, P.R., Price, J.R., Tobin, K.W., Karnowski, T.P., “Semiconductor Sidewall Shape Estimation,” SPIE Journal of Electronic Imaging, Vol. 13, No. 3, July 2004.

Tobin, K. W., Karnowski, T.P., Arrowood, L.F., Ferrell, R.K., Goddard, J.S., Lakhani, F., “Content-based Image Retrieval for Semiconductor Process Characterization”, EURASIP Journal on Applied Signal Processing, Vol. 2002, No. 7, 2002.

K.W. Tobin, T.P. Karnowski, R.K. Ferrell, “Method for Indexing and Retrieving Manufacturing-Specific Digital Imagery Based on Image Content”, ERID No. 0668 / Q&B 6321-131, U.S. Patent No. 6,751,343, June 15, 2004.

K.W. Tobin, T.P. Karnowski, R.K. Ferrell, “Method for Localizing and Isolating an Errant Process Step”, U.S. Patent No. 6,535,776, March 18, 2003.

Other Publications Related to Retinal Image Analysis

The literature is replete with research on the topic of retinal image analysis including vascular segmentation, optic nerve detection, lesion detection, and a variety of biomedical CBIR applications. The following is a short list of some of the literature we have been reviewing and referencing in our research (these are in more-or-less alphabetical order according to first author).

Aksoy, S., et al. Interactive classification and content-based retrieval of tissue images. in SPIE Annual Meeting, Applications of Digital Image Processing Session. 2002. Seattle, WA.

Brodley, C., et al. Content-Based Retrieval from Medical Image Databases: A Synergy of Human Interaction, Machine Learning and Computer Vision. in 10th International Conference on Artificial Intelligence. 1999. Orlando, FL: American Association for Artifcial Intelligence.

Bueno, J.M., et al. How to Add Content-based Image Retrieval Capability in a PACS. in The 15 th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002). 2002.

Cornforth, D.J., et al. Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy. in The 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems  2004. . 2004. Cairns, Australia.

Foracchia, M., E. Grisan, and A. Ruggeri, Detection of Optic Disc in Retinal Images by Means of a Geometrical Model of Vessel Structure. IEEE Transactions on Medical Imaging, 2004. 23(10): p. 1189-1195.

Foracchia, M., E. Grisan, and A. Ruggeri, Luminosity and contrast normalization in retinal images. Medical Image Analysis, 2005. 9(3): p. 179-190.

Foracchia, M., E. Grisan, and A. Ruggeri. Detection of Vessel Caliber Irregularities in Color Retinal Fundus Images by Means of Fine Tracking. in 2nd European Medical & Biological Engineering Conference. 2002. Vienna (Austria).

Fountain, S.R. and T.N. Tan, Efficient Rotation Invariant Texture Features for Content-Based Image Retrieval. Pattern Recognition, 1998. 31(11): p. 1725-1732.

Freeman, W.T. and E.H. Adelson, The Design and Use of Steerable Filters. IEEE Tranactions on Pattern Analysis and Machine Intelligence, 1991. 13(9): p. 891-906.

Grisan, E., et al. A new tracking system for the robust extraction of retinal vessel structure. in The 26th Annual International Conference of the IEEE EMBS 2004. San Francisco, CA, USA.

Güld, M.O., et al. A Generic Concept for the Implementation of Medical Image Retrieval Systems. in Connecting medical informatics and bio-informatics. Proceedings of MIE 2005. 2005. Amsterdam.

Gupta, A., et al. Content-based Retrieval of Ophthalmological Images. in IEEE International Conference on Image Processing. 1996.

Haralick, A.a., Feature normalization and likelihood-based similarity measurements for image retrieval. Pattern Recognition Letters, 2001: p. 563-582.

Hitzenberger, C.K., et al., Three-dimensional imaging of the human retina by high-speed optical coherence tomography. Oprics Express, 2003. 11(21): p. 2753-2761.

HongQing, Z. Segmentation of Blood Vessels in Retinal Images Using 2-D Entropies of Gray Level-Gradient Co-occurrence Matrix. in IEEE International Conference on Acoustics, Speech, and Signal Processing. 2004.

Hongqing, Z., S. Huazhong, and L. Limin. Blood Vessels Segmentation in Retina via Wavelet Transforms Using Steerable Filters. in 17th IEEE Symposium on Computer-Based Medical Systems, CBMS 2004. 2004.

Hoover, A. and M. Goldbaum, Locating the Optic Nerve in a Retinal Image Using the Fuzzy Convergence of the Blood Vessels. IEEE Transactions on Medical Imaging, 2003. 22(8): p. 951-958.

Hyvarinen, A. and E. Oja, Independent component analysis: algorithms and applications. Neural Networks, 2000. 13: p. 411-430.

Lalonde, M., M. Beaulieu, and L. Gagnon, Fast and Robust Optic Disc Detection Using Pyramidal Decomposition and Hausdorff-Based Template Matching. IEEE Transactions on Medical Imaging, 2001. 20(11): p. 1193-1200.

Le Bozec, C., Zapletal, E., Jaulent, M., Heudes, D., Degoulet, P. Towards content-based image retrieval in a HIS-integrated PACS. in Proceedings AIMA Symposium. 2000.

Leandro, J.J.G., R.M.C. JR, and H.F. Jelinek. Blood Vessels Segmentation in Retina: Preliminary Assessment of the Mathematical Morphology and of the Wavelet Transform Techniques. in XIV Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'01). 2001.

Leandro, J.J.G., et al. Blood Vessels Segmentation in Non-Mydriatic Images using Wavelets and Statistical Classifiers. in XVI Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI’03). 2003.

Lehmann, T.M., et al. The IRMA Project: A State of the Art Report on Content-Based Image Retrieval in Medical Applications. in 7th Korea-Germany Joint Workshop on Advanced Medical Image Processing. 2003. EWHA Womans University.

Lehmann, T.M., et al. Content-Based Image Retrieval in Medical Applications: A Novel Multi-Step Approach. 2000.

Lehmanna, T.M., et al., Automatic categorization of medical images for content-based retrieval and data mining. Computerized Medical Imaging and Graphics, 2005. 29: p. 143-155.

Lin, T. and Y. Zheng, Adaptive image enhancement for retinal blood vessel segmentation. Electronics Letters, 2002. 38(19): p. 1090-1091.

Lubbers, K., et al., A Probabilistic Approach to Medical Image Retrieval. Lecture Notes in Computer Science, 2005. 3491: p. 761-772.

Luo, S., X. Li, and G. Zhou. A simplified fuzzy connectedness method used for segmentation of vessel images. in The 25th Annual Intemational Conference of the IEEE EMBS. 2003. Cancun, Mexico.

Marchiori, A., et al. CBIR for Medical Images - An Evaluation Trial. in Content-Based Access of Image and Video Libraries 2001. (CBAIVL 2001). IEEE Workshop on. 2001: IEEE.

Martinez-Perez, M.E., Computer Analysis of the Geometry of the Retinal Vasculature, in Department of Biological and Medical Systems. 2000, University of London: London, England. p. 195.

Martinez-Perez, M.E., et al. Segmentation of Retinal Blood Vessels Based on the Second Directional Derivative and Region Growing. in IEEE International Conference on Image Processing. 1999.

Matsopoulos, G.K., et al., Multimodal Registration of Retinal Images Using Self Organizing Maps. IEEE Transactions on Medical Imaging, 2004. 23(12): p. 1557-1563.

Mehtre, B.M., M.S. Kankanhalli, and W.F. Lee, Shape Measures for Content Based Image Retrieval: A Comparison. Information Processing and Management, 1997. 33(3): p. 319-337.

Mojsilovic, A. and J. Gomes. Semantic Based Categorization, Browsing and Retrieval in Medical Image Databases. in 2002 International Conference on Image Processing. 2002.

Morris, D.T. and C. Donnison, Identifying the neuroretinal rim boundary using dynamic contours. Image and Vision Computing, 1999. 17: p. 169-174.

Muller, H., Rosset, Antoine;Valleea, Jean-Paul ; and Geissbuhler, Antoine. Comparing feature sets for content-based image retrieval in a medical case database. in SPIE Medical Imaging. 2004. San Diego, CA, USA.

Muller, H., Muller, Wolfgang, Squire, D. Marchand-Maillet, Pun, T., Performance Evaluation in Content-Based Image Retrieval: Overview and Proposals. Pattern Recognition Letters, 2001. 22: p. 593-601.

Muller, H., et al., A Review of Content-Based Image Retrieval Systems in Medical Applications - Clinical Benets and Future Directions. International Journal of Medical Informatics, 2004. 73(1): p. 1-23.

Muller, H., et al., A reference data set for the evaluation of medical image retrieval systems. Computerized Medical Imaging and Graphics, 2004.

Niemeijer, M., et al., Automatic Detection of Red Lesions in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging, 2005. 24(5): p. 584-592.

Pinz, A., et al., Mapping the Human Retina. IEEE Transactions on Medical Imaging, 1998. 17(4): p. 606-619.

Rapantzikos, K., M. Zervakis, and K. Balas, Detection and segmentation of drusen deposits on human retina: Potential in the diagnosis of age-related macular degeneration. Medical Image Analysis, 2003. 7: p. 95-108.

Sbeh, Z.B., et al., A New Approach of Geodesic Reconstruction for Drusen Segmentation in Eye Fundus Images. IEEE Tranactions on Medical Imaging, 2001. 20(12): p. 1321-1333.

Schultz, C.P., et al., Molecular Imaging Portal: New Development IT Platform for Imaging, Nonimaging and Genomics. Molecular Imaging, 2005. 4(4): p. 71-77.

Smeulders, A.W.M., et al., Content-Based Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(12): p. 1349-1380.

Staal, J., et al., Ridge-Based Vessel Segmentation in Color Images of the Retina. IEEE Transactions on Medical Imaging, 2004. 23(4): p. 501-509.

Traina Jr., C., A.J.M. Traina, and J.M. de Figueiredo. Including Conditional Operators in Content-Based Image Retrieval in Large Sets of Medical Exams. in 17th IEEE Symposium on Computer-Based Medical Systems (CBMS’04). 2004.

Vasconcelos, N. and A. Lippman. A Unifying View of Image Similarity. in 5th International Conference on Pattern Recognition. 2000.

Walter, T., et al., A Contribution of Image Processing to the Diagnosis of Diabetic Retinopathy—Detection of Exudates in Color Fundus Images of the Human Retina. IEEE Tranactions on Medical Imaging, 2002. 21(10): p. 1236-1243.

Wang, L. and A. Bhalerao, Model Based Segmentation for Retinal Fundus Images. Lecture Notes in Computer Science, 2003(2749): p. 422-429.

Wlkinson, M.H.I., et al. Blood Vessel Segmentation Using Moving-Window Robust Automatic Threshold Selection. in IEEE International Conference on Image Processing. 2003.

Zana, F. and J.-C. Klein, Segmentation of Vessel-Like Patterns Using Mathematical Morphology and Curvature Evaluation. IEEE Transactions on Medical Imaging, 2001. 10(7).

Zana, F. and J.-C. Klein. Robust Segmentation of Vessels from Retinal Angiography. in International Conference on Digital Signal Processing, DSP. 1997.