Automatic Feature Extraction from Ocular Images
Author(s)
Ryszard S. Choraś*
Affiliation(s)
Institute of Telecommunications University of Technology & Life Sciences 85-796 Bydgoszcz, Poland.
Institute of Telecommunications University of Technology & Life Sciences 85-796 Bydgoszcz, Poland.
ABSTRACT
Ocular images processing is an important task in: i) biometrics system based on retina and/or sclera images, and ii) in clinical ophthalmology diagnosis of diseases like various vascular disorders. We presents a general framework for image processing of ocular images with a particular view on feature extraction. The method uses the set of geometrical and texture features and based on the information of the complex vessel structure of the retina and sclera. The feature extraction contains the image preprocessing, locating and segmentation of the region of interest (ROI). The image processing of ROI and the feature extraction are proceeded, and then the feature vector is determined for the human recognition and ophthalmology diagnosis.
Cite this paper
nullChoraś, R. (2012) Automatic Feature Extraction from Ocular Images. Open Journal of Applied Sciences, 2, 34-38. doi: 10.4236/ojapps.2012.24B009.
nullChoraś, R. (2012) Automatic Feature Extraction from Ocular Images. Open Journal of Applied Sciences, 2, 34-38. doi: 10.4236/ojapps.2012.24B009.
References
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[1] K.G. Goh, M.L. Lee, W. Hsu, H. Wang, ADRIS: An Automatic Diabetic Retinal Image Screening System, Medical Data Mining and Knowledge Discovery, Springer-Verlag, 2000. [2] W. Hsu, P.M.D.S. Pallawala, M.L. Lee, A.E. Kah-Guan, The Role of Domain Knowledge in the Detection of Retinal Hard Exudates, IEEE Computer Vision and Pattern Recognition, Hawaii, Dec 2001. [3] H. Li, O. Chutatape, Automated feature extraction in color retinal images by a model based approach, IEEE Trans. Biomed. Eng., 2004, vol. 51, pp. 246 -- 254. [4] N.M. Salem, A.K. Nandi, Novel and adaptive contribution of the red channel in pre-processing of colour fundus images, Journal of the Franklin Institute, 2007, p. 243--256. [5] C. Kirbas, K. Quek, Vessel extraction techniques and algorithm: a survey, Proceedings of the 3rd IEEE Symposium on BioInfomratics and Bioengineering (BIBE' 03), 2003. [6] S. Chang, D. Shim, Sub-pixel Retinal Vessel Tracking and Measurement Using Modified Canny Edge Detection Method, Journal of Imaging Science and Technology, March-April 2008 issue. [7] T. Chanwimaluang, G. Fan, An efficient algorithm for extraction of anatomical structures in retinal images, Proc. IEEE International Conference on Image Processing, pp. 1093--1096, (Barcelona, Spain. [8] H. Farzin, H. Abrishami-Moghaddam, M.S. Moin, A novel retinal identification system, EURASIP Journal on Advances in Signal Processing, vol. 2008, Article ID 280635, 2008. [9] R.S. Choras, Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems, International Journal of Biology and Biomedical Engineering, Issue 1, vol.1, pp.6--16, 2007. [10] R.S. Choras, Iris Recognition - M. Kurzynski, M.Wozniak (Eds.): Computer Recognition Systems 3, AISC 57, pp. 637--644, Springer-Verlag Berlin Heidelberg 2 [11] R.S. Choras, Iris-based person identification using Gabor wavelets and moments, Proceedings 2009 International Conference on Biometrics and Kansei Engineering ICBAKE 2009, pp. 55--59, CPS IEEE Computer Society, Los Alamitos, California, Washington, Tokyo [12] D. Gabor, "Theory of communication," J. Inst. Elect. Eng., 93, pp. 429--459, 1946