Computer-aided diagnostic detection system of venous beading in retinal images

The detection of venous beading in retinal images provides an early sign of diabetic retinopathy and plays an important role as a preprocessing step in diagnosing ocular diseases. We present a computer-aided diagnostic system to automatically detect venous bead- ing of blood vessels. It comprises of two modules, referred to as the blood vessel extraction module (BVEM) and the venus beading detection module (VBDM). The former uses a bell-shaped Gaussian kernel with 12 azimuths to extract blood vessels while the latter applies a neural network-based shape cognitron to detect venous beading among the extracted blood vessels for diagnosis. Both modules are fully computer- automated. To evaluate the proposed system, 61 retinal images (32 beaded and 29 normal images) are used for performance evaluation. © 2000 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(00)01305-2) Subject terms: bell-shaped Gaussian matched filter; blood vessel extraction mod- ule; detection; extraction; neural network; retinal images; shape cognitron; venus beading detection module.

[1]  K. Akita,et al.  Pattern Recognition Of Blood Vessel Networks In Ocular Fundus Images , 1982, Other Conferences.

[2]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[3]  Chein-I Chang,et al.  A relative entropy-based approach to image thresholding , 1994, Pattern Recognit..

[4]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[5]  Shinichi Tamura,et al.  Zero-crossing interval correction in tracing eye-fundus blood vessels , 1988, Pattern Recognit..

[6]  P. Gregson,et al.  Automated grading of venous beading. , 1995, Computers and biomedical research, an international journal.

[7]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[8]  A. V. Cideciyan,et al.  Registration of ocular fundus images: an algorithm using cross-correlation of triple invariant image descriptors , 1995 .

[9]  P. Nagin,et al.  Measurement of fluorescein angiograms of the optic disc and retina using computerized image analysis. , 1985, Ophthalmology.

[10]  K Fukushima,et al.  Handwritten alphanumeric character recognition by the neocognitron , 1991, IEEE Trans. Neural Networks.

[11]  Robert M. Haralick,et al.  Morphologic edge detection , 1987, IEEE J. Robotics Autom..

[12]  Chein-I Chang,et al.  Implementation of a 3-D model for neocognitron , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[13]  E. Heller.  An international journal. , 1968, Canadian Medical Association journal.