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Retinal Vasculature Extraction using Wavelets


Affiliations
1 Computer Science and Engineering, RCC Institute of Information Technology, Kolkata - 700015, West Bengal, India
 

Retinal vasculature extraction helps in diagnosing the early detection of diabetic retinopathy to prevent blindness. In this work vasculature structure extraction is proposed which is based on vessel detection method. Retinal normal and abnormal images are first preprocessed to enhance the vessel information. Two methods i.e. wavelet transforms along with multiscale hessian-eigenvalue approach are considered for vessel extraction. Results are promising and it shows that the method performs well in extracting the vascular pattern. The comparative studies with manual based segmentation prove the effectiveness of our proposed method.

Keywords

Blood Vessel Extraction, Diabetic Retinopathy, Discrete Wavelet Transform, Hessian Matrix.
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  • Masooomi R, Ahmadifard A, Mohtadizadeh A. Retinal vessel segmentation using non-sub-sampled contourlet transform and multi-scale line detection. IEEE Iranian Conference on Intelligent Systems (ICIS); 2014. p. 1–5.

  • Usman Akram M, Ali Atzaz S, Aneeque F, Khan Shoab A. Blood vessel enhancement and segmentation using wavelet transform. IEEE International Conference on Digital Image Processing; 2009.

  • Ceylan M, Yasar H. Blood vessel extraction from retinal images using complex wavelet transform and complex-valued artificial neural network. IEEE 36th International Conference on Telecommunications and Signal Processing (TSP); 2013. p. 822–5.

  • Bankhead P, Scholfield CN, McGeown JG, Curtis TM. Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS One. 2012; 7(3):e32435.

  • Sengar N, Dutta MK, Parthasarthi M, Burget R. Extraction of retinal vasculature by using morphology in fundus images. IEEE 2nd International Conference on Signal Processing and Integrated Networks (SPIN); 2015. p. 139–42.

  • Kurilova V, Pavlovicova J, Oravec M, Rakar R, Marcek I. Retinal blood vessels extraction using morphological operations. IEEE International Conference on Systems, Signals and Image Processing (IWSSIP); 2015. p. 265–8.

  • Frangi A, Niessen W, Vincken K, Viergever M. Multiscale vessel enhancement filtering. Medical Image Computing and Computer- Assisted Interventation (MICCAI-98); 1998. p. 130–7.

  • Salem NM, Nandi A. Segmentation of retinal blood vessels using scalespace features and k-nearest neighbour classifier. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2006 May. p. II–II.

  • Liu X, Zeng Z, Wang X. Vessel segmentation in retinal images with a multiple kernel learning based method. IEEE International Joint Conference on Neural Networks (IJCNN); Beijing, China. 2014 Jul 611.

  • Li Y, Gong H, Wu W, Liu G, Chen G. An automated method using hessian matrix and random walks for retinal blood vessel segmentation. IEEE 8th International Congress on Image and Signal Processing; 2015. p. 423–7.

  • Annunziata R, Garzelli A, Ballerini L, Mecocci A, Trucco E. Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE Journal of Biomedical and Health Informatics. 2015.


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  • Retinal Vasculature Extraction using Wavelets

Abstract Views: 829  |  PDF Views: 362

Authors

Sanjukta Mishra
Computer Science and Engineering, RCC Institute of Information Technology, Kolkata - 700015, West Bengal, India
Minakshi Banerjee
Computer Science and Engineering, RCC Institute of Information Technology, Kolkata - 700015, West Bengal, India

Abstract


Retinal vasculature extraction helps in diagnosing the early detection of diabetic retinopathy to prevent blindness. In this work vasculature structure extraction is proposed which is based on vessel detection method. Retinal normal and abnormal images are first preprocessed to enhance the vessel information. Two methods i.e. wavelet transforms along with multiscale hessian-eigenvalue approach are considered for vessel extraction. Results are promising and it shows that the method performs well in extracting the vascular pattern. The comparative studies with manual based segmentation prove the effectiveness of our proposed method.

Keywords


Blood Vessel Extraction, Diabetic Retinopathy, Discrete Wavelet Transform, Hessian Matrix.

References