ENHANCED NOVEL EXUDATE METHOD OF INPAINTING FOR RETINAL VESSEL SEGMENTATION IN CELLULAR DOMAIN USING CELLULAR AUTOMATION

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Published: Aug 28, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i7.4484
Keywords:
vessel segmentation, inpainting, exudates, retina, cellular automation.

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Naina Singh
Punjab Technical University
Aarti .

Abstract

Precise vessel detection within retinal images is a vital and tedious task. Diagnosis of retinal images is actually more challenging in pathological images with all the latest presence of exudates along with other abnormalities. In this paper, the study indicates that segment exudates with an unsupervised approach which has come up with better outcomes but unfortunately it suffers from the adverse effect of the undesirable noise. So it can be further improved by using an adaptive form of Neighbourhood Estimator Before Filling filter (NEBF) in the Cellular domain using cellular automation. This paper mainly focuses on evaluating the effectiveness; designing and implementation of Adaptive NEBF filter based retina image segmentation technique in cellular domain

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Vol. 8 No. 7 (2017): July-August 2017
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Author Biography

Naina Singh, Punjab Technical University

Department of Computer Engineering & Technology M-Tech Student

References

Annunziata, Roberto, et al. "Leveraging Multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation." IEEE journal of biomedical and health informatics 20.4 (2016): 1129-1138.

Chalakkal, Renoh Johnson, and Waleed Abdulla. "Automatic segmentation of retinal vasculature." In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, pp. 886-890. IEEE, 2017.

Nisha, K. L., G. Sreelekha, Sathidevi Puthumangalathu Savithri, Poornima Mohanachandran, and Anand Vinekar. "Fusion of structure adaptive filtering and mathematical morphology for vessel segmentation in fundus images of infants with Retinopathy of Prematurity." In Electrical and Computer Engineering (CCECE), 2017 IEEE 30th Canadian Conference on, pp. 1-6. IEEE, 2017.

Tuba, Eva, Lazar Mrkela, and Milan Tuba. "Retinal blood vessel segmentation by support vector machine classification." In Radioelektronika (RADIOELEKTRONIKA), 2017 27th International Conference, pp. 1-6. IEEE, 2017.

Ngo, Lua, and Jae-Ho Han. "Advanced deep learning for blood vessel segmentation in retinal fundus images." In Brain-Computer Interface (BCI), 2017 5th International Winter Conference on, pp. 91-92. IEEE, 2017.

Wang, Xiaohong, and Xudong Jiang. "Enhancing retinal vessel segmentation by color fusion." In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, pp. 891-895. IEEE, 2017.

Bajceta, Milija, Petar Sekulić, Slobodan Djukanović, Tomo Popovic, and Vesna Popović-Bugarin. "Retinal blood vessels segmentation using ant colony optimization." In Neural Networks and Applications (NEUREL), 2016 13th Symposium on, pp. 1-6. IEEE, 2016.

Prakash, Tejaswi D., Deepthi Rajashekar, and Gowri Srinivasa. "Comparison of algorithms for segmentation of blood vessels in fundus images." In Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on, pp. 114-118. IEEE, 2016.

Roychowdhury, Sohini, Dara D. Koozekanani, and Keshab K. Parhi. "Iterative vessel segmentation of fundus images." IEEE Transactions on Biomedical Engineering 62, no. 7 (2015): 1738-1749.

Dizdaroglu, Bekir. "Retinal vasculature segmentation based on fast level set method." In Signal Processing and Communications Applications Conference (SIU), 2015 23th, pp. 855-858. IEEE, 2015.

Prasad, Deepthi K., L. Vibha, and K. R. Venugopal. "Early detection of diabetic retinopathy from digital retinal fundus images." In Intelligent Computational Systems (RAICS), 2015 IEEE Recent Advances in, pp. 240-245. IEEE, 2015.

Halder, Amiya, and Pritam Bhattacharya. "An application of Bottom Hat transformation to extract blood vessel from retinal images." In Communications and Signal Processing (ICCSP), 2015 International Conference on, pp. 1791-1795. IEEE, 2015.

Singh, Nagendra Pratap, Rajesh Kumar, and Rajeev Srivastava. "Local entropy thresholding based fast retinal vessels segmentation by modifying matched filter." In Computing, Communication & Automation (ICCCA), 2015 International Conference on, pp. 1166-1170. IEEE, 2015.