Abstract
Diabetic retinopathy is one of the serious and sight-threatening complications of diabetics. The main symptom of diabetic retinopathy is the presence of exudates that results in yellow flecks due to the fluid that has seeped out of damaged capillaries. This causes the tissue in the retina to distend, resulting in hazy or unclear vision. If they are left untreated, diabetic retinopathy can cause blindness. Hence, segmentation of exudates is vital process in retinal pathologies. The proposed work involves accurate segmentation of exudates from the retinal fundus images. Initially, K-means clustering is applied on the retinal images to separate the exudates and optic disk. Genetic algorithm is used for the accurate segmentation of the exudates in which the fitness function is calculated to perform crossover between the segmented images obtained from the K-means clustering segmentation. Before performing the mutation process, the grayscale image is converted into the RGB channels. These three-segmented channels are further combined by the mutation process to obtain the genetic algorithm output. High-intensity region is determined to be the exudates and the low intensity is said to be the optic disk. The elimination of the optic disk which has the same intensity as that of the exudates is performed using watershed segmentation. Finally, the parameter validation is done after the morphological operations. This method was implemented in 10 images downloaded from CHASE and STARE database and the accuracy has been improved to 94% compared with the existing approaches.
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References
Antal B, Hajdu A (2012) An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng 59(6):1720–1726
Deshmukh AV, Patil TG, Patankar SS, Kulkarni JV (2015) Features based classification of hard exudates in retinal images. In: 2015 international conference on advances in computing, communications and informatics (ICACCI), pp 1652–1655
Pires R, Avila S, Jelinek HF, Wainer J, Valle E, Rocha A (2014) Automatic diabetic retinopathy detection using bossa nova representation. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 146–149
Roychowdhury S, Koozekanani DD, Parhi KK (2014) Dream: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inform 18(5):1717–1728
Esmaeili M, Rabbani H, Dehnavi AM, Dehghani A (2012) Automatic detection of exudates and optic disk in retinal images using curvelet transform. IET Image Proc 6(7):1005–1013
Aqeel, AF, Ganesan S (2014) Automated algorithm for retinal image exudates and Drusens detection, segmentation, and measurement. In: 2014 IEEE international conference on electro/information technology (EIT), pp 206–215
Pereira C, Gonçalves L, Ferreira M (2015) Exudate segmentation in fundus images using ant colony optimization approach. Inf Sci 296:14–24
Soares I, Castelo-Branco M, Pinheiro AM (2016) Optic disc localization in retinal images based on cumulative sum fields. IEEE J Biomed Health Inform 20(2):574–585
Yu H, Barriga ES, Agurto C, Echegaray S, Pattichis MS, Bauman W, Soliz P (2012) Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Trans Inf Technol Biomed 16(4):644–657
Radha R, Lakshman B (2013) Retinal image analysis using morphological process and clustering technique. Sig Image Process 4(6):55
Kusakunniran W, Wu Q, Ritthipravat P, Zhang J (2018) Hard exudates segmentation based on learned initial seeds and iterative graph cut. Comput Methods Programs Biomed 158:173–183
Esther JJJ, Sophia SG (2014) Detecting optic disc in digital fundus images using stochastic watershed transformation. IJREAT Int J Res Eng Adv Technol 2(1)
Vimala GAG, Mohideen SK (2013) Automatic detection of optic disk and exudate from retinal images using clustering algorithm. In: 2013 7th international conference on intelligent systems and control (ISCO), pp 280–284
Hole KR, Gulhane VS, Shellokar ND (2013) Application of genetic algorithm for image enhancement and segmentation. Int J Adv Res Comput Eng Technol (IJARCET) 2(4):1342
Jelinek HF, Pires R, Padilha R, Goldenstein S, Wainer J, Bossomaier T, Rocha A (2012) Data fusion for multi-lesion diabetic retinopathy detection. In: 2012 25th international symposium on computer-based medical systems (CBMS), pp 1–4
Wisaeng K, Hiransakolwong N, Pothiruk E (2015) Automatic detection of exudates in retinal images based on threshold moving average models. Biophysics 60(2):288–297
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Gayathri Devi, K., Dhivya, M., Preethi, S. (2019). Detection of Exudates and Removal of Optic Disk in Fundus Images Using Genetic Algorithm. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_129
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DOI: https://doi.org/10.1007/978-3-030-00665-5_129
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