Abstract
Optic disc segmentation in fundus image is a significant phase in diagnosis of eye disease like diabetic retinopathy and glaucoma. Segmenting the portion of optic disc which is bright yellowish in color is called as optic disc segmentation. Automated optic disc segmentation is essential to diagnose the eye disease at the earliest stage to prevent the eye sight loss. Segmentation of optic disc can be performed using clustering techniques. In this work, hierarchical and partition-based clustering techniques are used to segment the optic disc. Five datasets namely DIARETDB1, CHASE DB, HRF DB, INSPIRE DB, and DRIONS DB are used to evaluate the clustering techniques. A comparative study was made based on the results using the performance parameters like accuracy, error rate, positive predicted value, precision, recall, false discovery rate, and F1 score. The results show that the hierarchical clustering technique proves to be better than partition-based clustering for the all considered datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Setiawan AW, Mengko TR, Santoso OS, Suksmono AB (2013) Color retinal image enhancement using CLAHE. In: ICT for Smart Society (ICISS), pp 1–3. IEEE
Basit A, Fraz MM (2015) Optic disc detection and boundary extraction in retinal images. Appl Opt 54:3440–3447. https://doi.org/10.1364/AO.54.003440
Kumar BV, Karpagam GR, Rekha NV (2015) Performance analysis of deterministic centroid initialization method for partitional algorithms in image block clustering. Indian J Sci Technol 8(S7):63–73
Kumar BV, Janani K, Priya NM (2017) a survey on automatic detection of hard exudates in diabetic retinopathy. In: IEEE International Conference on Inventive Systems and Control, JCT College of Engineering and Technology, Coimbatore, Tamil Nadu
Kumar BV, Sabareeswaran S, Madumitha G (2018) A decennary survey on artificial intelligence methods for image segmentation. In: International conference on advanced engineering optimization through intelligent techniques, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India
Makrehchi M (2016) Hierarchical agglomerative clustering using common neighbours similarity. In: IEEE/WIC/ACM International Conference on Web Intelligence (WI), Omaha, NE, pp 546–551. https://doi.org/10.1109/WI.2016.0093
Haleem MS, Han L, Van Hemert J, Li B (2013) Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review. Comput Med Imaging Graph 37:581–596. https://doi.org/10.1016/j.compmedimag.2013.09.005
Panda S, Sahu S, Jena P, Chattopadhyay S (2012) Comparing fuzzy-C means and K-means clustering techniques: a comprehensive study. In: Advances in Computer Science, Engineering & Applications, AISC, vol. 166, pp 451–460
Mendonca AM, Sousa A, Mendonca L, Campilho A (2013) Automatic localization of the optic disc by combining vascular and intensity information. Comput Med Imaging Graph 37:409–417. https://doi.org/10.1016/j.compmedimag.2013.04.004
Marin D, Gegundez Arias ME, Suero A, Bravo JM (2015) Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput Methods Programs Biomed 118:173–185. https://doi.org/10.1016/j.cmpb.2014.11.003
Morales S, Naranjo V, Angulo J, Alcañiz M (2013) Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Trans Med Imaging 32:786–796. https://doi.org/10.1109/TMI.2013.2238244
Hsiao H-K, Liu C-C, Yu C-Y, Kuo S-W, Yu S-S (2012) A novel optic disc detection scheme on retinal images. Expert Syst Appl 39:10600–10606. https://doi.org/10.1016/j.eswa.2012.02.157
Xue L-Y, Lin J-W, Cao X-R, Yu L (2018) Retinal blood vessel segmentation using saliency detection model and region optimization. J Algorithms Comput Technol 3–12. https://doi.org/10.1177/1748301817725315
Prakash J (2018) Enhanced mass vehicle surveillance system. J Res 4(3):5–9
Kauppi T, Kalesnykiene V, Kamarainen J, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kalviainen H, Pietila J (2007) The DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Proceedings of the British Machine Vision Conference 2007. https://doi.org/10.5244/c.21.15
Owen CG, Rudnicka AR, Mullen R, Barman SA, Monekosso D, Whincup PH, Ng J, Paterson C (2009) Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina(caiar) program. Invest Ophthalmic Vis Sci 50(5):2004–2010
Budai A, Bock R, Maier A, Hornegger J, Michelson G (2013) Robust vessel segmentation in fundus images. Int J Biomed Imaging 1–11. https://doi.org/10.1155/2013/154860
Niemeijer M, Xu X, Dumitrescu A, Gupta P, van Ginneken B, Folk J, Abramoff M (2011) Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs. In: IEEE Trans Med Imaging. [Epub ahead of print] PubMed PMID: 21690008
Carmona EJ, Rincon M, Garcia-Feijoo J, Martinez-de-la-Casa JM (2008) Identification of the optic nerve head with genetic algorithms. Artif Intell Med 43(3):243–259
Kumar BV, Karpagam GR, Zhao Y (2019) Evolutionary algorithm with memetic search capability for optic disc localization in retinal fundus images. Intell Data Anal Biomed Appl 191–207. https://doi.org/10.1016/b978-0-12-815553-0.00009-4
Almazroa A, Burman R, Raahemifar K, Lakshminarayanan V (2015) J Ophthalmol 2015:1–28. https://doi.org/10.1155/2015/180972
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prakash, J., Kumar, B.V. (2021). An Empirical Analysis of Hierarchical and Partition-Based Clustering Techniques in Optic Disc Segmentation. In: Sharma, H., Saraswat, M., Kumar, S., Bansal, J.C. (eds) Intelligent Learning for Computer Vision. CIS 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 61. Springer, Singapore. https://doi.org/10.1007/978-981-33-4582-9_7
Download citation
DOI: https://doi.org/10.1007/978-981-33-4582-9_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4581-2
Online ISBN: 978-981-33-4582-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)