The tortuosity of corneal nerve fibres is correlated with a number of diseases such as diabetic neuropathy. The assessment of corneal nerve tortuosity in in vivo confocal microscopy images can inform the detection of diseases and complications. Clustering is a typical technique which is often used to discover data distribution. This paper proposes a dominant set based fuzzy clustering ensemble method based on cluster filtering. The proposed method uses similarity relationship to represent the connections among the clusters to form a graph and the dominant sets are extracted to form meta-clusters. Then, each meta-cluster is aggregated to obtain a cluster in the final clustering ensemble outputs. Experimental results on a public corneal nerve tortuosity data set demonstrates the effectiveness of the proposed method in IVCM image tortuosity grading.

Pan Su. Dominant Set Based Fuzzy Clustering Ensemble for Aggregation of Tortuosity Measures. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 5: 23-30. https://doi.org/10.25236/AJCIS.2020.030504.

[1] Yu Z, Zhu X, Wong H S, You J, Zhang J, Han G. Distribution-based cluster structure selection. IEEE Transactions on Cybernetics. 2017; 47(11): 3554–3567.
[2] Diao R, Chao F, Peng T, Snooke N, Shen Q. Feature selection inspired classifier ensemble reduction. IEEE Transactions on Cybernetics. 2014; 44(8): 1259–1268.
[3] Sevillano X, Al´ıas F, Socor´o J C. Positional and confidence voting-based consensus functions for fuzzy cluster ensembles. Fuzzy Sets and Systems. 2012; 193: 1–32, .
[4] Yu Z, Li L, Wong H S, You J, Han G, Gao Y, Yu G. Probabilistic cluster structure ensemble. Information Sciences. 2014; 267: 16–34.
[5] Yan D, Chen A, Jordan M I. Cluster forests. Computational Statistics & Data Analysis. 2013; 66: 178–192.
[6] Iam-On N, Boongoen T, Garrett S, Price C. A link-based approach to the cluster ensemble problem. Pattern Analysis and Machine Intelligence. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011; 33(12): 2396–2409.
[7] Pavan M, M Pelillo. Dominant sets and pairwise clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007; 29(1): 167–172.
[8] Annunziata R, Kheirkhah A, Aggarwal S, Hamrah P, Trucco E. A fully automated tortuosity quantification system with application to corneal nerve fibres in confocal microscopy images. Medical Image Analysis. 2016; 32: 216-232.
[9] Su P, Shang C, Chen T, Shen Q. Exploiting data reliability and fuzzy clustering for journal ranking. IEEE Transactions on Fuzzy Systems. 2017; 25(5): 1306– 1319.
[10] Oliveira-Soto L, Efron N. Morphology of corneal nerves using confocal microscopy. Cornea. 2001; 20(4): 374-84.
[11] Fabio S, Zheng Xiaodong, Yuichi O, Alfredo R. Automatic evaluation of corneal nerve tortuosity in images from in vivo confocal microscopy. Investigative Ophthalmology & Visual Science. 2011; 52(9): 6404.
[12] Zhao Y, Rada L, Chen K, Harding S P, Zheng Y. Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE transactions on medical imaging. 2015; 34(9): 1797–1807.
[13] Bezdek, C James. Pattern recognition with fuzzy objective function algorithms. 1981; 22(1171): 203–239.
[14] Lotmar W, Freiburghaus A, Bracher D. Measurement of vessel tortuosity on fundus photographs. Albrecht Von Graefes Archiv Fr Klinische Und Experimentelle Ophthalmologie. 1979; 211: 49-57.
[15] Hart W E, Goldbaum M H, Kube P, Nelson M. Measurement and classification of retinal vascular tortuosity. International journal of medical informatics. 1999; 53(2-3): 239-52.
[16] Annunziata R, Kheirkhah A, Aggarwal S, Cavalcanti B M, Hamrah P, Trucco E. Two-dimensional plane for multi-scale quantification of corneal subbasal nerve tortuosity. Investigative ophthalmology & visual science. 2016; 57: 1132-1139.
[17] Patasius D J M, Marozas V, Lukosevicius A. Evaluation of tortuosity of eye blood vessels using the integral of square of derivative of curvature. In Proceedings Eur. Med. Biol. Eng. Conf. (EMBEC05); 2005.
[18] Bullitt E, Gerig G, Pizer S M, Lin W, Aylward S R. Measuring tortuosity of the intracerebral vasculature from MRA images. IEEE Transactions on Medical Imaging. 2003; 22: 1163-1171.
[19] Scarpa F, Zheng X, Ohashi Y, Ruggeri A. Automatic evaluation of corneal nerve tortuosity in images from in vivo confocal microscopy. Investigative ophthalmology visual science. 2011; 52(9): 6404-6408.
[20] Bribiesca E. A measure of tortuosity based on chain coding. Pattern Recognition. 2013; 46(3): 716-724.