Abstract
Dry eye is an increasingly common disease in modern society which affects a wide range of population and has a negative impact on their daily activities, such as working with computers or driving. It can be diagnosed through an automatic clinical test for tear film lipid layer classification based on color and texture analysis. Up to now, researchers have mainly focused on the improvement of the image analysis step. However, there is still large room for improvement on the machine learning side. This paper presents a methodology to optimize this problem by means of class binarization, feature selection, and classification. The methodology can be used as a baseline in other classification problems to provide several solutions and evaluate their performance using a set of representative metrics and decision-making methods. When several decision-making methods are used, they may offer disagreeing rankings that will be solved by conflict handling in which rankings are merged into a single one. The experimental results prove the effectiveness of the proposed methodology in this domain. Also, its general purpose allows to adapt it to other classification problems in different fields such as medicine and biology.
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References
Wolff E (1954) Anatomy of the eye and orbit, 4th edn. H. K. Lewis and Co., London
Nichols KK, Nichols JJ, Mitchell GL (2004) The lack of association between signs and symptoms in patients with dry eye disease. Cornea 23(8):762–770
Rolando M, Refojo MF, Kenyon KR (1983) Increased tear evaporation in eyes with keratoconjunctivitis sicca. Arch Ophthalmol 101:557–558
Lemp MA (1998) Epidemiology and classification of dry eye. Adv Exp Med Biol 438:791–803
Guillon J (1998) Non-invasive tearscope plus routine for contact lens fitting . Contact Lens Anterior Eye 21(1):S31–S40
King-Smith P, Finkd B, Fogt N (1999) Three interferometric methods for measuring the thickness of layers of the tear film. Optom Vis Sci 76:19–32
Goto E, Yagi Y, Kaido M, Matsumoto Y, Konomi K, Tsubota K (2003) Improved functional visual acuity after punctal occlusion in dry eye patients. Am J Ophthalmol 135(5):704–705
Ramos L, Penas M, Remeseiro B, Mosquera A, Barreira N, Yebra-Pimentel E (2011) Texture and color analysis for the automatic classification of the eye lipid layer. In: LNCS: advances in computational interlligence (international work conference on artificial neural networks, IWANN’11), vol 6692. pp 66–73
Remeseiro B, Penas M, Barreira N, Mosquera A, Novo J, García-Resúa C (2013) Automatic classification of the interferential tear film lipid layer using colour texture analysis. Comput Methods Programs Biomed 111:93–103
Remeseiro B, Penas M, Mosquera A, Novo J, Penedo MG, Yebra-Pimentel E (2012) Statistical comparison of classifiers applied to the interferential tear film lipid layer automatic classification. Comput Math Methods Med 1–10:2012
Remeseiro B, Boló-Canedo V, Peteiro-Barral D, Alonso-Betanzos A, Guijarro-Berdinas B, Mosquera A, Penedo MG, Sánchez-Marono N (2014) A methodology for improving tear film lipid layer classification. IEEE J Biomed Health Inform 18(4):1485–1493
Rebeca Méndez, Remeseiro B, Peteiro-Barral D, Penedo MG (2014) Evaluation of class binarization and feature selection in tear film classification using topsis. CCIS: agents and artificial intelligence. Rev Sel Pap ICAART 2013 449:179–193
Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1–2):1–39
Wei G-W (2010) Extension of TOPSIS method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information. Knowl Inf Syst 25(3):623–634
Laplante A (2009) Using the analytical hierarchy process in selecting commercial real-time operating systems. Int J Inf Technol Decis Mak 8(01):151–168
Peng Yi, Wang G, Wang H (2012) User preferences-based software defect detection algorithms selection using MCDM. Inf Sci 191:3–13
Wu D, Boyer KL, Nichols JJ, King-Smith PE (2010) Texture-based prelens tear film segmentation in interferometry images. Mach Vis Appl 21(3):253–259
Ramos L, Barreira N, Mosquera A, Penedo MG, Yebra-Pimentel E, García-Resúa C (2014) Analysis of parameters for the automatic computation of the tear film break-up time test based on CCLRU standards. Comput Methods Programs Biomed 113(3):715–724
Carpente A, Ramos L, Barreira N, Penedo MG, Pena-Verdeal H and Giráldez MJ (2014) On the automation of the tear film non-invasive break-up test. In: 2nd International symposium on computer-based medical systems (CBMS), pp 185–188
Guillon J, Guillon M (1997) Tearscope Plus Clinical Handbook and Tearscope Plus Instructions Keeler Ltd, Windsor, Berkshire, Keeler Inc, Broomall, PA
Calvo D, Mosquera A, Penas M, García-Resúa C, Remeseiro B (2010) Color texture analysis for tear film classification: a preliminary study. LNCS Int Conf Image Anal Recognit (ICIAR) 6112:388–397
McLaren K (1976) The development of the CIE 1976 (L*a*b) uniform colour-space and colour-difference formula. J Soc Dyers Colour 92(9):338–341
Bradski G (2000) The OpenCV Library. Dr. Dobb’s J 25(11):120–126
Haralick Robert M, Shanmugam K, Its’Hak Dinstein (1973) Texture features for image classification. IEEE Trans Syst Man Cybernet Syst Man Cybernet 3:610–621
VOPTICAL_I1, VARPA optical dataset acquired and annotated by optometrists from the Optometry Service of the University of Santiago de Compostela (Spain), 2012. http://www.varpa.es/voptical_I1.html. Accessed Apr 2016
Furnkranz J (2003) Round robin ensembles. Intell Data Anal 7(5):385–403
Dietterich TG and Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. Arxiv preprint arXiv:9501101
Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 2:263–286
Allwein EL, Schapire RE, Singer Y (2001) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1:113–141
Guyon I, Gunn S, Nikravesh M, Zadeh L (2006) Feature extraction: foundations and applications. Springer, New York
Manning Christopher D, Prabhakar R, Hinrich S (2008) Introduction to information retrieval, vol 1. Cambridge University Press, Cambridge
Loughrey J, Cunningham P (2005) Overfitting in wrapper-based feature subset selection: the harder you try the worse it gets. Res Dev Intell Syst XXI:33–43
Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2011) On the behavior of feature selection methods dealing with noise and relevance over synthetic scenarios. In: The 2011 international joint conference on neural networks (IJCNN), pp 1530–1537
Hall MA (1999) Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato
Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1–2):155–176
Zhao Z, Liu H (2007) Searching for interacting features. In: Proceedings of the 20th international joint conference on Artifical intelligence. pp 1156–1161
Mitchell T (1997) Machine learning. McGraw-Hill
Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268
Friedman JH (1989) Regularized discriminant analysis. J Am Stat Asso 84(405):165–175
Jensen F (1996) An introduction to bayesian networks. Springer, New York
Murthy SK (1998) Automatic construction of decision trees from data a multi-disciplinary survey. Data Min Knowl Discov 2:345–389
Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):1–47
Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65:386–408
Fernandez Caballero JC, Martínez FJ, Hervás C, Gutiérrez PA (2010) Sensitivity versus accuracy in multiclass problems using memetic pareto evolutionary neural networks. IEEE Trans Neural Netw 21(5):750–770
Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications, vol 13. Springer, New York
Opricovic S, Tzeng GH (2004) Compromise solution by mcdm methods: a comparative analysis of vikor and topsis. Eur J Oper Res 156(2):445–455
Kuo Y, Yang T, Huang GW (2008) The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Ind Eng 55(1):80–93
Opricovic S (1998) Multicriteria optimization of civil engineering systems. Fac Civil Eng Belgrade 2(1):5–21
Peng Yi, Kou Gang, Wang Guoxun, Shi Yong (2011) FAMCDM: a fusion approach of MCDM methods to rank multiclass classification algorithms. Omega 39(6):677–689
Gautheir TD (2001) Detecting trends using spearman’s rank correlation coefficient. Environ Forensics 2(4):359–362
Acknowledgments
This research has been partially funded by the Secretaría de Estado de Investigación of the Spanish Government and FEDER funds of the European Union through the research projects TIN2012-37954 and PI14/02161; and by the Consellería de Industria of the Xunta de Galicia through the research projects GPC2013/065 and GRC2014/035. We would also like to thank the Optometry Service of the University of Santiago de Compostela (Spain) for providing us with the annotated dataset.
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Peteiro-Barral, D., Remeseiro, B., Méndez, R. et al. Evaluation of an automatic dry eye test using MCDM methods and rank correlation. Med Biol Eng Comput 55, 527–536 (2017). https://doi.org/10.1007/s11517-016-1534-5
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DOI: https://doi.org/10.1007/s11517-016-1534-5