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Appl. Sci. 2017, 7(2), 192; doi:10.3390/app7020192

Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology

1
College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
2
Engineering Experimental Class of National Pilot School, School of Precision Instrument & Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
3
Shanxi Eye Hospital, Taiyuan 030002, Shanxi, China
*
Author to whom correspondence should be addressed.
Received: 20 December 2016 / Accepted: 13 February 2017 / Published: 16 February 2017
(This article belongs to the Special Issue Smart Healthcare)
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Abstract

To overcome the limitation of artificial judgment of meibomian gland morphology, we proposed a solution based on an improved fuzzy c-means (FCM) algorithm and rough sets theory. The rough sets reduced the redundant attributes while ensuring classification accuracy, and greatly reduced the amount of computation to achieve information dimension compression and knowledge system simplification. However, before this reduction, data must be discretized, and this process causes some degree of information loss. Therefore, to maintain the integrity of the information, we used the improved FCM to make attributes fuzzy instead of discrete before continuing with attribute reduction, and thus, the implicit knowledge and decision rules were more accurate. Our algorithm overcame the defects of the traditional FCM algorithm, which is sensitive to outliers and easily falls into local optima. Our experimental results show that the proposed method improved recognition efficiency without degrading recognition accuracy, which was as high as 97.5%. Furthermore, the meibomian gland morphology was diagnosed efficiently, and thus this method can provide practical application values for the recognition of meibomian gland morphology. View Full-Text
Keywords: meibomian gland; fuzzy c-means; rough sets; attribute reduction; pattern recognition meibomian gland; fuzzy c-means; rough sets; attribute reduction; pattern recognition
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Liang, F.; Xu, Y.; Li, W.; Ning, X.; Liu, X.; Liu, A. Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology. Appl. Sci. 2017, 7, 192.

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