# Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology
## 1 Introduction  
In recent years, a variety of new medical devices have been used to help doctors with clinical diagnosis while producing a large amount of image data. Manual interpretation of images relies too much on physician experience and is not very efficient. To this end, many scholars attempt to use computer-assisted processing of medical images. In the work by [PERSON] et al. [1], the support vector machine was used to recognize brain magnetic resonance imaging (MRI) images with a recognition rate up to 95.45%. In another study, overcoming the influence of fracture diversity and individual differences, a decision tree was used to achieve automatic X-detection [2]. The method by [PERSON] et al. [3] used fuzzy recognition to classify white blood cell images to solve the contradiction between real-time detection accuracy and speed. [PERSON] et al. [4] developed a new method for the filtering of X-ray digital images of chests based on multi-resolution and rough set. This paper attempts to use image recognition technology to assist doctors in interpreting meibomian gland images and thus improve diagnostic accuracy and efficiency.  
Meibomian gland dysfunction (MGD) is a very common eye disease [5]. In recent years, with the increase of electronic products, the incidence of MGD has increased dramatically, seriously affecting people's normal lives. Many experts have conducted numerous research studies on MGD in order to determine how to prevent the disease, offer timely diagnosis, and reduce the troubles caused bythe disease. However, the current recognition of meibomian gland morphology still relies on the experience of doctors. With the development of pattern recognition technologies and continuous improvement of clinical diagnostic requirements, it is necessary to develop an intelligent diagnosis system that can replace human experience with advanced science and technology.  
The rough sets theory was proposed in 1982 by [PERSON] (1982), a Polish mathematician, whose main idea was to improve the accuracy and correctness of data analysis through attribute reduction under the premise of keeping the same classification ability. However, continuous attributes must be discretized before using rough sets theory to do attribute reduction for information system, and this process results in some degree of information loss. The fuzzy rough sets theory, presented by French scholars [PERSON] and [PERSON] (1982), combines the advantages of fuzzy sets and rough sets, and extends precise sets to fuzzy sets, and fuzzy equivalence classes are determined by the membership function, thus avoiding information loss to a certain extent. Rough sets theory has been developed in theoretical research and applied research for more than thirty years. Currently, many scholars apply rough sets theory to industrial control R. D. P.  
same classification, and their relationship is an indiscernibility relation, which is equivalent to an equivalence relation on \(U\).  
The concept of indiscernibility relation is the cornerstone of the rough sets theory, which reveals the granular structure of domain knowledge. The concept assumes that some knowledge is in the domain, and uses attributes and attributes' values to describe the objects. If two objects have the same attributes and attributes' values, they have an indiscernibility relation. Mathematically, the indiscernibility relation of a set and the division of a set are equivalent concepts, one-to-one, and unique to each other. This concept means that objects in the domain can be described with different attributes' sets to express exactly the same facts.