Examination on image segmentation method of ischemic optic neuropathy based on fuzzy clustering theory.

The purpose is to use medical image processing technology to avoid the influence of subjective factors through the mutual penetration and development of clinical medicine and computer science. Can diagnose the degree of malignancy of ischemic optic neuropathy as quickly as possible, and can take an effective treatment plan for the patient early.Therefore, image segmentation of ischemic optic neuropathy based on fuzzy clustering theory is particularly important for the diagnosis of disease in patients. This paper analyzes the research status of medical image segmentation at home and abroad and the development trend in this aspect in China. Discussed the fuzzy C-means clustering (FCM) image segmentation algorithm in depth, studied the effects of iterative cutoff error, initial clustering center, number of clustering categories and fuzzy weighted index on the practical application of the algorithm. At the same time, the traditional algorithm is not sensitive to the spatial information of the image, making the algorithm sensitive to noise. Firstly, introduced the spatial information of the image, and introduced the algorithm based on spatial information constraint, Based on the above description and based on the neighborhood properties described by the two-dimensional histogram, studied and proposed a relatively easy to understand multidimensional distance measurement method. That is, the two-dimensional pixel value and the neighborhood pixel value viewpoint that can be updated in the two-dimensional direction, by setting a clustering objective function, a clustering measurement method includes neighborhood information. Through the above two-dimensional image segmentation algorithm based on neighborhood spatial information, proposed an image segmentation algorithm for ischemic optic neuropathy of fuzzy kernel clustering theory combined with spatial information. The experimental results show that the proposed algorithm can show excellent results in ischemic neuropathy image segmentation, and the algorithm has faster convergence speed and higher classification accuracy. Experimental results of artificial images and actual images show that the algorithm has strong noise immunity and practicability. [ABSTRACT FROM AUTHOR]