Self-adaptive super-pixel FCM method for eyeground lint spot image segmentation

An image segmentation and fundus image technology, which is applied in image analysis, image enhancement, image data processing, etc., can solve the problems of delaying patients' optimal treatment time and high cost, achieve good solution set search performance, fast clustering convergence, and improve The effect of segmentation efficiency

Inactive Publication Date: 2021-06-04
NANTONG UNIVERSITY
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Problems solved by technology

Not only the cost of labor is high, but also the best time for patients to see a doctor will be delayed

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  • Self-adaptive super-pixel FCM method for eyeground lint spot image segmentation
  • Self-adaptive super-pixel FCM method for eyeground lint spot image segmentation
  • Self-adaptive super-pixel FCM method for eyeground lint spot image segmentation

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[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0059] This embodiment provides an adaptive superpixel FCM method for image segmentation of fundus lint spots, such as figure 1 As shown, it includes the following steps: S10. Input the fundus image data of a standard diabetic patient, and after preprocessing, artificially cut out the lesion area of ​​the lint spot lesion image in equal proportions, and obtain the marked lint spot lesion image. S20. After extracting the lesion area of ​​the lint spot lesion image, perform filter...

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Abstract

The invention provides a self-adaptive super-pixel FCM method for eyeground lint spot image segmentation. The method comprises the following steps: S10, artificially cutting a lesion area of a lint spot lesion image in equal proportion; s20, performing super-pixel processing on the cotton wool patch lesion image, and taking a super-pixel point as a self-adaptive FCM sample; s30, optimizing a self-adaptive FCM clustering center by using a derivative multi-population genetic algorithm; s40, calculating a per-pixel loss value and a callback parameter after clustering; and S50, forming a genetic FCM clustering model for eye fundus lint spot image segmentation, inputting an eye fundus image, and outputting a segmented eye fundus lint spot lesion area image. According to the self-adaptive super-pixel FCM method for eyeground lint spot image segmentation, the operation time is greatly shortened, the image segmentation precision is improved, and an important image feature basis is provided for clinical diagnosis and treatment of related diabetic retina lint spot lesion diseases.

Description

technical field [0001] The invention relates to the technical field of image processing and analysis, in particular to an adaptive superpixel FCM method for image segmentation of fundus lint spots. Background technique [0002] With the increase of diabetic patients in recent years, diabetic retinopathy (Diabetic Retinopathy, DR), the most serious eye complication of diabetes, has become the leading cause of blindness in adults. Today, my country is the world's largest diabetes country, and diabetes knowledge is poorly popularized, and the awareness rate, diagnosis rate, and control compliance rate are all low, which results in a high incidence of diabetes reticulum, and the diagnosis and treatment are lagging behind, causing serious harm to patients. had serious consequences. The World Health Organization announced that sugar reticulum is the main cause of visual impairment and blindness in the world. The risk of blindness in diabetic patients is 25 times that of non-diabet...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/136G06K9/62G06N3/12
CPCG06T7/11G06T7/136G06N3/126G06T2207/20004G06T2207/20081G06T2207/30041G06F18/23213
Inventor 丁卫平冯志豪李铭孙颖曹金鑫鞠恒荣黄嘉爽程纯秦廷帧沈鑫杰潘柏儒
Owner NANTONG UNIVERSITY
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