Segmentation method of oct image layer based on random forest and compound activity curve

Abstract

The invention relates to an OCT image layer segmentation method based on random forest and compound activity curve, which is designed for accurate segmentation of retinal layer and effusion. The OCT image layer segmentation method based on random forest and compound activity curve of the present invention, comprises: obtains OCT image feature training random forest classifier; Composite activity curve algorithm obtains final SF1; Extract 24 features of OCT image, use random forest classifier , the OCT image layer segmentation method based on random forest and compound activity curve in the present invention has simple operation and accurate detection result. It overcomes the problems of low recognition rate and poor segmentation effect of existing OCT image segmentation algorithms for lesions.

Description

technical field

[0001] The invention belongs to the field of medical image processing algorithms, in particular to an OCT image layer segmentation method based on random forests and compound activity curves. Background technique

[0002] Central serous retinopathy is a severe and complex retinal disease that can easily lead to blindness. Central serous retinopathy mainly manifests as the accumulation of subretinal serous fluid in the staggered area, which may also lead to retinal pigment epithelial detachment. In addition to serous accumulation, retinal pigment epithelial detachment may occur subserously or near the center of the macula. This fluid causes swelling of the retinal layers, which may suddenly change in thickness and optical strength. Thus, central serous retinopathy is a common disorder of the macula, which is responsible for central vision. Therefore, the quantitative analysis of central serous retinopathy has very important significance in the research of r...

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