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Retinal vessel segmentation method of fundus image based on classification and regression tree and AdaBoost

A technology of retinal blood vessels and classification and regression trees, applied in image analysis, image enhancement, image data processing, etc., can solve the problems of uneven background fundus images, low accuracy, poor fundus image effects, etc.

Active Publication Date: 2015-07-29
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The learning-based retinal vessel segmentation method is the most accurate method among all methods, but the existing methods are not effective for fundus images with very uneven background, especially fundus images with lesions, and the accuracy is not high.

Method used

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  • Retinal vessel segmentation method of fundus image based on classification and regression tree and AdaBoost
  • Retinal vessel segmentation method of fundus image based on classification and regression tree and AdaBoost
  • Retinal vessel segmentation method of fundus image based on classification and regression tree and AdaBoost

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Embodiment 1

[0073] According to the method described in this article, the figure 2 Figure a is segmented, and the resulting manual marking and segmentation results are shown in Figure b and Figure c, respectively, and the obtained ROC curve is shown in Figure d; from figure 2 We can see the segmentation results, and the ROC curve of the method in this paper (the area between the curve and the X coordinate axis can evaluate the pros and cons of the segmentation algorithm, the larger the area, the better), from the area between the curve and the x axis AZ=0.9838 , it can be seen that the segmentation method in this paper is accurate and credible, and the accuracy reaches 0.9658, the sensitivity reaches 0.8358 and the specificity reaches 0.9820, which better proves that the segmentation method in this paper is accurate and credible.

Embodiment 2

[0075] According to the method described in this article, the image 3 Figure a is segmented, and the resulting manual marking and segmentation results are shown in Figure b and Figure c, respectively, and the obtained ROC curve is shown in Figure d; from image 3 We can see the segmentation results, and the ROC curve of the method in this paper (the area between the curve and the X coordinate axis can evaluate the pros and cons of the segmentation algorithm, the larger the area, the better), from the area between the curve and the x axis AZ=0.9802 , it can be seen that the segmentation method in this paper is accurate and credible, and the accuracy reaches 0.9711, the sensitivity reaches 0.7578 and the specificity reaches 0.9914, which better proves that the segmentation method in this paper is accurate and credible.

Embodiment 3

[0077] According to the method described in this article, the Figure 4 Figure a is segmented, and the resulting manual marking and segmentation results are shown in Figure b and Figure c, respectively, and the obtained ROC curve is shown in Figure d; from Figure 4 We can see the segmentation results, and the ROC curve of the method in this paper (the area between the curve and the X coordinate axis can evaluate the pros and cons of the segmentation algorithm, the larger the area, the better), from the area between the curve and the x axis AZ=0.9514 , it can be seen that the segmentation method in this paper is accurate and credible, and the accuracy reaches 0.9658, the sensitivity reaches 0.7011 and the specificity reaches 0.9747, which better proves that the segmentation method in this paper is accurate and credible.

[0078] Depend on Figure 2-Figure 4 The data shows that the area between the ROC curve and the x-axis is above 0.9500, the accuracy is above 0.9500, the spe...

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Abstract

The invention discloses a retinal vessel segmentation method of a fundus image based on a classification and regression tree and AdaBoost. The method comprises the step of: constructing a 36-dimensinal feature vector including a local feature, a morphological feature and a pixel vector field divergence feature for each pixel point in the fundus image, so as to determine whether the pixel point is on a vessel. During classified calculation, the classification and regression tree is used as a weak classifier, so as to classify a sample set, then an AdaBoost classifier is trained, so as to obtain a strong classifier, and thus, the classified determination of each pixel point is completed, so as to obtain final segmentation results. The method has the advantages that a vessel trunk is preferably extracted, great advantages are taken to treat high-brightness focal areas, later treatment is facilitated and visual results are provided for main vessel diseases, and the method is suitable for computer aided quantitative analysis of the fundus image and disease diagnosis and has obvious clinical significance in auxiliary diagnosis of related diseases.

Description

technical field [0001] The invention relates to a retinal blood vessel segmentation method for a fundus image, and a retinal blood vessel segmentation method for a fundus image based on a classification regression tree and AdaBoost. Background technique [0002] A color fundus map is an image taken from different angles of the inner wall of the eyeball with a fundus camera. The fundus map can detect various eye diseases as early as possible, such as glaucoma, optic neuritis, macular degeneration, etc., which is convenient for timely and effective treatment. In addition, retinal blood vessels are the only blood vessels in the human body that can be directly observed non-invasively. Whether there are changes in its shape, diameter, scale, branch angle, and whether there is hyperplasia or exudation can reflect the lesions of the blood vessels in the whole body, such as Retinal microvessels in patients with arteriosclerosis, hypertension, diabetes, and kidney disease will all h...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06T7/00
CPCG06T7/0012G06T2207/30041G06T2207/20024G06T2207/10004G06F18/24323
Inventor 邹北骥朱承璋崔锦恺向遥李暄张思剑陈奇林
Owner CENT SOUTH UNIV
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