Retinal blood vessel segmentation method and device

A technology of retinal blood vessels and retina, which is applied in the field of medical image processing and machine vision, can solve the problems of subjective factors such as influence and manual segmentation that cannot be processed in batches, and achieve the effect of improving recognition, improving segmentation accuracy, and achieving balanced segmentation

Pending Publication Date: 2021-12-14
HEBEI UNIVERSITY
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Problems solved by technology

At present, the retinal vessel segmentation method is mainly based on manual segmentation by professional doctors, bu

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  • Retinal blood vessel segmentation method and device
  • Retinal blood vessel segmentation method and device
  • Retinal blood vessel segmentation method and device

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Embodiment Construction

[0044] The retinal vessel segmentation method provided by the present invention is a color retinal vessel segmentation method based on a cascaded residual deep neural network, such as figure 1 As shown, the method includes the following steps:

[0045] Step 1: Input a color retinal image. The color retinal image input in this step is generally selected from the public retinal dataset DRIVE or CHASE DB1. The input color retinal image lays the foundation for the subsequent training and verification of the retinal vessel segmentation model.

[0046] Step 2: Process the retinal image in step 1 with the green (Green, G) channel to complete the grayscale conversion. The image after grayscale conversion is shown in Figure 5 Shown in (a).

[0047] Step 3: Perform contrast-limited histogram equalization (CLAHE) and gamma conversion (Gamma Conversion) processing on the grayscale image in step 2. The image after gamma change is as follows Figure 5 Shown in (b). In the present inve...

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Abstract

The invention provides a retinal blood vessel segmentation method and device. The retinal blood vessel segmentation method is a color retinal image automatic segmentation method based on a deep convolutional neural network. The method comprises the following steps of: firstly, preprocessing a retina image, and processing a color image into a grey-scale map with higher contrast after a G channel, histogram equalization and gamma transformation; secondly, randomly segmenting the processed image into uniform small blocks to form a training data set; and then sending the data set into a deep convolutional neural network combining void space pyramid pooling and an efficient fusion attention mechanism, and carrying out training of a retinal vessel segmentation model; and finally, adjusting model parameters through a cross entropy loss function fused with the cost-sensitive matrix, and segmenting the blood vessel in the color retina image by using the optimized model. According to the invention, high segmentation accuracy and speed can be realized, and the manpower cost of doctors is reduced.

Description

technical field [0001] The invention relates to the technical fields of medical image processing and machine vision, in particular to a retinal blood vessel segmentation method and device. Background technique [0002] Retinal blood vessels are small blood vessels that doctors directly diagnose the human blood vessel structure in a non-invasive way. Segmentation and detection of retinal blood vessels can provide important clinical information for the diagnosis of eye diseases, diabetes, heart disease, etc. At present, the retinal vessel segmentation method is mainly based on manual segmentation by professional doctors, but manual segmentation has the disadvantages of being unable to batch process and being affected by subjective factors. In recent years, machine vision has performed well in medical image processing. It is particularly important to use machines instead of doctors to conduct intelligent observation and analysis of retinal images, assist doctors in determining...

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Application Information

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IPC IPC(8): G06T7/11G06T7/136G06N3/04G06N3/08
CPCG06T7/11G06T7/136G06N3/08G06T2207/10024G06T2207/20048G06T2207/20081G06T2207/20084G06T2207/30041G06T2207/30101G06N3/045Y02T10/40
Inventor 崔振超宋姝洁杨文柱齐静
Owner HEBEI UNIVERSITY
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