Efficient CNN-CRF network-based retina image segmentation method

An image segmentation and retinal technology, which is applied in the field of medical image processing, can solve problems such as low precision, long processing time, and inability to automatically segment, so as to achieve the effect of ensuring accuracy

Inactive Publication Date: 2017-10-17
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0005] The above segmentation methods either have low precision,

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  • Efficient CNN-CRF network-based retina image segmentation method
  • Efficient CNN-CRF network-based retina image segmentation method
  • Efficient CNN-CRF network-based retina image segmentation method

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Embodiment Construction

[0023] The method of the present invention first uses the entire retinal image as the input of the full convolutional neural network, and then uses the full convolutional neural network to predict the pixels in the retinal image; according to the output of the full convolutional neural network, uses the conditional random field to The feature image is segmented, and the blood vessel segmentation map is finally obtained through only one forward operation, such as figure 1 shown.

[0024] The method and technical effects of the present invention will be described below through specific examples.

[0025] Step 1: Randomly select 40 retinal images from the international public dataset DRIVE (Digital Retinal Image for Vessel Extraction), 30 of which are used as training samples, and the remaining 10 are used as test images. To solve the problem of insufficient training samples, the present invention expands the number of samples by rotating, flipping, and other operations on each ...

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Abstract

The invention discloses an efficient CNN-CRF network-based retina image segmentation method. According to the method, aiming at an image space information constraint problem, a full convolutional neural network and a conditional random field are combined; and aiming at a retina image blood vessel segmentation problem, a whole image is designed and an end-to-end deep learning and segmentation model is trained. Through predicting image pixels through the full convolutional neural network and segmenting semantic meanings of the conditional random field, a retina blood vessel image segmentation result is finally obtained. Compared with a pixel-by-pixel segmentation method, the method is capable of segmenting a complete image through a forward operation, so that the processing effect of the method is higher than that of the current technological level; and the method can be widely applied to the retina diagnosis fields of diabetes, hypertension and glaucoma, and provide powerful theoretical and technological support for the pathological diagnosis of retina images.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a retinal image segmentation method based on an efficient CNN-CRF (Convolutional Neural Network, Conditional Random Field) network. Background technique [0002] Retinal images are closely related to blinding eye diseases such as diabetes, hypertension, and glaucoma, so segmenting retinal images for digital analysis is an essential step. Since manual segmentation of retinal images is time-consuming and laborious, automatic segmentation methods for retinal images have gradually become mainstream. [0003] Segmentation methods for retinal vessel images are mainly divided into two categories: rule-based and learning-based segmentation methods. The rule-based segmentation method mainly uses the adjusted parameters that constitute the segmentation rules to process the image. Chaudhuri et al proposed to use a Gaussian curve to approximate the gray level information, and use 12...

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Application Information

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IPC IPC(8): G06T7/00G06T7/10
CPCG06T7/0012G06T7/10G06T2207/20081G06T2207/20084G06T2207/30041
Inventor 杨路罗院生徐宏程洪
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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