Diabetic retinopathy fundus photography standard image generation method

A diabetic retina and standard image technology, which is applied in the field of medical image processing, can solve the problems of DR fundus photography image clarity and photography angle are difficult to achieve the ideal state, and achieve the effect of improving the accuracy of diagnosis and the method is simple and effective

Inactive Publication Date: 2017-12-22
HUZHOU TEACHERS COLLEGE
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AI Technical Summary

Problems solved by technology

[0007] In view of the lack of professional equipment and professional ophthalmologists in grass-roots hospitals, it is difficult to achieve the ideal state of DR intelligent auxiliary diagnosis input requirements for the acquired DR fundus photographic images and camera angles. This invention will use GAN technology in deep learning , optimize and upgrade the DR fundus photographic images obtained by different ophthalmic equipment at the grassroo

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  • Diabetic retinopathy fundus photography standard image generation method
  • Diabetic retinopathy fundus photography standard image generation method
  • Diabetic retinopathy fundus photography standard image generation method

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Embodiment 1

[0021] Step (1) further includes: the generation model adopted is the GAN model of the generation confrontation network in deep learning. GAN was proposed by Ian Goodfellow in 2014. The main idea is to train a generator (Generator, referred to as G) from random noise or Generate realistic samples from potential variables, and train a discriminator (D for short) to distinguish real data and generated data, and train both at the same time until a Nash equilibrium is reached—the data generated by the generator is indistinguishable from real samples. The discriminator also cannot correctly distinguish generated data from real data. This model can generate standard images from non-standard diabetic retinopathy images; for the GAN model, its optimization problem is a minimization-maximization problem, and its objective function is shown in formula (1); where x is sampled from the real data distribution p data (x), z sampled from the prior distribution p z (z) (e.g. Gaussian noise ...

Embodiment 2

[0024] The extraction method of the local orientation gradient histogram HOG feature is as follows: first divide the image into small connected areas, that is: cell units, then collect the gradient or edge direction histograms of each pixel in the cell units, and finally combine these histograms The graphs are combined to form a feature descriptor.

[0025] The extraction method of the HOG feature of the local orientation gradient histogram is further described as follows: a local area image is performed:

[0026] 1) Grayscale (consider the image as a three-dimensional image of x, y, z (grayscale));

[0027] 2) Use standardized Gamma space and color space correction method to standardize (normalize) the color space of the input image; the purpose is to adjust the contrast of the image, reduce the influence of local shadows and illumination changes in the image, and suppress noise at the same time interference.

[0028] In order to reduce the influence of illumination factors...

Embodiment 3

[0045] Extraction method of SIFT feature in step 2

[0046] The full name of SIFT is Scale Invariant Feature Transform, scale invariant feature transformation, including 4 main steps:

[0047] 1) Extremum detection in scale space: search for images in all scale spaces, and identify potential

[0048] Points of interest that are invariant to scale and selection.

[0049] Usually DoG (differential Gaussian, Difference o f Gaussina) to approximate Laplacian of Gaussian.

[0050] Let k be the scale factor of two adjacent Gaussian scale spaces, then the definition of DoG:

[0051] D(x,y,σ)=[G(x,y,kσ)-G(x,y,σ)]*I(x,y)

[0052] =L(x,y,kσ)-L(x,y,σ)

[0053] Among them, G(x, y, σ) is a Gaussian kernel function. σ is called the scale space factor, which is the standard deviation of the Gaussian normal distribution, reflecting the degree of blurring of the image, the larger the value, the blurrier the image, and the larger the corresponding scale. L(x, y, σ) represents the Gaussia...

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Abstract

The invention provides a diabetic retinopathy fundus photography standard image generation method, which comprises the following steps: 1) enabling a collected non-standard fundus image to be generated into a new sample image through a generation model; 2) carrying out local feature extraction on the new sample image; and 3) comparing local features of the non-standard image with local features of a standard image in a discrimination model, if the local features are consistent, outputting the new sample image, that is, the generated standard image, and if the local features are not consistent, adjusting the new sample image. The provided method is simple and effective; the definition of the generated standard image reaches requirement of an intelligent aided diagnosis system; and accuracy of diagnosis is improved.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a standard image generation method for fundus photography of diabetic retinopathy, which is used for artificial intelligence medical diagnosis. Background technique [0002] Diabetic retinopathy (Diabetic Retinopathy, DR) is a common blinding eye disease. China is the country with the largest number of type 2 diabetes patients in the world, and the prevalence and blindness rate of DR are also increasing year by year. It is currently the number one blinding disease among working-age people. Currently, the prevalence of DR among people with diabetes in my country is 24.7%-37.5%. According to the statistics of the International Diabetes Federation, as of 2015, there were about 110 million people with diabetes in my country. Based on this, it is estimated that there are about 27 million people with DR in my country. The prevention and treatment of the disease has become an in...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/50G06V10/462G06V2201/03G06F18/2411
Inventor 吴茂念杨卫华郑博朱绍军刘云芳孙元强
Owner HUZHOU TEACHERS COLLEGE
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