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
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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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