Iterative Fundus Image Vessel Segmentation Method Based on Range Modulation Loss
A fundus image and blood vessel technology, applied in the field of image processing, can solve the problems of vascular structure constraints, models that cannot be learned to improve segmentation results, etc., and achieve the effect of improving accuracy and robustness
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[0074] The fundus image used in this embodiment comes from a public data set, and the hardware device used is a GPU workstation, including Intel Xeon CPU E5-2620 and GeForce GTX 1080GPU, as shown in Figure 2 (a) is the G channel component of the training image.
[0075] (1) Normalize the original image (ie fundus image)
[0076] Since fundus images from different sources have inconsistent image resolution, brightness, and contrast, it is necessary to normalize before inputting the segmentation model (ie dense convolutional neural network model) to ensure the stability of the segmentation algorithm. The specific implementation steps are as follows:
[0077] 1) Unify the diameter of the field of view, that is, take the position of the middle 1 / 2 height of the original image to estimate the size of the field of view of the original image, sum the values of the RGB channels of each pixel along the width direction of the original image, and then Binarize each pixel:
[0078] B ...
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