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Retinal blood vessels cut
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Using mean filtering, morphological operation and canny to find the edge to cut blood vessels, medical image processing is one of the key and difficult problems in the field of image processing. With the rapid development of digital image processing technology, with the help of computer and other auxiliary means, medical image extraction methods and image quality have been greatly improved. Retinal fundus image is the only deep microvessel that can be observed directly without trauma. The analysis of retinal blood vessels is helpful to the accurate diagnosis of diseases. However, due to the uneven gray distribution of retinal blood vessel image, the low contrast between target blood vessel and image background, and the pollution of image noise, the automatic segmentation of retinal blood vessel is very difficult. Therefore, it is of great significance for the pathological diagnosis of clinical ophthalmology to establish a computer-aided analysis system of fundus and retina images for quantitative analysis and qualitative detection of fundus and retina tissues. On the basis of consulting a large number of digital image processing literature, combined with the drive fundus retinal image database model, through the analysis of the characteristics of different image segmentation algorithms, the segmentation experiment and comparative analysis of fundus retinal vascular image are carried out. The main process is as follows: 1. By analyzing the RGB color system model, the contrast of retinal images of each channel is compared. Master the basic operation method of digital image processing
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LBP processing pictures
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Local binary patterns (LBP) is a type of feature used for classification in computer vision. LBP is the particular
case of the Texture Spectrum model proposed in 1990.[1][2] LBP was first described in 1994.[3][4] It has
since been found to be a powerful feature for texture classification; it has further been determined that when
LBP is combined with the Histogram of oriented gradients (HOG) descriptor, it improves the detection
performance considerably on some datasets.
The LBP feature vector,
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LBP image conversion based on OpenCV
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Using local binary pattern approach to color proof to the LBP pattern, local binary pattern (English: Local
binary patterns, abbreviations: LBP) In the field of machine vision is used to classify a characteristic, was put
forward in 1994. Local binary patterns in texture classification is a very powerful Features if local binary
pattern feature combined with the histogram of oriented gradients, you can very effectively improve
detection ef
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