An edge detection and extraction algorithm for standard water model of magnetic resonance imaging

Abstract

The invention discloses an edge detection and extraction algorithm for magnetic resonance standard water model, Firstly, the pixel difference between true and false edges is enlarged by spatial enhancement, Then the initial adaptive canny algorithm and area sorting are used to eliminate most of the pseudo-edges, After that, the edge of each layer of spherical shell is obtained by closed region reduction and quadratic adaptive canny algorithm. Finally, the final image is classified by convolution neural network, and the edge required by the final result is obtained. After edge detection by canny operator, pseudo-edge can be suppressed effectively, By using the secondary canny operator to detect the edges after restoring the image in the closed area, the edges that the first canny can not detect can be detected effectively. This method has the advantages of high recognition rate and short time, and can reduce the error in the calculation process, so that the edge information of the standard water model can be extracted completely and clearly.

Description

technical field [0001] The invention relates to the field of image edge detection algorithms, in particular to an edge detection and extraction algorithm for magnetic resonance standard water models. Background technique [0002] Magnetic resonance imaging (Magnetic resonance imaging, MRI) is the most advanced imaging diagnostic technology in the medical field today. This technology can extract more life information without damaging human organs and tissues. A high-tech after ultrasound, has been widely used in clinical applications. It uses magnetic field and radio frequency signal to vibrate the precessing hydrogen nuclei in human tissue to generate radio frequency signal, which is imaged by computer processing. Therefore, to ensure the efficient and accurate operation of the magnetic resonance imaging system, it is necessary to ensure the accuracy of the magnetic resonance test data. [0003] Usually, a standard water model needs to be added during the simulation proces...

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Problems solved by technology

[0005] At present, there are many methods for edge feature extraction, including differential operator method, curve fitting method, gray histogram threshold method, facet model method, etc. Each method has its own advantages and disadvantages, and there are many problems, such as different edge The effect of feature extraction algorithms on different image processing is very different. Moreover, MRI images generally have large noises. General edge extraction algorithms often detect more false edges, while real edges will be missing, resulting in extraction The information provided contains large errors

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