Lumbar lateral image rapid classification method

Abstract

The invention provides a lumbar lateral image rapid classification method, which comprises the following steps: S1, acquiring a lumbar lateral image, and selecting a region of interest on the lumbar lateral image; S2, obtaining texture features; S3, performing secondary extraction after feature selection; and S4, performing a classifier identification process to obtain a classification result. According to the method, decomposition and reconstruction of the lumbar lateral image are realized according to a nonlinear enhancement algorithm of controllable pyramid decomposition, the spatial resolution of the image can be improved, and effective image enhancement can be performed on low-contrast detail areas such as bone microstructures and the like on the image, so that the display on image presentation becomes clear. The algorithm has approximate translation invariance and more direction selectivity, and the enhancement of the lumbar vertebra overlapping edge and the bone microstructure edge is effectively improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for quickly classifying lumbar lateral images. Background technique [0002] At present, the incidence of thoracolumbar fractures is on the rise, especially severe thoracolumbar fractures combined with spinal cord injury, which has become one of the focuses of orthopedic clinical research. With the development of spinal biomechanics and the progress of imaging, many new theories and technologies have appeared in the treatment of thoracolumbar fractures, but for the study of classification system, grasp of surgical indications, direct or indirect decompression, and fixed segment Issues of choice, fusion or non-fusion therapy remain controversial. [0003] Due to factors such as noise in imaging data, improper X-exposure, and excessive thickness of human tissue, the detailed structure of the lumbar spine is obliterated by noise, blurred edges, and low contrast ar...

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Problems solved by technology

[0003] Due to factors such as noise in imaging data, improper X-exposure, and excessive thickness of human tissue, the detailed structure of the lumbar spine is obliterated by noise, blurred edges, and low contrast are likely to cause missed and misdiagnosed diseased tissues, which restricts the development and widespread use of imaging.

[0004] Changes in bone microstructure are affected by factors such as gender and age differences. The traditional adaptive unsharp masking algorithm uses local variance as a parameter to enhance the proportion of high-frequency components, thereby effectively enhancing the edges and details of the image. Sensitive to noise, ineffective in enhancing low-contrast parts of the original image and prone to artifacts

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