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The application of key feature extraction algorithm based on Gabor wavelet transformation in the diagnosis of lumbar intervertebral disc degenerative changes

机译：基于Gabor小波变换的关键特征提取算法在腰椎间盘退行性变化诊断中的应用

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OBJECTIVE:Based on the theoretical basis of Gabor wavelet transformation, the application effects of feature extraction algorithm in Magnetic Resonance Imaging (MRI) and the role of feature extraction algorithm in the diagnosis of lumbar vertebra degenerative diseases were explored. METHOD:The structure of lumbar vertebra and degenerative changes were respectively introduced to clarify the onset mechanism and pathological changes of lumbar vertebra degenerative changes. Most importantly, the theoretical basis of Gabor wavelet transformation and the extraction effect of feature information in lumbar vertebra MRI images were introduced. The differentiation effects of feature information extraction algorithm on annulus fibrosus and nucleus pulposus were analyzed. In this study, the data of lumbar spine MRI was randomly selected from the Wenzhou Lumbar Spine Research Database as research objects. A total of 130 discs were successfully fitted, and 109 images were graded by a doctor after observation, which was compared with the results of the artificial diagnosis. Through the comparison with the results of observation and diagnosis by professional doctors, the accuracy of feature extraction algorithm based on Gabor wavelet transformation in the diagnosis of lumbar vertebra degenerative changes was analyzed. RESULTS:1. Compared with the results of the manual diagnosis, the accuracy of the classification method was 88.3%. In addition, the specificity (SPE), accuracy (ACC), and sensitivity (SEN) of the classification method were respectively 89.5%, 92.4%, and 87.6%. 2. The mutual information method and the KLT algorithm were utilized for vertebral body tracking. The maximum mutual information method was more effective in the case of fewer image sequences; however, with the increase of image frames, the accumulation of errors would make the tracking effects of images get worse. Based on the KLT algorithm, the enhanced vertebral boundary information was selected; the soft tissues showed in the obtained images were smooth, the boundary information of vertebral body was enhanced, and the results were more accurate. CONCLUSION:The feature extraction algorithm based on Gabor wavelet transformation could easily and quickly realize the localization of the lumbar intervertebral disc, and the accuracy of the results was ensured. In addition, from the aspect of vertebral body tracking, the tracking effects based on the KLT algorithm were better and faster than those based on the maximum mutual information method.
机译：目的：基于Gabor小波变换的理论基础，探讨了磁共振成像（MRI）特征提取算法的应用效果及特征提取算法在腰椎椎体诊断中的作用。方法：分别引入腰椎和退行性变化的结构，以阐明腰椎退行性变化的发病机制和病理变化。最重要的是，介绍了Gabor小波变换的理论基础和腰椎椎骨MRI图像中特征信息的提取效果。分析了特征信息提取算法的分化效果和环纤维素和核浆气的分化效应。在这项研究中，腰椎MRI的数据从温州腰椎研究数据库随机选择作为研究对象。总共130个圆盘成功地安装了130个圆盘，并且在观察后通过医生进行了109个图像，与人工诊断的结果进行了比较。通过与专业医生的观察和诊断结果的比较，分析了基于Gabor小波变换的特征提取算法的准确性，分析了腰椎诊断性改变变化的诊断。结果：1。与手动诊断结果相比，分类方法的准确性为88.3％。此外，分类方法的特异性（SPE），精度（ACC）和敏感度（SEN）分别为89.5％，92.4％和87.6％。 2.互信息方法和KLT算法用于椎体跟踪。在图像序列较少的情况下，最大互信息方法更有效;然而，随着图像帧的增加，误差的累积会使图像的跟踪效果变得更糟。基于KLT算法，选择了增强的椎骨边界信息;在所获得的图像中显示的软组织是光滑的，椎体的边界信息增强，结果更准确。结论：基于Gabor小波变换的特征提取算法很容易迅速地实现腰椎椎间盘的定位，确保了结果的准确性。另外，从椎体跟踪的方面，基于KLT算法的跟踪效果比基于最大互信息方法更好，更快。

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