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首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >DEGENERATIVE DISC SEGMENTATION AND DIAGNOSIS TECHNOLOGY USING IMPORTANT FEATURES FROM MRI OF SPINE IN IMAGES
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DEGENERATIVE DISC SEGMENTATION AND DIAGNOSIS TECHNOLOGY USING IMPORTANT FEATURES FROM MRI OF SPINE IN IMAGES

机译：利用图像中脊柱MRI重要特征的变性椎间盘分离和诊断技术

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摘要

In this paper, we focus on the medical imaging segmentation techniques which are used in the study of spine diseases. In the medical reports, it is shown that common people worry more about the spine diseases caused by the disc degeneration. Because of the complex composition of the spine, which includes the spine bones, cartilage, fat, water and soft tissue, it is hard to correctly and easily find out the position of each cartilage in the spine images. This above problem always causes over-segmentation or unability to extract the cartilages. Thus, we propose an accurate and automated method to detect the abnormal disc. We combine two standard models with the threshold value to accurately identify the cartilage. Among the processing, we also solve the noising problems of spine image through morphological methods, removing the noncartilage areas using our proposed method, and find out the average height of the cartilages. Therefore, we can easily determine whether the disc is degenerated or not. In the experimental result, the segmentation accuracy of the extracted region by the proposed approach is evaluated by two criterions. The first criterion is statistical evaluation indices of image segmentation. It is evaluated by professional physician's manual segmentation, and the results show that our proposed method is easily implemented and has high accuracy, with the highest rate reaching 99.88%. The second criterion is a comparison evaluation index evaluated by our proposed system and other existence system. From this result, we know that our proposed system is better than other existence system.
机译：在本文中，我们重点研究用于脊椎疾病研究的医学成像分割技术。在医学报告中，表明普通人更加担心由椎间盘退变引起的脊柱疾病。由于脊柱的复杂组成，包括脊柱的骨骼，软骨，脂肪，水和软组织，因此很难正确，轻松地找出每个软骨在脊柱图像中的位置。上述问题总是导致过度分割或无法提取软骨。因此，我们提出了一种准确，自动化的方法来检测异常光盘。我们将两个标准模型与阈值结合起来以准确识别软骨。在处理过程中，我们还通过形态学方法解决了脊柱图像的噪点问题，使用我们提出的方法去除了非软骨区域，并找出了软骨的平均高度。因此，我们可以轻松确定光盘是否已退化。在实验结果中，通过两个标准评估了所提方法对提取区域的分割精度。第一个标准是图像分割的统计评估指标。通过专业医师的人工分割对结果进行评价，结果表明，该方法易于实现，具有较高的准确率，最高达到99.88％。第二个标准是由我们提出的系统和其他存在系统评估的比较评估指标。从这个结果，我们知道我们提出的系统比其他存在系统更好。

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