Fractal analysis was used in the study to determine a set of feature descriptors which could be applied in the process of diagnosing bone damage caused by osteoporosis. The subject of the research was CT images of vertebrae on the thoraco-lumbar region. The dataset contained images of healthy patients and patients diagnosed with osteoporosis. On the basis of fractal analysis and feature selection by linear stepwise regression, three descriptors were obtained. These were two fractal dimensions calculated by the variation method and fractal lacunarity calculated by the box counting method. The first two descriptors were obtained as a result of the analysis of gray images, and the third was the result of analysis of binary images. The effectiveness of the descriptors was verified using six popular supervised classification methods: linear and quadratic discriminant analyses, naive Bayes classifier, decision tree, K-nearest neighbors (K-NN) and random forests. The best results were obtained using the K-NN classifier; they were as follows: overall classification accuracy: 81%, classification sensitivity: 78%, classification specificity: 90%, positive predictive value: 90% and negative predictive value: 77%. The results of the research have shown that fractal analysis can be a useful tool to extract features of spinal CT images in the diagnosis of osteoporotic bone defects.

中文翻译：

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分形分析作为特征提取检测骨质疏松性骨破坏的方法

研究中使用分形分析来确定一组特征描述符，这些描述符可用于诊断骨质疏松症引起的骨损伤过程。研究的主题是胸腰椎区域的椎骨 CT 图像。该数据集包含健康患者和被诊断患有骨质疏松症的患者的图像。在分形分析和线性逐步回归特征选择的基础上，得到三个描述符。这是通过变分法计算的两个分形维数和通过盒计数法计算的分形空隙。前两个描述符是灰度图像分析的结果，第三个是二值图像分析的结果。使用六种流行的监督分类方法验证了描述符的有效性：ķ-最近的邻居（ķ-NN) 和随机森林。最好的结果是使用ķ-NN分类器；分别为：总体分类准确率：81%，分类敏感性：78%，分类特异性：90%，阳性预测值：90%，阴性预测值：77%。研究结果表明，分形分析可以作为提取脊柱CT图像特征在骨质疏松性骨缺损诊断中的有用工具。