Department of Electronics & Communication Engineering, Amity University, Noida, Uttar Pradesh, India.
Univ. Orléans, I3MTO Laboratory, EA 4708, 45067 Orléans, France.
Comput Biol Med. 2017 Dec 1;91:148-158. doi: 10.1016/j.compbiomed.2017.10.011. Epub 2017 Oct 13.
Osteoporosis is a common bone disease which often leads to fractures. Clinically, the major challenge for the automatic diagnosis of osteoporosis is the complex architecture of bones. The clinical diagnosis of osteoporosis is conventionally done using Dual-energy X-ray Absorptiometry (DXA). This method has specific limitations, however, such as the large size of the instrument, a relatively high cost and limited availability. The method proposed here is based on the automatic processing of X-ray images. The bone X-ray image was statistically processed and strategically reformed to extract discriminatory statistical features of different orders. These features were used for machine learning for the classification of two populations composed of osteoporotic and healthy subjects. Four classifiers - support vector machine (SVM), k-nearest neighbors, Naïve Bayes and artificial neural network - were used to test the performance of the proposed method. Tests were performed on X-ray images of the calcaneus bone collected from the hospital of Orleans. The results are significant in terms of accuracy and time complexity. Experimental results indicate a classification rate of 98% using an SVM classifier which is encouraging for automatic osteoporosis diagnosis using bone X-ray images. The low time complexity of the proposed method makes it suitable for real time applications.
骨质疏松症是一种常见的骨骼疾病，常导致骨折。临床上，骨质疏松症自动诊断的主要挑战是骨骼的复杂结构。骨质疏松症的临床诊断通常采用双能 X 射线吸收法（DXA）。然而，这种方法有其特定的局限性，例如仪器尺寸大、成本相对较高且可用性有限。这里提出的方法基于 X 射线图像的自动处理。对骨 X 射线图像进行了统计处理和策略性重构，以提取不同阶数的有区别的统计特征。这些特征用于机器学习，对由骨质疏松症和健康受试者组成的两个群体进行分类。使用支持向量机（SVM）、k-最近邻、朴素贝叶斯和人工神经网络这四种分类器来测试所提出方法的性能。在奥尔良医院采集的跟骨 X 射线图像上进行了测试。就准确性和时间复杂度而言，结果是显著的。实验结果表明，使用 SVM 分类器的分类率为 98%，这对于使用骨 X 射线图像进行自动骨质疏松症诊断是令人鼓舞的。所提出方法的低时间复杂度使其适用于实时应用。
