Christopher Joseph Jesu, Ramakrishnan Swaminathan
Department of Instrumentation Engineering, MIT Campus, Anna University, Chromepet, Chennai 600 044, India.
J Med Syst. 2008 Apr;32(2):117-22. doi: 10.1007/s10916-007-9114-8.
In this work the mechanical strength components of human femur trabecular bone are analyzed and classified using planar radiographic images and neural network. The mechanical strength regions such as Primary Compressive, Primary Tensile, Secondary Tensile and Ward Triangle in femur trabecular bone images (N = 100) are delineated by semi-automatic image processing procedure. First and higher order texture parameters and parameters such as apparent mineralization and total area associated with the strength regions are derived for normal and abnormal images. The statistically derived significant parameters corresponding to the primary strength regions are fed to the neural network for training and validation. The classifications are carried out using feed forward network that is trained with standard back propagation algorithm. Results demonstrate that the apparent mineralization of normal samples is always high as (71%) compared to abnormal samples (64%). Entropy shows a high value (7.3) for normal samples and variation between the mean intensity and apparent mineralization for the primary strength zone is statistically significant (p < 0.0005). The classified outputs are validated by sensitivity and specificity measurements and are found to be 66.66% and 80% respectively. Further it appears that it is possible to differentiate normal and abnormal samples from the conventional radiographic images. As trabecular architecture in the human femur is an important factor contributing to bone strength, the procedure adopted here could be a useful supplement to the clinical observations for bone loss and fracture risk.
在这项工作中，利用平面放射图像和神经网络对人股骨小梁骨的机械强度成分进行了分析和分类。通过半自动图像处理程序勾勒出股骨小梁骨图像（N = 100）中的主要压缩、主要拉伸、次要拉伸和沃德三角等机械强度区域。针对正常和异常图像，导出了一阶和高阶纹理参数以及与强度区域相关的表观矿化和总面积等参数。将与主要强度区域相对应的经统计得出的显著参数输入神经网络进行训练和验证。使用前馈网络进行分类，该网络采用标准反向传播算法进行训练。结果表明，正常样本的表观矿化率始终高于异常样本（分别为71%和64%）。正常样本的熵值较高（7.3），主要强度区域的平均强度与表观矿化之间的差异具有统计学意义（p < 0.0005）。通过敏感性和特异性测量对分类输出进行验证，发现其分别为66.66%和80%。此外，似乎可以从传统放射图像中区分正常和异常样本。由于人股骨中的小梁结构是影响骨强度的一个重要因素，这里采用的方法可能是对骨质流失和骨折风险临床观察的有益补充。
