Department of Software and IT Engineering, Ecole de Technologie Supérieure, Montréal, QC, Canada.
IEEE Trans Biomed Eng. 2010 May;57(5):1143-51. doi: 10.1109/TBME.2009.2037214. Epub 2010 Feb 5.
Spinal deformities are diagnosed using posteroanterior (PA) radiographs. Automatic detection of the spine on conventional radiographs would be of interest to quantify curve severity, would help reduce observer variability and would allow large-scale retrospective studies on radiographic databases. The goal of this paper is to present a new method for automatic detection of spinal curves from a PA radiograph. A region of interest (ROI) is first extracted according to the 2-D shape variability of the spine obtained from a set of PA radiographs of scoliotic patients. This region includes 17 bounding boxes delimiting each vertebral level from T1 to L5. An adaptive filter combining shock with complex diffusion is used to individually restore the image of each vertebral level. Then, texture descriptors of small block elements are computed and submitted for training to support vector machines (SVM). Vertebral body's locations are thereby inferred for a particular vertebral level. The classifications of block elements for all 17 SVMs are identified in the image and a voting system is introduced to cumulate correctly predicted blocks. A spline curve is then fitted through the centers of the predicted vertebral regions and compared to a manual identification using a Student t-test. A clinical validation is performed using 100 radiographs of scoliotic patients (not used for training) and the detected spinal curve is found to be statistically similar (p < 0.05) in 93% of cases to the manually identified curve.
脊柱畸形是通过后前位（PA）射线照片诊断的。在常规射线照片上自动检测脊柱对于量化曲线严重程度、减少观察者的变异性以及允许在射线照相数据库上进行大规模回顾性研究将非常有意义。本文的目的是提出一种从 PA 射线照片自动检测脊柱曲线的新方法。首先根据从一组脊柱侧凸患者的 PA 射线照片中获得的脊柱 2D 形状变化提取感兴趣区域（ROI）。该区域包括 17 个边界框，从 T1 到 L5 每个椎体水平都有一个边界框。使用 Shock 与复杂扩散相结合的自适应滤波器对每个椎体水平的图像进行单独恢复。然后，计算小块元素的纹理描述符，并提交给支持向量机（SVM）进行训练。从而推断出特定椎体水平的椎体位置。在图像中识别出所有 17 个 SVM 的块元素分类，并引入投票系统来累积正确预测的块。然后通过预测的椎体区域的中心拟合样条曲线，并使用学生 t 检验与手动识别进行比较。使用 100 张未用于训练的脊柱侧凸患者的射线照片进行临床验证，发现检测到的脊柱曲线在 93%的情况下与手动识别的曲线在统计学上相似（p<0.05）。
