Deschênes Sylvain, Godbout Benoit, Skalli Wafa, de Guise Jacques
Laboratoire de recherche en imagerie et orthopédie, CRCHUM, ETS 1560 Sherbrooke E., Montreal (Quebec), H2L 4M1, Canada.
Stud Health Technol Inform. 2002;91:276-80.
We propose a wavelet multi-resolution analysis to localize specific features in both lateral and frontal radiographs. This analysis allows an elegant spectral investigation that leads simultaneously to image de-noising and edge extraction. It is combined with an a priori knowledge of the spine's morphology and a 3D spline curve characterization of its global shape. Actual work deals with identifying the contours of the vertebral bodies and the localization of vertebrae's endplates. However, this information could also lead to the selection of a 3D statistical model of the spine suited for the studied deformation. Working with retro-projections of the model, we aim at creating edge models for each vertebra that will be used to geometrically match the wavelet's edges. The manual feature identification could then be replaced in the reconstruction of the 3D representation of the spine.
我们提出一种小波多分辨率分析方法,用于在侧位和正位X光片中定位特定特征。这种分析允许进行精细的频谱研究,同时实现图像去噪和边缘提取。它与脊柱形态的先验知识以及其整体形状的三维样条曲线特征相结合。实际工作涉及识别椎体轮廓和椎骨终板的定位。然而,这些信息也可用于选择适合所研究变形的脊柱三维统计模型。通过使用该模型的反投影,我们旨在为每个椎骨创建边缘模型,用于与小波边缘进行几何匹配。这样,在脊柱三维表示的重建中,手动特征识别就可以被取代。