提出一种基于腰椎MRI分割的图像分析框架。针对PCA对数据细节描述能力的不足，用ICA代替PCA同时进行形状和纹理模型建模，并对独立分量快速排序，形成基于独立分量分析的改进型主动外观模型(ICA-AAMs)，分割MRI中椎体；以分割结果基础，使用MDL准则获得腰椎生理曲线的最佳拟合函数，用该曲线曲率来计算腰椎前凸角，通过椎终板间的倾斜灰度投影来分析椎间盘退行性变化。实验表明，改进型ICA-AAMs的腰椎椎体分割具有更高的精度，在此基础上腰部生理参数定量分析也更为准确客观。系统不但以提供客观的量化信息的方式

An image analysis framework based on vertebrae segmentation in lumbar MRI is presented in this paper. As PCA lacks for describing data in detail, it is replaced by ICA in both shape and texture modeling with a further fast sorting of Independent Components. Improved ICA-AAMs is then built, with which vertebrae in MRI are segmented. Based on vertebra segmentation, the best fitting function of lumbar curve is obtained with the MDL principle; lumbar lordosis angles are then computed based on the curvature of this lumbar curve. At last, disc degeneration is evaluated from a declining intensity projecting profile between the endplates of vertebrae. Experiments have testified the accuracy and validity of the segmentation and quantitative analysis respectively. This framework reduces the clinical trifle in terms of providing impersonal quantitative information to doctors. It is also extensible to lumbar computer aided diagnosis (CAD).