Batmanghelich Nematollah, Taskar Ben, Davatzikos Christos
Section of Biomedical Image Analysis, Radiology Department, University of Pennsylvania, Philadelphia, PA 19014, USA.
Inf Process Med Imaging. 2009;21:423-34. doi: 10.1007/978-3-642-02498-6_35.
This paper presents a general and unifying optimization framework for the problem of feature extraction and reduction for high-dimensional pattern classification of medical images. Feature extraction is often an ad hoc and case-specific task. Herein, we formulate it as a problem of sparse decomposition of images into a basis that is desired to possess several properties: 1) Sparsity and local spatial support, which usually provides good generalization ability on new samples, and lends itself to anatomically intuitive interpretations; 2) good discrimination ability, so that projection of images onto the optimal basis yields discriminant features to be used in a machine learning paradigm; 3) spatial smoothness and contiguity of the estimated basis functions. Our method yields a parts-based representation, which warranties that the image is decomposed into a number of positive regional projections. A non-negative matrix factorization scheme is used, and a numerical solution with proven convergence is used for solution. Results in classification of Alzheimers patients from the ADNI study are presented.
本文针对医学图像高维模式分类中的特征提取与约简问题，提出了一个通用且统一的优化框架。特征提取通常是一项特定且因情况而异的任务。在此，我们将其表述为一个图像稀疏分解问题，分解到一个期望具有若干特性的基上：1）稀疏性和局部空间支撑性，这通常能在新样本上提供良好的泛化能力，并便于进行解剖学上直观的解释；2）良好的判别能力，以便将图像投影到最优基上能产生用于机器学习范式的判别特征；3）估计基函数的空间平滑性和连续性。我们的方法产生了一种基于部件的表示，保证图像被分解为多个正的区域投影。使用了一种非负矩阵分解方案，并采用具有收敛性证明的数值解来求解。展示了来自阿尔茨海默病神经影像学计划（ADNI）研究中阿尔茨海默病患者分类的结果。
