1 Department of Applied Mathematics, University of Granada, Spain.
2 Department of Computer Science, University of Munster, Germany.
Int J Neural Syst. 2018 Nov;28(9):1850022. doi: 10.1142/S0129065718500223. Epub 2018 May 7.
Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.
计算机辅助诊断 (CAD) 代表了一种重要的工具，可以使用几种实际的成像技术自动对患有和不患有阿尔茨海默病 (AD) 的患者进行分类。本研究分析了优化感兴趣体积 (VOI) 的方法，以便从磁共振成像 (MRI) 中提取三维 (3D) 纹理，从而诊断 AD、轻度认知障碍转化者 (MCIc)、轻度认知障碍非转化者 (MCInc) 和正常受试者。所提出方法的一个重要特点是使用 3D 特征代替传统的二维 (2D) 特征，通过使用 3D 离散小波变换 (3D-DWT) 方法从 T1 加权 MRI 中提取特征。由于将 3D-DWT 应用于每个 VOI 时系数数量很高，因此使用基于互信息的特征选择算法，即最小冗余最大相关性 (mRMR) 算法。为了发现大脑中最相关的区域 (VOI)，已经进行了区域优化，使用多目标遗传算法来优化系统的准确性是目标之一。使用多类支持向量机 (SVM) 分类器获得的混淆矩阵来计算系统的误差指标。主成分分析 (PCA) 用于减少分类器的特征数量。研究中使用的受试者队列由 296 名不同的患者组成。第一组 206 名患者用于优化 VOI 选择，另一组 90 名独立患者（不属于第一组）用于测试遗传算法得出的解决方案。所提出的方法在多类分类中取得了优异的结果，准确率达到 94.4%，并且还提取了与大脑最相关点位置相关的重要信息。这表明该方法可以辅助其他神经退行性疾病的研究，提高诊断的准确性并找到与疾病相关的大脑最相关区域。
