Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India.
Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India.
Magn Reson Imaging. 2019 Oct;62:167-173. doi: 10.1016/j.mri.2019.06.019. Epub 2019 Jul 4.
An inventive scheme for automated tissue segmentation and classification is offered in this paper using Fast Discrete Wavelet Transform (DWT)/Band Expansion Process (BEP), Kernel-based least squares Support Vector Machine (KLS-SVM) and F-score, backed by Principal Component Analysis (PCA). Using input as T1, T2 and Proton Density (PD) scans of patients, CSF (Cerebrospinal Fluid), WM (White matter) and GM (Gray matter) are afforded as output, which act as hallmark for brain atrophy and thus sustaining in diagnosis of Alzheimer's disease (AD) from Mild Cognitive Impairment (MCI) and Healthy Controls (HC). The blending of BEP features from DWT and texture features from Gray Level Co-occurrence Matrix (GLC) promises to be a savior in atrophy revelation of the segmented tissues. Data used for evaluation of this study is taken from the ADNI database that encloses T1-weighted s-MRI (Structural Magnetic Imaging Resonance) scans of 158 patients with AD and 145 HC. Preprocessing steps unearthed five parameters for classification (i.e. cortical thickness, curvature, gray matter volume, surface area, and sulcal depth), in the preliminary step. For challenging the classifier performance, ROC (Receiver operating characteristics) curves are painted and the SVM classifiers of two-dimensional spaces took the top two important features as classification features for separating HC and AD to the maximum extent. The final results revealed that Fast DWT + F-Score + PCA + KLS-SVM + Poly Kernel is giving 100% tissue classification accuracy for test samples under consideration with only 7 input features.
本文提出了一种使用快速离散小波变换（DWT）/带扩展处理（BEP）、基于核的最小二乘支持向量机（KLS-SVM）和 F 分数，以及主成分分析（PCA）的自动化组织分割和分类的创新方案。该方案以患者的 T1、T2 和质子密度（PD）扫描作为输入，以脑脊液（CSF）、白质（WM）和灰质（GM）作为输出，这些输出是脑萎缩的标志，因此有助于从轻度认知障碍（MCI）和健康对照（HC）中诊断阿尔茨海默病（AD）。DWT 的 BEP 特征与灰度共生矩阵（GLC）的纹理特征的融合有望成为揭示分割组织萎缩的救星。本研究使用的数据来自 ADNI 数据库，其中包含 158 名 AD 患者和 145 名 HC 的 T1 加权 s-MRI（结构磁共振成像）扫描。预处理步骤揭示了用于分类的五个参数（即皮质厚度、曲率、灰质体积、表面积和脑沟深度），这是初步步骤。为了挑战分类器的性能，绘制了 ROC（接收器操作特征）曲线，二维空间的 SVM 分类器选择了两个最重要的特征作为分类特征，以最大限度地区分 HC 和 AD。最终结果表明，快速 DWT+F 分数+PCA+KLS-SVM+多核在仅考虑 7 个输入特征的情况下，对测试样本的组织分类准确率达到 100%。
