Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism.
Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism.
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DOI：
10.1371/journal.pone.0130274
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Salas-Gonzalez D
中科院分区：
文献类型：
--
作者：
Brahim A;Ramírez J;Górriz JM;Khedher L;Salas-Gonzalez D
强度归一化是 DaTSCAN SPECT 成像研究和分析中重要的预处理步骤。由于大多数自动监督图像分割和分类方法都将强度分布的假设建立在标准化强度范围上，因此强度归一化起着非常重要的作用。在这项工作中，对不同的新颖强度归一化方法进行了比较。这些提出的方法基于高斯混合模型 (GMM) 图像滤波和均方误差 (MSE) 优化。基于GMM的图像滤波方法是根据概率阈值来实现的，该阈值去除非特定区域中似然性可以忽略不计的簇。 MSE 优化方法由线性变换组成，该线性变换是通过最小化强度归一化图像和模板之间的非特定区域中的 MSE 获得的。所提出的强度归一化方法与：i）基于广泛使用的特异性与非特异性结合比的标准方法，以及ii）基于α稳定分布的线性方法。该比较是在 DaTSCAN 图像数据库上进行的，该数据库包括分析和分类阶段，用于开发用于帕金森综合症 (PS) 检测的计算机辅助诊断 (CAD) 系统。此外，这些提出的方法还校正了调制图像强度的空间变化伪影。最后，通过对这两种方法使用留一法交叉验证技术，系统获得了高达 92.91% 的准确度、94.64% 的灵敏度和 92.65% 的特异性的结果，优于以前基于标准和线性方法（用作参考）的方法。使用先进的强度归一化技术，例如基于 GMM 的图像滤波和 MSE 优化，可以改善 PS 的诊断。
Intensity normalization is an important pre-processing step in the study and analysis of DaTSCAN SPECT imaging. As most automatic supervised image segmentation and classification methods base their assumptions regarding the intensity distributions on a standardized intensity range, intensity normalization takes on a very significant role. In this work, a comparison between different novel intensity normalization methods is presented. These proposed methodologies are based on Gaussian Mixture Model (GMM) image filtering and mean-squared error (MSE) optimization. The GMM-based image filtering method is achieved according to a probability threshold that removes the clusters whose likelihood are negligible in the non-specific regions. The MSE optimization method consists of a linear transformation that is obtained by minimizing the MSE in the non-specific region between the intensity normalized image and the template. The proposed intensity normalization methods are compared to: i) a standard approach based on the specific-to-non-specific binding ratio that is widely used, and ii) a linear approach based on the α-stable distribution. This comparison is performed on a DaTSCAN image database comprising analysis and classification stages for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome (PS) detection. In addition, these proposed methods correct spatially varying artifacts that modulate the intensity of the images. Finally, using the leave-one-out cross-validation technique over these two approaches, the system achieves results up to a 92.91% of accuracy, 94.64% of sensitivity and 92.65 % of specificity, outperforming previous approaches based on a standard and a linear approach, which are used as a reference. The use of advanced intensity normalization techniques, such as the GMM-based image filtering and the MSE optimization improves the diagnosis of PS.
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