Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing the possible dopamine deficiency. During the last decade, a number of computer systems have been proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the visual examination of the data. In this work, we propose a computer system based on machine learning to separate Parkinsonian patients and control subjects using the size and shape of the striatal region, modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel the striatum. This region is then divided into two according to the brain hemisphere division and characterized with 152 measures, extracted from the volume and its three possible 2-dimensional projections. Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally, the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as a feature.

中文翻译：

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基于纹状体形态学的帕金森病辅助诊断

帕金森综合征是一种以纹状体多巴胺进行性丧失为特征的临床综合征。它的诊断通常得到神经影像学数据的证实，例如 DaTSCAN 神经影像，这些数据可以可视化可能的多巴胺缺乏症。在过去的十年中，已经提出了许多计算机系统来自动分析 DaTSCAN 神经图像，从而消除了数据视觉检查所固有的主观性。在这项工作中，我们提出了一种基于机器学习的计算机系统，该系统使用从 DaTSCAN 数据建模的纹状体区域的大小和形状来区分帕金森病患者和控制对象。首先，使用基于自适应阈值的算法来分割纹状体。然后根据大脑半球的划分将该区域分为两部分，并用 152 个度量进行表征，从体积及其三个可能的二维投影中提取。之后，使用 Bhattacharyya 距离来丢弃最不具辨别力的度量，最后，通过支持向量机分类器估计神经图像类别。使用包含 189 个 DaTSCAN 神经图像的数据集对该方法进行了评估，获得了超过 94% 的准确率。该比率优于使用每个纹状体体素的强度作为特征的先前方法获得的比率。