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首页> 外文期刊>Arabian Journal for Science and Engineering >Diagnosis of Parkinson’s Disease at an Early Stage Using Volume Rendering SPECT Image Slices
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Diagnosis of Parkinson’s Disease at an Early Stage Using Volume Rendering SPECT Image Slices

机译：使用体绘制SPECT图像切片在早期诊断帕金森氏病

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摘要

The non-degenerative variant called scans without evidence of dopaminergic deficit (SWEDD) is clinically analyzed andwrongly understood as Parkinson’s disease (PD) that results ineffective diagnosis of PD in the early stage. The present workis designed to improve the diagnostic accuracy at the early stage of PD from SWEDD and healthy control (HC). The volumerendering image slices are used as a novel method to achieve better diagnostic accuracy. These image slices are chosen fromthe single-photon emission computed tomography (SPECT) images based on their striatal uptake region, which contributesappreciated information on the shape of the striatum. Features related to surface and the shape of the striatum are calculatedfrom the segmented region of the chosen image slices to illustrate the good amount of variations among early PD, SWEDD,and HC. Among these feature sets, the most optimized feature is selected using genetic algorithm. The performance of theclassifiers like linear, radial basis function-support vector machine (RBF-SVM), extreme learning machine (ELM) activationfunctions, and RBF-ELM are investigated and compared based on the most optimized feature. It is noted that the RBFELMoffers better performance with an accuracy of 98.23% than the other classifiers. This also proves that the present workis better than the previous studies. Hence, the proposed approach could act as an aid in the detection of early stage of PD.
机译：临床上分析了无多巴胺能缺乏症（SWEDD）证据的非变性变体扫描，并将其错误理解为帕金森氏病（PD），导致帕金森氏病在早期无法有效诊断。目前的工作旨在提高SWEDD和健康对照（HC）在PD早期诊断的准确性。体绘制图像切片被用作一种新颖的方法，以实现更好的诊断准确性。这些图像切片基于其纹状体吸收区域从单光子发射计算机断层扫描（SPECT）图像中选择，这些图像有助于纹状体的形状信息。从所选图像切片的分割区域计算出与纹状体表面和形状相关的特征，以说明早期PD，SWEDD和HC之间的大量变化。在这些功能集中，使用遗传算法选择最优化的功能。基于最优化的功能，对线性，径向基函数支持向量机（RBF-SVM），极限学习机（ELM）激活函数和RBF-ELM等分类器的性能进行了研究和比较。值得注意的是，RBFELM提供了比其他分类器更好的性能，准确性为98.23％。这也证明目前的工作比以前的研究要好。因此，所提出的方法可以帮助检测PD的早期阶段。

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