Early diagnosis of the neurodegenerative disorder Parkinson’s disease (PD) and Scan without Evidence of Dopaminergic Deficit (SWEDD) is essential for effective patient management in neurodisorders, as both have the same clinical characteristics. The present work intends to propose an efficient method for analyzing volume rendering Single-Photon Emission Computed Tomography (SPECT) image slices, using Alpha stable distribution-based intensity normalization techniques for discriminating early PD from Healthy Control (HC) and SWEDD. The Volume rendering image (VRI) slices of early PD, HC and SWEDD are chosen from the database called Parkinson’s Progression Markers Initiative (PPMI). The alpha stable distribution technique is adapted to normalize the intensity values outside the striatum of the VRI in order to keep up the uniform intensity values throughout the database images. The shape features and image surface-related features are taken out from the VRI slices of the three different groups. The most optimized feature set is computed based on its consistency by Genetic Algorithm (GA). The computed optimized features of VRI slices show a remarkable performance in detecting the early stage of PD. The Extreme learning machine (ELM) classifier with Radial Basis Function (RBF) kernel shows a better performance accuracy of 99.12% than Support vector machine (SVM). Performance measures of the classifiers have ensured the validity of the experiments. Thus, the proposed method is advantageous to the neurologist in the early diagnosis of PD.

神经退行性疾病帕金森病 (PD) 的早期诊断和无多巴胺能缺陷证据的扫描 (SWEDD) 对于神经疾病患者的有效管理至关重要，因为两者具有相同的临床特征。目前的工作旨在提出一种分析体积渲染单光子发射计算机断层扫描 (SPECT) 图像切片的有效方法，使用基于 Alpha 稳定分布的强度归一化技术将早期 PD 与健康对照 (HC) 和 SWEDD 区分开来。早期 PD、HC 和 SWEDD 的体积渲染图像 (VRI) 切片选自称为帕金森病进展标记计划 (PPMI) 的数据库。alpha 稳定分布技术适用于对 VRI 纹状体外部的强度值进行归一化，以便在整个数据库图像中保持均匀的强度值。从三个不同组的 VRI 切片中取出形状特征和图像表面相关特征。最优化的特征集是基于遗传算法（GA）的一致性计算的。VRI 切片的计算优化特征在检测 PD 的早期阶段显示出显着的性能。具有径向基函数 (RBF) 内核的极限学习机 (ELM) 分类器显示出比支持向量机 (SVM) 更好的性能精度，达到 99.12%。分类器的性能测量确保了实验的有效性。因此，