Purpose The study is conducted to identify the best corpus callosum (CC) sub-region that corresponds to highest callosal tissue alteration occurred due to Parkinsonism. In this regard the efficacy of local binary pattern (LBP) based texture analysis (TA) of CC is performed to quantify the changes in topographical distribution of callosal fiber connected to different regions of cortex. The extent of highest texture alteration in CC is used for differential diagnosis. Materials and Methods Study included subjects with Parkinson’s disease (PD) (n = 20), and atypical Parkinsonian disorders – multiple system atrophy (MSA) (n = 20), Progressive supranuclear palsy (PSP) (n = 20), and healthy controls (n = 20). For each subject, we have automated the ROI extraction within mid-sagittal CC, followed by LBP TA. Two-class support vector machine (SVM) classification for each disorder as against HC is performed using extracted LBP features like energy and entropy. Correct classification ratio (CCR) is computed as the fraction of correctly classified ROIs at each of the CC sub-regions based on well-known Witelson and Hofer schemes. Based on CCR values, the “Scatter Index (SI)” is proposed to capture how localized (closer to 0) or scattered (closer to 1) the textural changes are among the CC sub-regions, across all subjects per class. The CCR values are further utilized to classify the disease groups. Results Highest alteration of texture is observed in mid-body of CC. The consistency of this finding is quantified using SI for all subjects in a specific class that results more localized textural changes in PSP (15%) and MSA (25%), in comparison to PD (47%). Classification among disease groups results maximum classification accuracy of 90% in classifying PSP from PD-NC. Conclusion Our result demonstrates the efficacy of proposed methodology in analyzing tissue alteration in MRI of Parkinsonian disorders and thus has potential to become valuable tool in computer aided differential diagnosis.

中文翻译：

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基于 LBP 纹理的帕金森病患者胼胝体亚区域表征的新统计框架：一项初步研究

目的 本研究旨在确定与因帕金森病而发生的最高胼胝体组织改变相对应的最佳胼胝体 (CC) 子区域。在这方面，基于局部二值模式 (LBP) 的 CC 纹理分析 (TA) 的功效被执行以量化连接到皮层不同区域的胼胝体纤维的地形分布的变化。CC 中最高纹理改变的程度用于鉴别诊断。材料和方法研究包括患有帕金森病 (PD) (n = 20) 和非典型帕金森病 – 多系统萎缩 (MSA) (n = 20)、进行性核上性麻痹 (PSP) (n = 20) 和健康对照的受试者（n = 20）。对于每个主题，我们在中矢状 CC 中自动提取 ROI，然后是 LBP TA。使用提取的 LBP 特征（如能量和熵）对每种疾病进行两类支持向量机 (SVM) 分类。正确分类率 (CCR) 计算为基于众所周知的 Witelson 和 Hofer 方案在每个 CC 子区域中正确分类的 ROI 的分数。基于 CCR 值，提出了“散布指数 (SI)”来捕捉每个类别的所有主题中 CC 子区域之间的纹理变化是如何局部化（接近 0）或分散（接近 1）的。CCR 值进一步用于对疾病组进行分类。结果在CC的中部观察到最大的纹理变化。这一发现的一致性使用 SI 对特定类别中的所有受试者进行量化，与 PD (47%) 相比，导致 PSP (15%) 和 MSA (25%) 的局部纹理变化更多。在将 PSP 从 PD-NC 中分类时，疾病组之间的分类导致 90% 的最大分类准确度。结论 我们的结果证明了所提出的方法在分析帕金森病 MRI 中的组织改变方面的有效性，因此有可能成为计算机辅助鉴别诊断中的有价值工具。