Gore Sonal, Dhole Aniket, Kumbhar Shrishail, Jagtap Jayant
Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India.
Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), (SIU), Lavale, Pune, Maharashtra, India.
MethodsX. 2023 Sep 5;11:102359. doi: 10.1016/j.mex.2023.102359. eCollection 2023 Dec.
Parkinson's disease (PD) is one of the neurodegenerative diseases and its manual diagnosis leads to time-consuming process. MRI-based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities. 43 subjects from Neurocon and 40 subjects from Tao-Wu dataset were examined, which consisted of 36 scans of healthy controls and 47 scans of Parkinson's patients. Total 360 2D MRI images are selected among around 17000 slices of T1-weighted and resting scans of selected 72 subjects. Local binary pattern (LBP) method was applied with custom variants to acquire advanced textural biomarkers from MRI images. LBP histogram helped to learn discriminative local patterns to detect and classify Parkinson's disease. Using recursive feature elimination, data dimensions of around 150-300 LBP histogram features were reduced to 13-21 most significant features based on score, and important features were analysed using SVM and random forest algorithms. Variant-I of LBP has performed well with highest test accuracy of 83.33%, precision of 84.62%, recall of 91.67%, and f1-score of 88%. Classification accuracies were obtained from 61.11% to 83.33% and AUC-ROC values range from 0.43 to 0.86 using four variants of LBP.•Parkinson's classification is carried out using an advanced biomedical texture feature. Texture extraction using four variants of uniform, rotation invariant LBP method is performed for radiomic analysis of Parkinson's disorder.•Proposed method with support vector machine classifier is experimented and an accuracy of 83.33% is achieved with 10-fold cross validation for detection of Parkinson's patients from MRI-based radiomic analysis.•The proposed predictive model has proved the potential of textures of extended version of LBP, which have demonstrated subtle variations in local appearance for Parkinson's detection.
帕金森病（PD）是一种神经退行性疾病，其人工诊断过程耗时。基于磁共振成像（MRI）的计算机辅助诊断有助于医学专家更精确、快速地诊断帕金森病。对T1加权和静息态模式的3D MRI扫描进行基于纹理的放射组学分析。对来自Neurocon的43名受试者和来自Tao-Wu数据集的40名受试者进行了检查，其中包括36例健康对照扫描和47例帕金森病患者扫描。在选定的72名受试者的约17000个T1加权和静息扫描切片中，总共选择了360幅二维MRI图像。应用具有自定义变体的局部二值模式（LBP）方法从MRI图像中获取先进的纹理生物标志物。LBP直方图有助于学习判别性局部模式以检测和分类帕金森病。使用递归特征消除，基于分数将约150 - 300个LBP直方图特征的数据维度减少到13 - 21个最显著特征，并使用支持向量机（SVM）和随机森林算法分析重要特征。LBP的变体I表现良好，测试准确率最高为83.33%，精确率为84.62%，召回率为91.67%，F1分数为88%。使用LBP的四种变体获得的分类准确率在61.11%至83.33%之间，AUC - ROC值在0.43至0.86之间。
• 使用先进的生物医学纹理特征进行帕金森病分类。对帕金森病进行放射组学分析时，使用均匀、旋转不变LBP方法的四种变体进行纹理提取。
• 对提出的支持向量机分类器方法进行了实验，在基于MRI的放射组学分析中检测帕金森病患者时，通过10折交叉验证实现了83.33%的准确率。
• 所提出的预测模型证明了LBP扩展版本纹理的潜力，这些纹理在帕金森病检测的局部外观上表现出细微变化。
