Mild Cognitive Impairment (MCI) is the asymptomatic, preclinical transitional stage among aging and Alzheimer’s Disease (AD). Detection of MCI can ensure the timely intervention required to manage the disease’s severity. Morphological alterations of Lateral Ventricle (LV) is considered as a significant biomarker for disease diagnosis. This research aims to analyze the shape alterations of the LV region using Fractional Order Jacobi Fourier Moment (FOJFM) features, which are categorized by their generic nature and capabilities to perform time-frequency analysis. T1-weighted transaxial view brain MR images (HC = 92 and MCI = 63) are obtained from publicly available Open Access Series of Imaging Studies (OASIS) database. The LV region is delineated using Weighted Level Set (WLS) segmentation method and results are compared to Ground Truth (GT) images. FOJFM features are employed to characterize the morphometry of LV region. From this segmented region, 200 features are computed by varying the value of order and fractional parameters. Random Forest (RF) and Support Vector Machine (SVM) classifiers are used to differentiate Healthy Control (HC) and MCI subjects. Results show that WLSE is able to delineate the LV structure. The segmented region shows good correlation with the GT area. FOJFM features are observed to be statistically significant in discriminating HC and MCI subjects with p<0.05. For MCI subjects, the feature values show higher variation as compared with HC brain, which might be due to the surface expansion of ventricular area during disease progression. SVM and RF classifiers show high performance F-measure values of 93.14% and 86.24%, respectively, for differentiating MCI conditions. The proposed moment based FOJFM features are able to capture the morphological changes of LV region related to MCI condition. Hence the proposed pipeline of work can be useful for the automated and early diagnosis of diseased conditions
© 2021 The Author(s), published by Walter de Gruyter GmbH, Berlin/Boston
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