Characterization of Fornix Structural Changes in Controls, MCI, and AD Using sMR Images and Rotational Invariant Moments

IF 2.2 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Letters Pub Date : 2024-12-12 DOI:10.1109/LSENS.2024.3517320

Ahsan Ali;Jac Fredo Agastinose Ronickom;Ramakrishnan Swaminathan

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引用次数: 0

Abstract

Alzheimer's disease (AD) is a prevalent neurodegenerative form of dementia that progressively affects individuals. Alterations in the fornix region are significant biomarkers of AD. In this letter, an approach is proposed to distinguish mild cognitive impairment (MCI) from normal cognition (NC) and AD subjects using structural magnetic resonance (sMR) images and rotational invariant moments (RIMs). Initially, the sMR images are preprocessed using a standard pipeline, and the fornix structure is segmented using the reaction-diffusion level set (RDLS) algorithm. Then, a total of 11 RIMs are computed from the fornix region. Further, the significant features are identified using statistical tests: Shapiro–Wilk, Wilcoxon's rank-sum, and one-way analysis of variance. The results indicate that RDLS effectively segments the fornix structure. The RIM features demonstrate high statistical significance ( p < 0.01) in differentiating MCI from NC and AD, highlighting their ability to capture shape variations in the fornix. The proposed approach effectively differentiates MCI from NC and AD subjects, suggesting its clinical relevance in Alzheimer's condition analysis.

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使用sMR图像和旋转不变矩表征控件、MCI和AD的穹窿结构变化

阿尔茨海默病（AD）是一种普遍的神经退行性痴呆，逐渐影响个体。穹窿区的改变是AD的重要生物标志物。在这篇文章中，提出了一种利用结构磁共振（sMR）图像和旋转不变矩（rim）来区分轻度认知障碍（MCI）与正常认知障碍（NC）和AD受试者的方法。首先，使用标准管道对sMR图像进行预处理，并使用反应扩散水平集（RDLS）算法对穹窿结构进行分割。然后，从穹窿区域计算共11个环。此外，使用统计检验：Shapiro-Wilk， Wilcoxon's秩和和单向方差分析来确定显著特征。结果表明，RDLS能有效分割穹窿结构。RIM特征在区分MCI与NC和AD方面具有很高的统计学意义（p < 0.01），突出了它们在穹窿中捕捉形状变化的能力。该方法有效区分MCI与NC和AD受试者，提示其在阿尔茨海默病状态分析中的临床相关性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Letters Engineering-Electrical and Electronic Engineering

CiteScore

3.50

自引率

7.10%

发文量

194