Non-Invasive Imaging and Diagnostic Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, 600036, India.
Department of Mathematics, Indian Institute of Technology Madras, 600036, India.
Comput Methods Programs Biomed. 2019 May;173:147-155. doi: 10.1016/j.cmpb.2019.03.003. Epub 2019 Mar 7.
Brainstem analysis in Magnetic Resonance Images is essential to detect Alzheimer's condition in the preclinical stages. In this work, an attempt has been made to segment the brainstem in sagittal (2D) and volumetric (3D) images and evaluate texture changes to differentiate Alzheimer's disease (AD) stages.
The images obtained from a public access database are spatial normalized, skull stripped and contrast enhanced. Morphological Reconstruction based Fast and Robust Fuzzy 'C' Means technique is used to cluster the brain tissue in preprocessed images into three groups namely cerebrospinal fluid, grey matter and white matter. Brainstem is segmented from the white matter tissue using connected component labelling. Texture features from volumetric and sagittal brainstem slices are extracted and its statistical significance is evaluated.
Results show that the proposed approach is able to segment the brainstem from all the considered images. Variation in texture is observed to be less than 2% among sagittal brainstem slices. Additionally, midsagittal and volumetric features are correlated, suggesting that midsagittal brainstem structure gives an estimate of brainstem volume. Texture features extracted from midsagittal slice shows significant variation (p < 0.05) and is able to differentiate AD classes.
Midsagittal brainstem texture features are able to capture the changes occurring in the early stages of disease condition. As the distinction of AD in preclinical stage is complex and clinically significant, this approach could be useful for early diagnosis of the disease.
磁共振成像中的脑干分析对于在临床前阶段检测阿尔茨海默病（AD）至关重要。在这项工作中，我们尝试对矢状（2D）和容积（3D）图像进行脑干分割，并评估纹理变化以区分 AD 阶段。
从公共访问数据库中获取的图像进行空间归一化、颅骨剥离和对比度增强。基于形态重建的快速稳健模糊“C”均值技术用于将预处理图像中的脑组织聚类为三个组，即脑脊液、灰质和白质。使用连通分量标记从白质组织中分割出脑干。从容积和矢状脑干切片中提取纹理特征，并评估其统计学意义。
结果表明，所提出的方法能够分割所有考虑的图像中的脑干。观察到矢状脑干切片之间的纹理变化小于 2%。此外，正中矢状面和容积特征相关，表明正中矢状面脑干结构可以估计脑干体积。从中矢状切片提取的纹理特征显示出显著的变化（p<0.05），并且能够区分 AD 类别。
中矢状面脑干纹理特征能够捕捉到疾病早期发生的变化。由于在临床前阶段区分 AD 较为复杂且具有临床意义，因此这种方法可能有助于疾病的早期诊断。
