Characterization of Alzheimer conditions in MR images using volumetric and sagittal brainstem texture features. (May 2019)

• Record Type: Journal Article
• Authors: Rohini, P.; Sundar, S.; Ramakrishnan, S.
• Is Part Of: Computer methods and programs in biomedicine. Volume 173(2019)
• Page Start: 147
• Page End: 155
• Publication Date: 2019-05
• Subjects: Alzheimer's disease -- Brainstem -- Morphological Reconstruction based Fast and Robust Fuzzy 'C' Means -- Connected Component Labelling -- Texture feature; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.03.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Brainstem analysis in Magnetic Resonance Images is essential to detect Alzheimer's condition in the preclinical stages. Automated segmentation of Brainstem structure using Morphological reconstruction based Fuzzy 'C' Means and Connected Component Labelling. Texture variation among brainstem sagittal slices is observed to be less than 2%. Correlation between volumetric and midsagittal features suggests midsagittal brainstem structure gives an estimate of brainstem volume. Midsagittal brainstem texture features are able to capture the changes occurring in the early stages of disease condition. Abstract: Background and Objective: 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. Method: 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: 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. Conclusion: 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.