MR Imaging Texture Analysis of the Corpus Callosum and Thalamus in Amnestic Mild Cognitive Impairment and Mild Alzheimer Disease

Oliveira, Márcia Silva de; Balthazar, Márcio Luiz Figueredo; D’Abreu, Anelyssa; Yasuda, Clarissa L.; Damasceno, Benito Pereira; Cendes, Fernando; Castellano, Gabriela

doi:10.3174/ajnr.a2232

Cited by 93 publications

(86 citation statements)

References 35 publications

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“…For example, texture features can help segment lesions in glioblastoma multiforme [50]. Likewise, abnormal textures in the corpus callosum and thalamus were found to be associated with Alzheimer's disease [51]. Abnormal texture patterns in the pons, midbrain, dentate nucleus, globus pallidus, and corona radiata can also be observed in subjects with Parkinson's disease [52].…”

Section: Discussionmentioning

confidence: 99%

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

et al. 2017

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Background: Emerging evidence suggests the presence of neuroanatomical abnormalities in subjects with autism spectrum disorder (ASD). Identifying anatomical correlates could thus prove useful for the automated diagnosis of ASD. Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue heterogeneity, and for identifying abnormalities related to these differences. However, only a limited number of studies have investigated the link between image texture and ASD. This paper proposes the study of texture features based on grey level co-occurrence matrix (GLCM) as a means for characterizing differences between ASD and development control (DC) subjects. Our study uses 64 T1-weighted MRI scans acquired from two groups of subjects: 28 typical age range subjects 4-15 years old (14 ASD and 14 DC, age-matched), and 36 non-typical age range subjects 10-24 years old (20 ASD and 16 DC). GLCM matrices are computed from manually labeled hippocampus and amygdala regions, and then encoded as texture features by applying 11 standard Haralick quantifier functions. Significance tests are performed to identify texture differences between ASD and DC subjects. An analysis using SVM and random forest classifiers is then carried out to find the most discriminative features, and use these features for classifying ASD from DC subjects. Results:Preliminary results show that all 11 features derived from the hippocampus (typical and non-typical age) and 4 features extracted from the amygdala (non-typical age) have significantly different distributions in ASD subjects compared to DC subjects, with a significance of p < 0.05 following Holm-Bonferroni correction. Features derived from hippocampal regions also demonstrate high discriminative power for differentiating between ASD and DC subjects, with classifier accuracy of 67.85%, sensitivity of 62.50%, specificity of 71.42%, and the area under the ROC curve (AUC) of 76.80% for age-matched subjects with typical age range. Conclusions:Results demonstrate the potential of hippocampal texture features as a biomarker for the diagnosis and characterization of ASD.

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Section: Discussionmentioning

confidence: 99%

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

et al. 2017

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“…Not many studies applied texture analysis on MCI patients. One such study was that of Oliveira et al, [34] where texture analysis was carried out only in the thalamus and corpus callosum of the brain. Because of the small number of subjects (17 MCI, 16 mild AD patients and 16 NC) the segmentation of corpus callosum and the thalamus was carried out manually and 44 texture parameters were extracted.…”

Section: Iiia Classification Of MCI and Ad Studiesmentioning

confidence: 99%

An Overview of Quantitative Magnetic Resonance Imaging Analysis Studies in the Assessment of Alzheimer’s Disease

Leandrou

Petroudi

Kyriacou

et al. 2016

XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016

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“…Furthermore, in neither of these two studies is an analysis with MCI/early-stage AD subjects performed. Other studies have carried out texture analysis in the corpus callosum and thalamus, but the focus is on groupwise analyses rather than the classification of individual subjects [53].…”

Section: Texture Analysismentioning

confidence: 99%

“…As in previous texture studies [52,53], we compute the first eleven Haralick features (according to [126]) at a 3 × 3 × 3 sliding window centered on each brain voxel. This allows for texture analysis in the entire brain rather than at specific ROIs.…”

Section: Calculation Of the Feature Mapsmentioning

confidence: 99%

“…Furthermore, in neither of these two studies was an analysis with MCI patients performed. Other studies have carried out texture analysis in the corpus callosum and thalamus [53]. In all cases, the texture descriptors are computed at manually segmented ROIs, thereby requiring a priori knowledge about the disease and becoming dependent on the quality of the segmentations.…”

Section: Introduction 71mentioning

confidence: 99%

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Towards earlier detection of Alzheimer's disease using magnetic resonance images

Simões¹

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Alzheimer's disease (AD) is the most common type of dementia and a major cause of disability worldwide. Early detection of AD is essential to provide the patients with adequate and timely treatments and to help researchers monitor their effectiveness. Structural Magnetic Resonance Imaging (MRI) is a diagnostic tool that provides high-resolution images and a high brain tissue contrast.MRI-based biomarkers have been investigated in an attempt to describe and quantify structural differences between groups of normal elderly controls and subjects suffering from AD. Additionally, classification methods have been proposed that use these biomarkers as features to distinguish between those groups, thereby also providing diagnostic value.Two main approaches have been extensively explored in the past decades to perform early-stage AD classification based on structural MR images. The first uses the volume and/or the shape of specific brain structures, such as the hippocampi and the entorhinal cortex. As a consequence, these methods rely substantially on the quality of: 1) the assumptions of which brain regions are affected at an early stage of AD; 2) the segmentation of these brain structures, which suffers from large variability across studies. Another major line of research overcomes the first drawback by using voxelwise measures, such as the probability maps of the brain tissues. However, these methods require a voxelwise inter-subject correspondence, which is difficult to achieve, particularly considering the large anatomical variability of the brain across different subjects.Besides the above-mentioned disadvantages of these two approaches, they both focus on structural (volume, shape, density) changes only. It has recently been considered that also the MR image intensities and textures can provide complementary information that is overlooked by the structural-based features.In this thesis, we propose methods to help diagnose AD at an early stage of development. In particular, we build on the existing literature on classification approaches that use MR image textures for early detection of AD.vi Firstly, we focus our analysis on a type of lesions in the white matter (white matter hyperintensities) that have been shown to play a role in cognitive decline. We propose a method to automatically segment these lesions from a single MRI modality that can be suitable for large-scale clinical trials. We show that our method, despite using less information, performs similarly to current state-of-the-art multimodal approaches. Afterwards, we evaluate the performance of white matter lesion texture descriptors in the detection of Mild Cognitive Impairment (MCI, a transitional stage between normal ageing and dementia). Results show that the textures are more discriminative than the widely used lesion volumes and locations.Secondly, we evaluate three approaches that use texture descriptors without requiring prior brain structure segmentations. The first one takes the graylevel histograms computed in the whole brain and in sev...

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MR Imaging Texture Analysis of the Corpus Callosum and Thalamus in Amnestic Mild Cognitive Impairment and Mild Alzheimer Disease

Cited by 93 publications

References 35 publications

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

An Overview of Quantitative Magnetic Resonance Imaging Analysis Studies in the Assessment of Alzheimer’s Disease

Towards earlier detection of Alzheimer's disease using magnetic resonance images

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