Computer-aided diagnosis of alzheimer’s disease and mild cognitive impairment with MARS/CMARS classification using structural MR images

Download

index.pdf

Date

2017

Author

Çevik, Alper

Metadata

Show full item record

Item Usage Stats

113
views

29
downloads

Early detection of Alzheimer’s disease (AD) and its prodromal stage, amnestic mild cognitive impairment (MCI), has drawn remarkable attention in recent years. Despite the impressive developments in fields of image analysis, pattern classification, and machine learning, no computer-aided diagnosis system has yet been a part of the clinical routine to diagnose the AD. This thesis study aims to propose a thorough procedure which involves detecting the early signs of disease-originated deformations by fully-automated analysis of structural brain magnetic resonance images (MRI). A comprehensive review including the taxonomy of related biomarkers and state-of-the-art techniques is introduced. Proposed methodology involves extraction of voxel intensity-based features (such as tissue probability maps) through segmenation and registration of brain MRI volumes. Voxel-based morphometry framework is employed to provide one-to-one correspondance between the images. Quality of the feature set is evaluated by an analysis including other approaches such as feature-based morphometry. A novel hybrid procedure involving both statistical analysis and utilization of domain knowledge is proposed for feature selection. Performance of the method is compared with these of well-known dimensionality reduction techniques. Multivariate adaptive regression splines (MARS) and Conic MARS (CMARS) were utilized for construction of the class-separating hyperplanes through a parameter optimization procedure involving cross-validation. This study is the first-time engagement of both MARS and CMARS algorithms in field of medical image analysis. Qualitative and quantitative evaluations of classifier performances were presented including a comparison with benchmark studies in the field. Promising results are acquired through the tests performed on Alzheimer’s Disease Neuroimaging Initiative (ADNI) data.

Subject Keywords

Imaging systems in medicine., Alzheimer's disease., Diagnostic imaging., Machine learning.

URI

http://etd.lib.metu.edu.tr/upload/12621555/index.pdf
https://hdl.handle.net/11511/26970

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Meta analysis of alzheimer’s disease at the gene expression level İzgi, Hamit; Somel, Mehmet; Department of Biology (2017) In this study, publicly available microarray gene expression datasets are used to investigate common gene expression changes in different postmortem brain regions in Alzheimer’s Disease (AD) patients compared to control subjects, and to find possible functional associations related to these changes. The hypothesis is that pathogenesis of the disease converges into common patterns of dysregulation/alteration or dysfunction in molecular pathways across different brain regions in AD. In total, I studied 13 dat...
Transcriptomic network analysis of brain aging and alzheimers disease Parvizi, Poorya; Somel, Mehmet; Tunçbağ, Nurcan; Department of Biology (2017) Multiple studies have investigated aging brain transcriptomes to identify for age-dependent expression changes and determine genes that may participate in age-related dysfunction. However, aging is a highly complex and heterogeneous process where multiple genes contribute at different levels depending on individuals’ environments and genotypes. Both this biological heterogeneity of aging, as well as technical biases and weaknesses inherent to transcriptome measurements, limit the information gained from a s...
Improving the sub-cortical gm segmentation using evolutionary hierarchical region merging Çiftçioğlu, Mustafa Ulaş; Gökçay, Didem; Department of Medical Informatics (2011) Segmentation of sub-cortical Gray Matter (GM) structures in magnetic resonance brain images is crucial in clinic and research for many purposes such as early diagnosis of neurological diseases, guidance of surgical operations and longitudinal volumetric studies. Unfortunately, the algorithms that segment the brain into 3 tissues usually suffer from poor performance in the sub-cortical region. In order to increase the detection of sub-cortical GM structures, an evolutionary hierarchical region merging approa...
Somatic copy number variant load in neurons of healthy controls and Alzheimer’s disease patients Turan, Zeliha Gözde; Richter, Vincent; Bochmann, Jana; Parvizi, Poorya; Yapar, Etka; Işıldak, Ulas; Waterholter, Sarah-Kristin; Leclere-Turbant, Sabrina; Son, Çağdaş Devrim; Duyckaerts, Charles; Yet, İdil; Arendt, Thomas; Somel, Mehmet; Ueberham, Uwe (2022-12-01) The possible role of somatic copy number variations (CNVs) in Alzheimer’s disease (AD) aetiology has been controversial. Although cytogenetic studies suggested increased CNV loads in AD brains, a recent single-cell whole-genome sequencing (scWGS) experiment, studying frontal cortex brain samples, found no such evidence. Here we readdressed this issue using low-coverage scWGS on pyramidal neurons dissected via both laser capture microdissection (LCM) and fluorescence activated cell sorting (FACS) across five...
Understanding the link between alzheimer’s disease and type 2 diabetesin terms of metabolic alterations Lüleci, Hatice Büşra; Çakır, Tunahan (Orta Doğu Teknik Üniversitesi Enformatik Enstitüsü; 2022-10) UNDERSTANDING THE LINK BETWEEN ALZHEIMER’S DISEASE AND TYPE 2 DIABETES IN TERMS OF METABOLIC ALTERATIONS Hatice Büşra Lüleci, Tunahan Çakır Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey ABSTRACT Alzheimer’s disease (AD) is a type of dementia that causes impairment in memory, reasoning, and thinking. Type 2 diabetes (T2D) is common in the general elderly population and is significantly associated with a higher risk of dementia. However, metabolic alterations responsible for this a...

Citation Formats

A. Çevik, “Computer-aided diagnosis of alzheimer’s disease and mild cognitive impairment with MARS/CMARS classification using structural MR images,” Ph.D. - Doctoral Program, Middle East Technical University, 2017.