Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques

2019 ◽  
Vol 43 (9) ◽  
Author(s):  
U. Rajendra Acharya ◽  
Steven Lawrence Fernandes ◽  
Joel En WeiKoh ◽  
Edward J. Ciaccio ◽  
Mohd Kamil Mohd Fabell ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Brain Mri ◽  
Automated Detection ◽  
Extraction Techniques

scholarly journals A Survey on Classification algorithms of Brain Images in Alzheimer’s disease based on Feature Extraction techniques

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ruhul Amin Hazarika ◽  
Arnab Kumar Maji ◽  
Samarendra Nath Sur ◽  
Babu Sena Paul ◽  
Debdatta Kandar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Classification Algorithms ◽  
Extraction Techniques ◽  
Brain Images

scholarly journals Early Recognition of Alzheimer’s Disease using Brain MRI

2017 ◽  
Vol 2 (1) ◽  
pp. 6
Author(s):  
Rania Ahmed Kadry Abdel Gawad Birry
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Euclidean Distance ◽  
Early Recognition ◽  
Brain Mri ◽  
Extraction Techniques ◽  
City Block ◽  
Cosine Distance ◽  
City Block Distance ◽  
The Brain

Abstract—Alzheimer’s disease (AD) is a brain disease that causes a slow decline in memory, thinking and reasoning skills. It represents a major public health problem.  Magnetic Resonance Imaging (MRI) have shown that the brains of people with (AD) shrink significantly as the disease progresses. This shrinkage appears in specific brain regions such as the hippocampus which is a small, curved formation in the brain that plays an important role in the limbic system also involved in the formation of new memories and is also associated with learning and emotions.  Medical information on brain MRI is used in detecting the abnormalities in physiological structures. Structural MRI measurements can detect and follow the evolution of brain atrophy which is a marker of the disease progression; therefore, it allows diagnosis and prediction of AD.  The research’s main target is the early recognition of Alzheimer’s disease automatically, which will thereby avoid deterioration of the case resulting in complete brain damage stage.  Alzheimer’s disease yields visible changes in the brain structures. The aim is to recognize if the patient belongs to Alzheimer’s disease category or a normal healthy person at an early stage. Initially, image pre-processing and features extraction techniques are applied including data reduction using Discrete Cosine Transform (DCT) and Cropping, then traditional classification techniques like Euclidean Distance, Chebyshev Distance, Cosine Distance, City Block Distance, and Black pixel counter, were applied on the resulting vectors for classification. Image pre-processing includes noise reduction, Gray-scale conversion and binary scale conversion were applied for the MRI images. Feature extraction techniques follow including cropping and low spatial frequency components (DCT). This paper aims to automatically recognize and detect Alzheimer’s infected brain using MRI, without the need of clinical expert. This early recognition would be helpful to postpone the disease progression and maintain it at an almost steady stage. It was concluded after collecting a dataset of 50 MRI , 25 for normal MRI and  25 for AD MRI that Chebyshev Distance classifier yielded the highest success rate in the recognition of Alzheimer’s disease with accuracy 94% compared to other classification techniques used where, Euclidean Distance is 91.6%,  Cosine Distance is 86.8%, City block Distance is 89.6%, Correlation Distance is 86.4% and Black pixels counter is 90%.

Statistical Features-Based Diagnosis of Alzheimer's Disease using MRI

2014 ◽  
pp. 38-53 ◽  
Author(s):  
Namita Aggarwal ◽  
Bharti Rana ◽  
R.K. Agrawal
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Brain Slices ◽  
Statistical Features ◽  
Extraction Techniques ◽  
Brain Images ◽  
Appropriate Treatment ◽  
Sensitivity Specificity

Early detection of Alzheimer's Disease (AD), a neurological disorder, may help in development of appropriate treatment to slow down the disease's progression. In this chapter, a method is proposed that may assist in diagnosis of AD using T1 weighted MRI brain images. In the proposed method, first-and-second-order-statistical features were extracted from multiple trans-axial brain slices covering hippocampus and amygdala regions, which play a significant role in AD diagnosis. Performance of the proposed approach is compared with the state-of-the-art feature extraction techniques in terms of sensitivity, specificity, and accuracy. The experiment was carried out on two datasets built from publicly available OASIS data, with four well-known classifiers. Experimental results show that the proposed method outperforms all the other existing feature extraction techniques irrespective of the choice of classifier and dataset. In addition, the statistical test demonstrates that the proposed method is significantly better in comparison to the existing methods. The authors believe that this study will assist clinicians/researchers in classification of AD patients from controls based on T1-weighted MRI.

scholarly journals Case of early-onset Alzheimer’s disease with atypical manifestation

2021 ◽  
Vol 34 (1) ◽  
pp. e100283
Author(s):  
Lin Zhu ◽  
Limin Sun ◽  
Lin Sun ◽  
Shifu Xiao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Manifestation ◽  
Early Onset ◽  
Short Term Memory ◽  
Brain Mri ◽  
Memory Loss ◽  
Amyloid Β ◽  
Amyloid Β Protein ◽  
Fluent Aphasia

Short-term memory decline is the typical clinical manifestation of Alzheimer’s disease (AD). However, early-onset AD usually has atypical symptoms and may get misdiagnosed. In the present case study, we reported a patient who experienced symptoms of memory loss with progressive non-fluent aphasia accompanied by gradual social withdrawal. He did not meet the diagnostic criteria of AD based on the clinical manifestation and brain MRI. However, his cerebrospinal fluid examination showed a decreased level of beta-amyloid 42, and increased total tau and phosphorylated tau. Massive amyloid β-protein deposition by 11C-Pittsburgh positron emission tomography confirmed the diagnosis of frontal variant AD. This case indicated that early-onset AD may have progressive non-fluent aphasia as the core manifestation. The combination of individual and precision diagnosis would be beneficial for similar cases.

scholarly journals Communicating 5-Year Risk of Alzheimer’s Disease Dementia: Development and Evaluation of Materials that Incorporate Multiple Genetic and Biomarker Research Results

2020 ◽  
pp. 1-14
Author(s):  
Jessica Mozersky ◽  
Sarah Hartz ◽  
Erin Linnenbringer ◽  
Lillie Levin ◽  
Marissa Streitz ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Absolute Risk ◽  
Brain Mri ◽  
Community Dwelling ◽  
Educational Materials ◽  
Research Results ◽  
Material Development ◽  
Biomarker Research

Background: Cognitively normal (CN) older adults participating in Alzheimer’s disease (AD) research increasingly ask for their research results—including genetic and neuroimaging findings—to understand their risk of developing AD dementia. AD research results are typically not returned for multiple reasons, including possible psychosocial harms of knowing one is at risk of a highly feared and untreatable disease. Objective: We developed materials that convey information about 5-year absolute risk of developing AD dementia based on research results. Methods: 20 CN older adults who received a research brain MRI result were interviewed regarding their wishes for research results to inform material development (Pilot 1). Following material development, 17 CN older adults evaluated the materials for clarity and acceptability (Pilot 2). All participants were community-dwelling older adults participating in longitudinal studies of aging at a single site. Results: Participants want information on their risk of developing AD dementia to better understand their own health, satisfy curiosity, inform family, and future planning. Some articulated concerns, but the majority wanted to know their risk despite the limitations of information. Participants found the educational materials and results report clear and acceptable, and the majority would want to know their research results after reviewing them. Conclusion: These materials will be used in a clinical study examining the psychosocial and cognitive effects of offering research results to a cohort of CN older adults. Future AD research may incorporate the return of complex risk information to CN older adults, and materials are needed to communicate this information.

scholarly journals Genetic overlap between Alzheimer’s disease and depression mapped onto the brain

2021 ◽  
Author(s):  
Jennifer Monereo Sánchez ◽  
Miranda T. Schram ◽  
Oleksandr Frei ◽  
Kevin O’Connell ◽  
Alexey A. Shadrin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Brain Mri ◽  
Genetic Correlations ◽  
Gaussian Mixture ◽  
Brain Morphology ◽  
Uk Biobank ◽  
Genetic Overlap ◽  
The Uk

ABSTRACTBackgroundAlzheimer’s disease (AD) and depression are debilitating brain disorders that are often comorbid. Shared brain mechanisms have been implicated, yet findings are inconsistent, reflecting the complexity of the underlying pathophysiology. As both disorders are (partly) heritable, characterizing their genetic overlap may provide etiological clues. While previous studies have indicated negligible genetic correlations, this study aims to expose the genetic overlap that may remain hidden due to mixed directions of effects.MethodsWe applied Gaussian mixture modelling, through MiXeR, and conjunctional false discovery rate (cFDR) analysis, through pleioFDR, to genome-wide association study (GWAS) summary statistics of AD (n=79,145) and depression (n=450,619). The effects of identified overlapping loci on AD and depression were tested in 403,029 participants of the UK Biobank (mean age 57.21 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data.ResultsMiXer estimated 98 causal genetic variants overlapping between the two disorders, with 0.44 concordant directions of effects. Through pleioFDR, we identified a SNP in the TMEM106B gene, which was significantly associated with AD (B=-0.002, p=9.1×10−4) and depression (B=0.007, p=3.2×10−9) in the UK Biobank. This SNP was also associated with several regions of the corpus callosum volume anterior (B>0.024, p<8.6×10−4), third ventricle volume ventricle (B=-0.025, p=5.0×10−6), and inferior temporal gyrus surface area (B=0.017, p=5.3×10−4).DiscussionOur results indicate there is substantial genetic overlap, with mixed directions of effects, between AD and depression. These findings illustrate the value of biostatistical tools that capture such overlap, providing insight into the genetic architectures of these disorders.

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