Alzheimer’s Detection Based on Segmentation of MRI Image

Biju, K. S.; Alfa, S.S.; Lal, Kavya; Antony, A. Joseph; Akhil, M Kurup

doi:10.1016/j.procs.2017.09.088

Cited by 33 publications

(10 citation statements)

References 8 publications

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“…Biju K Sa et al, [1] provided the software solution of for detecting the Alzheimer's disease and brain abnormalities as well and produced a 3D representation of MRI slices. The study collected and analyzed various parameters from MRI dataset like cortex area, grey matter volume, white matter volume, and cavity area and brain density.…”

Section: Prasad Et Al[8]mentioning

confidence: 99%

Classification of Alzheimer Using fMRI Data and Brain Network

Yadav¹,

Gautam²,

Mishra³

2018

Computer Science &Amp; Information Technology

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Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing attention of neuroscientists and computer scientists, since it opens a new window to explore functional network of human brain with relatively high resolution. BOLD technique provides almost accurate state of brain. Past researches prove that neuro diseases damage the brain network interaction, protein-protein interaction and gene-gene interaction. A number of neurological research paper also analyse the relationship among damaged part. By computational method especially machine learning technique we can show such classifications. In this paper we used OASIS fMRI dataset affected with Alzheimer's disease and normal patient's dataset. After proper processing the fMRI data we use the processed data to form classifier models using SVM (Support Vector Machine), KNN (K-nearest neighbour) & Naïve Bayes. We also compare the accuracy of our proposed method with existing methods. In future, we will other combinations of methods for better accuracy.

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Section: Prasad Et Al[8]mentioning

confidence: 99%

Classification of Alzheimer Using fMRI Data and Brain Network

Yadav¹,

Gautam²,

Mishra³

2018

Computer Science &Amp; Information Technology

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“…Biju et al [5] defined specific ratios obtained by processing MRI scans pertaining to which the state of the brain can be classified into normal, 1 st stage Alzheimer's, or 2 nd stage Alzheimer's. It follows a method of calculation of WM and GM present in three cross sections of the brain visualized through MR images [6], namely, axial, coronal and sagittal cross sections.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis

Bhavana

Kariputtaiah²

2023

IJECE

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In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.

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“…Hence, these brain imaging tools can be used for diagnosing the AD [19] [20]. By segmenting the affected tissues in the brain images, it is possible to observe the neurological changes that occur in AD, and the information can be used to train a classifier to classify AD more accurately [21] [22].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques

et al. 2021

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Alzheimer's disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients and the paucity of proper diagnostic tools, detection and classification of AD are considered as a challenging research area. Before a Cognitively normal (CN) person develops symptoms of AD, he may pass through an intermediate stage, commonly known as Mild Cognitive Impairment (MCI). MCI is having two stages, namely StableMCI (SMCI) and Progressive MCI (PMCI). In SMCI, a patient remains stable, whereas, in the case of PMCI, a person gradually develops few symptoms of AD. Several research works are in progress on the detection and classification of AD based on changes in the brain. In this paper, we have analyzed few existing state-of-art works for AD detection and classification, based on different feature extraction approaches. We have summarized the existing research articles with detailed observations. We have also compared the performance and research issues in each of the feature extraction mechanisms and observed that the AD classification using the wavelet transform-based feature extraction approaches might achieve convincing results.

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Alzheimer’s Detection Based on Segmentation of MRI Image

Cited by 33 publications

References 8 publications

Classification of Alzheimer Using fMRI Data and Brain Network

Classification of Alzheimer Using fMRI Data and Brain Network

Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis

A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques

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