• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.667-675
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• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.233-242
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• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.29-38
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• 1997

Dementia is defined as a syndrome which is characterized by various impairments in cognitive functions, especially memory function. Most of the diagnostic criteria for dementia include memory impairment as on essential feature. Memory decline can be present as a consequence of the aging process. But it does not cause significant distress or impairment in social and occupational functionings while dementiadoes. Depression may also be associated with memory impairment. However, unlike dementia, depression dose not cause decrease in delayed verbal learning and recognition memory. In dementia, different features of memory impairment may be present depending on the involved area. Memory impairment in cortical dementia is affected by the disturbance of encoding of information and memory consolidation, while memory imparnene in subcortical denentiy is affected by the disturbance of retrieval in subcortical dementia.

• Journal of Biomedical Engineering Research
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• v.26 no.6
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• pp.383-391
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• 2005

Age-related White Matter Changes (WMC) on Magnetic Resonance Imaging (MRI) are known to appear frequently in Multiple sclerosis (MS) and Alzheimer's disease and to be related to cognitive impairment. The characterization of these WMC is very important to the study of psychology and aging. These changes consist of periventricular and subcortical types, however it is difficult to detect and segment WMC using only intensity-based methods, because their intensity, level IS similar to th~t of the gray matter (GM). In this paper, we propose a new method of segmenting periventricular WMC using K-means clustering and morphological features.

• Smart Media Journal
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• v.12 no.7
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• pp.52-58
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• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• Smart Media Journal
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• v.12 no.3
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.101-108
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• 2021

Purpose: To identify characteristic magnetic resonance imaging (MRI) features to differentiate between Krabbe disease and metachromatic leukodystrophy (MLD) in young children. Materials and Methods: We collected all confirmed cases of Krabbe disease and MLD between October 2004 and September 2020 at Seoul National University Children's Hospital. Patients with initial MRI available were included. Their initial MRIs were retrospectively reviewed for the following: 1) presence of white matter signal abnormality involving the periventricular and deep white matter, subcortical white matter, internal capsule, brainstem, and cerebellum; 2) presence of volume decrease and signal alteration in the corpus callosum and thalamus; 3) presence of the tigroid sign; 4) presence of optic nerve hypertrophy; and 5) presence of enhancement or diffusion restriction. Results: Eleven children with Krabbe disease and 12 children with MLD were included in this study. There was no significant difference in age or symptoms at onset. Periventricular and deep white matter signal alterations sparing the subcortical white matter were present in almost all patients of the two groups. More patients with Krabbe disease had T2 hyperintensities in the internal capsule and brainstem than patients with MLDs. In contrast, more patients with MLD had T2 hyperintensities in the splenium and genu of the corpus callosum. No patient with Krabbe disease showed T2 hyperintensity in the corpus callosal genu. A decrease in volume in the corpus callosum and thalamus was more frequently observed in patients with Krabbe disease than in those with MLD. Other MRI findings including the tigroid sign and optic nerve hypertrophy were not significantly different between the two groups. Conclusion: Signal abnormalities in the internal capsule and brainstem, decreased thalamic volume, decreased splenial volume accompanied by signal changes, and absence of signal changes in the callosal genu portion were MRI findings suggestive of Krabbe disease rather than MLD based on initial MRI. Other MRI findings such as the tigroid sign could not help differentiate between these two diseases.

• Journal of Korean Neurosurgical Society
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• v.30 no.5
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• pp.622-626
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• 2001

Acute disseminated encephalomyelitis(ADEM) is an uncommon immune-mediated inflammatory demyelinating disorder that typically affects the white matter of the central nervous system. Radiologic findings of acute disseminated encephalomyelitis are not pathognomomic. The differential diagnosis is always difficult. Occasionally, the clinical features, radiological and histopathological findings of patients with acute disseminated encephalomyelitis mimic the brain tumor or other space occupying lesions. The authors report a 6-year-old girl who presented with right hemiparesis two days after nausea and vomiting. Brain MRI of the patient revealed non-enhanced multiple cystic lesions in subcortical white matter of both cerebral hemisphere with prominent edema. One of the cystic lesions was resected to differentiate with metastatic tumor or inflammatory disease such as abscess and confirmed as the acute disseminated encephalomyelitis via various immunohistochemical stains. Pertinent literature is reviewed with discussion on this uncommon ADEM associated with multiple cystic lesions.

• Annals of Clinical Neurophysiology
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• v.4 no.1
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• pp.21-27
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• 2002

Background : Spatial analysis of EEG is a phenomenal assessment and not so informative for phase space and dynamic aspect of EEG data. In contrast, nonlinear EEG analysis attempts to characterize the dynamics of neural networks in the brain. We have analyzed the features of EEG nonlinearly in subjects with white matter change on brain MRI and compared the results with cognitive function in each. Methods : Digital EEG data were taken for 30 seconds in 9 subjects with white matter degeneration and in 5 healthy normal controls without white matter change on MRI. Then we analyzed them nonlinearly to calculate the correlation dimension(D2) using the MATLAB software. The cognitive function was assessed by 3MS(modified mini-mental state examination). The severity of white matter change was assessed by Scheltens scale. Results : The mean D2 value of normal control was greater than that of white matter degeneration group. The D2s of some channels were correlative with 3MS and degree of white matter degeneration significantly. Conclusions : nonlinear analysis of EEG can be used as one of adjuvant functional studies for prediction of cognitive impairment in subjects with white matter degeneration and subcortical white matter change can be influential on cognitive function and correlation dimension of EEG.

• Investigative Magnetic Resonance Imaging
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• v.21 no.3
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• pp.125-130
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• 2017

Purpose: To evaluate the clinical significance of T1 high signal intensity on the globus pallidus as a predictor of severe hepatic encephalopathy in patients with acute-on-chronic liver failure (ACLF), which is a distinct syndrome characterized by multi-organ dysfunction including cerebral failure. Materials and Methods: From January 2002 to April 2014, we retrospectively reviewed the magnetic resonance imaging (MRI) findings and clinical and magnetic resonance (MR) features of 74 consecutive patients (44 men and 30 women; mean age, 59.5 years) with liver cirrhosis. The chronic liver failure-sequential organ failure assessment score was used to diagnose ACLF. The pallidal index (PI), calculated by dividing the mean signal intensity of the globus pallidus by that of the subcortical frontal white matter were compared according to ACLF. The PI was compared with the Model for End-Stage Liver Disease (MELD) score in predicting the development of ACLF. Results: Fifteen patients who were diagnosed with ACLF had higher hepatic encephalopathy grades (initial, P = 0.024; follow-up, P = 0.002), MELD scores (P < 0.001), and PI (P = 0.048). In the ACLF group, the mean PI in patients with cerebral failure was significantly higher than that in the patients without cerebral failure (1.33 vs. 1.20, P = 0.039). In patients with ACLF, the area under the curve (AUC) for PI was 0.680 (95% confidence intervals [CI], 0.52-0.85), which was significantly lower than that for the MELD score (AUC, 0.88; 95% CI, 0.77-0.99) (P = 0.04). Conclusion: The PI can be an ancillary biomarker for predicting the development of ACLF and severe hepatic encephalopathy.