지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.7 No.2
- 발행연도
- 2018.4
- 수록면
- 124 - 131 (8page)
- DOI
- 10.5573/IEIESPC.2018.7.2.124
이용수
초록· 키워드
오류제보하기
The current diagnostic evaluation criteria for Alzheimer’s disease (AD) are restricted to cognitive deficits, and less consideration is given to AD-related noncognitive, behavioral, and psychological symptoms of dementia. Vascular deterioration in AD subjects manifests neuropsychiatric symptoms (NPS). Early anxiety and depression are associated with cognitive decline. The brainstem undergoes deformation during the initiation of AD due to NPS. In this work, structural deformation of the brainstem is analyzed using Lattice Boltzmann criterion-based hybrid level set method (LSM) and fourth-order diffusion filtering. Contour and skeletal-based shape measures are extracted and analyzed using multilayer perceptron (MLP). Results show that the fourth-order diffusion filter provides strong edges, which aids in accurate segmentation. The Lattice Boltzmann criterion-based hybrid LSM is able to delineate the whole brainstem. Hough transform statistics (HTS) obtained from the segmented brainstem describe the geometric properties of the boundary of the brainstem. Eigen space values identify the skeletal variations of the brainstem for normal, Mild Cognitive Impairment (MCI), and AD subjects. Both HTS and eigen space features are found to represent better differentiation between normal, MCI, and AD subjects with a significance of p＜0.05. It is observed that Hough transform–based statistical values have the ability to better discriminate the structural deformity of the brainstem with classification accuracies of 73%, 68%, and 71% for normal–MCI, MCI–AD, and normal–AD classifications, respectively, using MLP. Thus, this analysis identifies the presence of structural abnormality in the brainstem during the progression of AD, and therefore, this analysis could aid in early diagnosis of AD.
목차
Abstract
1. Introduction
2. Methodology
3. Results
4. Conclusion
References
참고문헌 (16)
참고문헌 신청
[학술지(정기간행물)] - M. C. Masters / 2016 / Noncognitive symptoms of early Alzheimer disease A longitudinal analysis / Neurology 84 (6) : 617 ~ 622
[학술지(정기간행물)] - B. P Forester / 2003 / Measures to assess the noncognitive symptoms of dementia in the primary care setting / Primary care companion to the Journal of clinical psychiatry 5 (4) : 158 ~ 163
[보고서] - M. Prince / 2016 / World Alzheimer report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future
[학술지(정기간행물)] - Z. Ismail / 2016 / Neuropsychiatric symptoms as early manifestations of emergent dementia: provisional diagnostic criteria for mild behavioral impairment / Alzheimer's & dementia: the journal of the Alzheimer's Association 12 (2) : 195 ~ 202
[학술지(정기간행물)] - J. H. Lee / 2015 / Brainstem morphological changes in Alzheimer's disease / Neuroreport 26 (7) : 411 ~ 415
[학술지(정기간행물)] - B. P Forester / 2003 / Measures to assess the noncognitive symptoms of dementia in the primary care setting / Primary care companion to the Journal of clinical psychiatry 5 (4) : 158 ~ 163
[보고서] - M. Prince / 2016 / World Alzheimer report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future
[학술지(정기간행물)] - Z. Ismail / 2016 / Neuropsychiatric symptoms as early manifestations of emergent dementia: provisional diagnostic criteria for mild behavioral impairment / Alzheimer's & dementia: the journal of the Alzheimer's Association 12 (2) : 195 ~ 202
[학술지(정기간행물)] - J. H. Lee / 2015 / Brainstem morphological changes in Alzheimer's disease / Neuroreport 26 (7) : 411 ~ 415
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Diagnosis of Alzheimer "s Disease using Combined Featur e Selection Method
멀티미디어학회논문지
2021 .05
-
Single-cell RNA sequencing reveals B cell?related molecular biomarkers for Alzheimer’s disease
Experimental and Molecular Medicine
2021 .12
-
생성적 적대 신경망 기반의 3D 뇌 영상을 이용한 알츠하이머 질병 예측 모델
대한전자공학회 학술대회
2023 .06
-
An Approach to Differentiate Alzheimer’s Conditions using MR Image–based Zernike Moments and Fractal Features
IEIE Transactions on Smart Processing & Computing
2018 .06
-
Deep and Statistical-Based Methods for Alzheimer’s Disease Detection: A Survey
Journal of Computing Science and Engineering
2022 .03
-
Hippocampus Segmentation and Classification in Alzheimer ’s Disease and Mild Cognitive Impairment Applied on MR Images
멀티미디어학회논문지
2017 .01
-
Optimizing Content Duration for Mobile Ads
IEIE Transactions on Smart Processing & Computing
2016 .08
-
멀티오믹스 데이터를 활용한 알츠하이머 질병 예측 딥러닝 모델 개발 연구
한국정보기술학회논문지
2022 .07
-
경기도 초등학교 학생수 변화 경향에 대한 조사 연구
교육녹색환경연구
2021 .01
-
Exploring the possibility for early detection of Alzheimer’s disease with spatial-domain neural images
제어로봇시스템학회 국제학술대회 논문집
2020 .10
-
치매 진단을 위한 MRI 바이오마커 패치 영상 기반 3차원 심층합성곱신경망 분류 기술
멀티미디어학회논문지
2020 .08
-
Peripheral inflammatory biomarkers in Alzheimer’s disease: a brief review
BMB Reports
2020 .01
-
Stem cell therapy for Alzheimer’s disease and related disorders: current status and future perspectives
Experimental and Molecular Medicine
2015 .01
-
지방자치단체 광고효용성에 대한 탐색적 연구: KTX 광고노출 환경을 중심으로
멀티미디어학회논문지
2020 .01
-
Mitochondrial dysfunction and Alzheimer’s disease: prospects for therapeutic intervention
BMB Reports
2020 .01
-
Recent Advances in Photoelectrochemical Sensing of Alzheimer’s Biomarkers
BioChip Journal
2023 .06
-
An Implementation of Effective CNN Model for AD Detection
스마트미디어저널
2024 .06
-
Transformer based Alzheimer’s Disease prediction using Whole Brain MRI
대한전자공학회 학술대회
2022 .06
-
Classification of Alzheimer Disease by Combining Hippocampus Volume and Cortical Thickness from sMRI, Plus APoE ε4 and CSF Biomarkers
IEIE Transactions on Smart Processing & Computing
2021 .02
-
일 종합병원 외래환자와 보호자의 사전의료의향서에대한 지식, 인식 및 태도
보건정보통계학회지
2015 .01
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2018-569-002044336