Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding

Jie Zhang, Yonghui Fan, Qingyang Li, Paul M. Thompson, Jieping Ye, Yalin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Cortical thickness estimation performed in vivo via magnetic resonance imaging (MRI) is an important technique for the diagnosis and understanding of the progression of Alzheimer's disease (AD). Directly using raw cortical thickness measures as features with Support Vector Machine (SVM) for clinical group classification only yields modest results since brain areas are not equally atrophied during AD progression. Therefore, feature reduction is generally required to retain only the most relevant features for the final classification. In this paper, a spherical sparse coding and dictionary learning method is proposed and it achieves relatively high classification results on publicly available data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 dataset (N = 201) which contains structural MRI data of four clinical groups: cognitive unimpaired (CU), early mild cognitive impairment (EMCI), later MCI (LMCI) and AD. The proposed framework takes the estimated cortical thickness and the spherical parameterization computed by FreeSurfer as inputs and constructs weighted patches in the spherical parameter domain of the cortical surface. Then sparse coding is applied to the resulting surface patch features, followed by max-pooling to extract the final feature sets. Finally, SVM is employed for binary group classifications. The results show the superiority of the proposed method over other cortical morphometry systems and offer a different way to study the early identification and prevention of AD.

Original languageEnglish (US)
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Pages446-450
Number of pages5
ISBN (Electronic)9781509011711
DOIs
StatePublished - Jun 15 2017
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: Apr 18 2017Apr 21 2017

Other

Other14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
CountryAustralia
CityMelbourne
Period4/18/174/21/17

Fingerprint

Alzheimer Disease
Magnetic resonance
Support vector machines
Magnetic Resonance Imaging
Neuroimaging
Imaging techniques
Glossaries
Parameterization
Disease Progression
Brain
Learning
Support Vector Machine

Keywords

  • Alzheimer's Disease
  • Cortical Thickness
  • Sparse Coding
  • Support Vector Machine (SVM)
  • Weighted Spherical Harmonics

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Zhang, J., Fan, Y., Li, Q., Thompson, P. M., Ye, J., & Wang, Y. (2017). Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 446-450). [7950557] IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950557

Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding. / Zhang, Jie; Fan, Yonghui; Li, Qingyang; Thompson, Paul M.; Ye, Jieping; Wang, Yalin.

2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. p. 446-450 7950557.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhang, J, Fan, Y, Li, Q, Thompson, PM, Ye, J & Wang, Y 2017, Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding. in 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017., 7950557, IEEE Computer Society, pp. 446-450, 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, 4/18/17. https://doi.org/10.1109/ISBI.2017.7950557
Zhang J, Fan Y, Li Q, Thompson PM, Ye J, Wang Y. Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society. 2017. p. 446-450. 7950557 https://doi.org/10.1109/ISBI.2017.7950557
Zhang, Jie ; Fan, Yonghui ; Li, Qingyang ; Thompson, Paul M. ; Ye, Jieping ; Wang, Yalin. / Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding. 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. pp. 446-450
@inproceedings{ded5af0adb6e438cbe56efec853d796e,
title = "Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding",
abstract = "Cortical thickness estimation performed in vivo via magnetic resonance imaging (MRI) is an important technique for the diagnosis and understanding of the progression of Alzheimer's disease (AD). Directly using raw cortical thickness measures as features with Support Vector Machine (SVM) for clinical group classification only yields modest results since brain areas are not equally atrophied during AD progression. Therefore, feature reduction is generally required to retain only the most relevant features for the final classification. In this paper, a spherical sparse coding and dictionary learning method is proposed and it achieves relatively high classification results on publicly available data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 dataset (N = 201) which contains structural MRI data of four clinical groups: cognitive unimpaired (CU), early mild cognitive impairment (EMCI), later MCI (LMCI) and AD. The proposed framework takes the estimated cortical thickness and the spherical parameterization computed by FreeSurfer as inputs and constructs weighted patches in the spherical parameter domain of the cortical surface. Then sparse coding is applied to the resulting surface patch features, followed by max-pooling to extract the final feature sets. Finally, SVM is employed for binary group classifications. The results show the superiority of the proposed method over other cortical morphometry systems and offer a different way to study the early identification and prevention of AD.",
keywords = "Alzheimer's Disease, Cortical Thickness, Sparse Coding, Support Vector Machine (SVM), Weighted Spherical Harmonics",
author = "Jie Zhang and Yonghui Fan and Qingyang Li and Thompson, {Paul M.} and Jieping Ye and Yalin Wang",
year = "2017",
month = "6",
day = "15",
doi = "10.1109/ISBI.2017.7950557",
language = "English (US)",
pages = "446--450",
booktitle = "2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017",
publisher = "IEEE Computer Society",
address = "United States",

}

TY - GEN

T1 - Empowering cortical thickness measures in clinical diagnosis of Alzheimer's disease with spherical sparse coding

AU - Zhang, Jie

AU - Fan, Yonghui

AU - Li, Qingyang

AU - Thompson, Paul M.

AU - Ye, Jieping

AU - Wang, Yalin

PY - 2017/6/15

Y1 - 2017/6/15

N2 - Cortical thickness estimation performed in vivo via magnetic resonance imaging (MRI) is an important technique for the diagnosis and understanding of the progression of Alzheimer's disease (AD). Directly using raw cortical thickness measures as features with Support Vector Machine (SVM) for clinical group classification only yields modest results since brain areas are not equally atrophied during AD progression. Therefore, feature reduction is generally required to retain only the most relevant features for the final classification. In this paper, a spherical sparse coding and dictionary learning method is proposed and it achieves relatively high classification results on publicly available data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 dataset (N = 201) which contains structural MRI data of four clinical groups: cognitive unimpaired (CU), early mild cognitive impairment (EMCI), later MCI (LMCI) and AD. The proposed framework takes the estimated cortical thickness and the spherical parameterization computed by FreeSurfer as inputs and constructs weighted patches in the spherical parameter domain of the cortical surface. Then sparse coding is applied to the resulting surface patch features, followed by max-pooling to extract the final feature sets. Finally, SVM is employed for binary group classifications. The results show the superiority of the proposed method over other cortical morphometry systems and offer a different way to study the early identification and prevention of AD.

AB - Cortical thickness estimation performed in vivo via magnetic resonance imaging (MRI) is an important technique for the diagnosis and understanding of the progression of Alzheimer's disease (AD). Directly using raw cortical thickness measures as features with Support Vector Machine (SVM) for clinical group classification only yields modest results since brain areas are not equally atrophied during AD progression. Therefore, feature reduction is generally required to retain only the most relevant features for the final classification. In this paper, a spherical sparse coding and dictionary learning method is proposed and it achieves relatively high classification results on publicly available data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 dataset (N = 201) which contains structural MRI data of four clinical groups: cognitive unimpaired (CU), early mild cognitive impairment (EMCI), later MCI (LMCI) and AD. The proposed framework takes the estimated cortical thickness and the spherical parameterization computed by FreeSurfer as inputs and constructs weighted patches in the spherical parameter domain of the cortical surface. Then sparse coding is applied to the resulting surface patch features, followed by max-pooling to extract the final feature sets. Finally, SVM is employed for binary group classifications. The results show the superiority of the proposed method over other cortical morphometry systems and offer a different way to study the early identification and prevention of AD.

KW - Alzheimer's Disease

KW - Cortical Thickness

KW - Sparse Coding

KW - Support Vector Machine (SVM)

KW - Weighted Spherical Harmonics

UR - http://www.scopus.com/inward/record.url?scp=85023174597&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85023174597&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2017.7950557

DO - 10.1109/ISBI.2017.7950557

M3 - Conference contribution

AN - SCOPUS:85023174597

SP - 446

EP - 450

BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017

PB - IEEE Computer Society

ER -