An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map

Shih Ting Yang; Jiann Der Lee; Chung Hsien Huang; Jiun Jie Wang; Wen Chuin Hsu; Yau Yau Wai

doi:10.1007/978-3-642-17534-3_57

An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map

Shih Ting Yang^*, Jiann Der Lee, Chung Hsien Huang, Jiun Jie Wang, Wen Chuin Hsu, Yau Yau Wai

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Mild cognitive impairment (MCI) is considered as a transitional stage between normal aging and dementia. MCI has a high risk to convert into Alzheimer's disease (AD). In the related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, we designed a MRI-based classification framework to distinguish the patients of MCI and AD from normal individuals. First, volumetric features and shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a Self-organizing map classifier was trained for patient classification. By combining the volumetric features and shape features, the classification accuracy is reached to 86.76%, 66.67%, and 46.67% in AD, amnestic MCI (aMCI), and dysexecutive MCI (dMCI), respectively. In addition, with the help of PCA process, the classification result is improved to 93.63%, 73.33%, and 53.33% in AD, aMCI and dMCI, respectively.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	Models and Applications - 17th International Conference, ICONIP 2010, Proceedings
Pages	462-469
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-17534-3_57
State	Published - 2010
Event	17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia Duration: 22 11 2010 → 25 11 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6444 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Neural Information Processing, ICONIP 2010
Country/Territory	Australia
City	Sydney, NSW
Period	22/11/10 → 25/11/10

Keywords

Alzheimer's disease
Magnetic resonance imaging
Mild cognitive impairment
Principle component analysis
Self-organizing map
Shape descriptors

Access to Document

10.1007/978-3-642-17534-3_57

Cite this

Yang, S. T., Lee, J. D., Huang, C. H., Wang, J. J., Hsu, W. C., & Wai, Y. Y. (2010). An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map. In Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings (PART 2 ed., pp. 462-469). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6444 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-17534-3_57

Yang, Shih Ting ; Lee, Jiann Der ; Huang, Chung Hsien et al. / An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map. Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings. PART 2. ed. 2010. pp. 462-469 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map",

abstract = "Mild cognitive impairment (MCI) is considered as a transitional stage between normal aging and dementia. MCI has a high risk to convert into Alzheimer's disease (AD). In the related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, we designed a MRI-based classification framework to distinguish the patients of MCI and AD from normal individuals. First, volumetric features and shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a Self-organizing map classifier was trained for patient classification. By combining the volumetric features and shape features, the classification accuracy is reached to 86.76%, 66.67%, and 46.67% in AD, amnestic MCI (aMCI), and dysexecutive MCI (dMCI), respectively. In addition, with the help of PCA process, the classification result is improved to 93.63%, 73.33%, and 53.33% in AD, aMCI and dMCI, respectively.",

keywords = "Alzheimer's disease, Magnetic resonance imaging, Mild cognitive impairment, Principle component analysis, Self-organizing map, Shape descriptors",

author = "Yang, {Shih Ting} and Lee, {Jiann Der} and Huang, {Chung Hsien} and Wang, {Jiun Jie} and Hsu, {Wen Chuin} and Wai, {Yau Yau}",

year = "2010",

doi = "10.1007/978-3-642-17534-3_57",

language = "英语",

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Yang, ST, Lee, JD, Huang, CH, Wang, JJ, Hsu, WC & Wai, YY 2010, An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map. in Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6444 LNCS, pp. 462-469, 17th International Conference on Neural Information Processing, ICONIP 2010, Sydney, NSW, Australia, 22/11/10. https://doi.org/10.1007/978-3-642-17534-3_57

An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map. / Yang, Shih Ting; Lee, Jiann Der; Huang, Chung Hsien et al.
Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings. PART 2. ed. 2010. p. 462-469 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6444 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map

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AU - Lee, Jiann Der

AU - Huang, Chung Hsien

AU - Wang, Jiun Jie

AU - Hsu, Wen Chuin

AU - Wai, Yau Yau

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AB - Mild cognitive impairment (MCI) is considered as a transitional stage between normal aging and dementia. MCI has a high risk to convert into Alzheimer's disease (AD). In the related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, we designed a MRI-based classification framework to distinguish the patients of MCI and AD from normal individuals. First, volumetric features and shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a Self-organizing map classifier was trained for patient classification. By combining the volumetric features and shape features, the classification accuracy is reached to 86.76%, 66.67%, and 46.67% in AD, amnestic MCI (aMCI), and dysexecutive MCI (dMCI), respectively. In addition, with the help of PCA process, the classification result is improved to 93.63%, 73.33%, and 53.33% in AD, aMCI and dMCI, respectively.

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SN - 3642175333

SN - 9783642175336

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Yang ST, Lee JD, Huang CH, Wang JJ, Hsu WC, Wai YY. An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map. In Neural Information Processing: Models and Applications - 17th International Conference, ICONIP 2010, Proceedings. PART 2 ed. 2010. p. 462-469. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-17534-3_57

An image-aided diagnosis system for dementia classification based on multiple features and self-organizing map

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