3D neurological image retrieval with localized patholo-gycentric CMRGLC patterns

Weidong Cai; Sidong Liu; Lingfeng Wen; Stefan Eberl; Michael J. Fulham; Dagan Feng

doi:10.1109/ICIP.2010.5651869

3D neurological image retrieval with localized patholo-gycentric CMRGLC patterns

Weidong Cai^*, Sidong Liu, Lingfeng Wen, Stefan Eberl, Michael J. Fulham, Dagan Feng

^*Corresponding author for this work

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

36 Citations (Scopus)

Abstract

Functional neuroimaging has an important role in non-invasive diagnosis of neurodegenerative disorders. There are now large volumes of imaging data generated by functional imaging technologies and so there is a need to efficiently manage and retrieve these data. In this paper, we propose a new scheme for efficient 3D content-based neurological image retrieval. 3D pathology-centric masks were adaptively designed and applied for extracting CMRGlc (cerebral metabolic rate of glucose consumption) texture features with volumetric co-occurrence matrices from neurological FDG PET images. Our results, using 93 clinical dementia studies, show that our approach offers a robust and efficient retrieval mechanism for relevant clinical cases and provides advantages in image data analysis and management.

Original language	English
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3201-3204
Number of pages	4
ISBN (Electronic)	9781424479931
ISBN (Print)	9781424479948
DOIs	https://doi.org/10.1109/ICIP.2010.5651869
Publication status	Published - 1 Dec 2010
Externally published	Yes
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010

Conference

Conference	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	26/09/10 → 29/09/10

Keywords

3D neurological image
Brain PET image
Dementia
Image retrieval
Localized retrieval

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10.1109/ICIP.2010.5651869

Cite this

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title = "3D neurological image retrieval with localized patholo-gycentric CMRGLC patterns",

abstract = "Functional neuroimaging has an important role in non-invasive diagnosis of neurodegenerative disorders. There are now large volumes of imaging data generated by functional imaging technologies and so there is a need to efficiently manage and retrieve these data. In this paper, we propose a new scheme for efficient 3D content-based neurological image retrieval. 3D pathology-centric masks were adaptively designed and applied for extracting CMRGlc (cerebral metabolic rate of glucose consumption) texture features with volumetric co-occurrence matrices from neurological FDG PET images. Our results, using 93 clinical dementia studies, show that our approach offers a robust and efficient retrieval mechanism for relevant clinical cases and provides advantages in image data analysis and management.",

keywords = "3D neurological image, Brain PET image, Dementia, Image retrieval, Localized retrieval",

author = "Weidong Cai and Sidong Liu and Lingfeng Wen and Stefan Eberl and Fulham, \{Michael J.\} and Dagan Feng",

year = "2010",

month = dec,

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doi = "10.1109/ICIP.2010.5651869",

language = "English",

isbn = "9781424479948",

pages = "3201--3204",

booktitle = "2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2010 17th IEEE International Conference on Image Processing, ICIP 2010 ; Conference date: 26-09-2010 Through 29-09-2010",

}

Cai, W, Liu, S, Wen, L, Eberl, S, Fulham, MJ & Feng, D 2010, 3D neurological image retrieval with localized patholo-gycentric CMRGLC patterns. in 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings., 5651869, Institute of Electrical and Electronics Engineers (IEEE), pp. 3201-3204, 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, Hong Kong, 26/09/10. https://doi.org/10.1109/ICIP.2010.5651869

3D neurological image retrieval with localized patholo-gycentric CMRGLC patterns. / Cai, Weidong; Liu, Sidong; Wen, Lingfeng et al.
2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2010. p. 3201-3204 5651869.

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

TY - GEN

T1 - 3D neurological image retrieval with localized patholo-gycentric CMRGLC patterns

AU - Cai, Weidong

AU - Liu, Sidong

AU - Wen, Lingfeng

AU - Eberl, Stefan

AU - Fulham, Michael J.

AU - Feng, Dagan

PY - 2010/12/1

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N2 - Functional neuroimaging has an important role in non-invasive diagnosis of neurodegenerative disorders. There are now large volumes of imaging data generated by functional imaging technologies and so there is a need to efficiently manage and retrieve these data. In this paper, we propose a new scheme for efficient 3D content-based neurological image retrieval. 3D pathology-centric masks were adaptively designed and applied for extracting CMRGlc (cerebral metabolic rate of glucose consumption) texture features with volumetric co-occurrence matrices from neurological FDG PET images. Our results, using 93 clinical dementia studies, show that our approach offers a robust and efficient retrieval mechanism for relevant clinical cases and provides advantages in image data analysis and management.

AB - Functional neuroimaging has an important role in non-invasive diagnosis of neurodegenerative disorders. There are now large volumes of imaging data generated by functional imaging technologies and so there is a need to efficiently manage and retrieve these data. In this paper, we propose a new scheme for efficient 3D content-based neurological image retrieval. 3D pathology-centric masks were adaptively designed and applied for extracting CMRGlc (cerebral metabolic rate of glucose consumption) texture features with volumetric co-occurrence matrices from neurological FDG PET images. Our results, using 93 clinical dementia studies, show that our approach offers a robust and efficient retrieval mechanism for relevant clinical cases and provides advantages in image data analysis and management.

KW - 3D neurological image

KW - Brain PET image

KW - Dementia

KW - Image retrieval

KW - Localized retrieval

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M3 - Conference proceeding contribution

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BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings

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Y2 - 26 September 2010 through 29 September 2010

ER -

3D neurological image retrieval with localized patholo-gycentric CMRGLC patterns

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Keywords

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