TY - JOUR
T1 - Gross feature recognition of Anatomical Images based on Atlas grid (GAIA)
T2 - Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI
AU - Qin, Yuan Yuan
AU - Hsu, Johnny T.
AU - Yoshida, Shoko
AU - Faria, Andreia V.
AU - Oishi, Kumiko
AU - Unschuld, Paul G.
AU - Redgrave, Graham W.
AU - Ying, Sarah H.
AU - Ross, Christopher A.
AU - Van Zijl, Peter C.M.
AU - Hillis, Argye E.
AU - Albert, Marilyn S.
AU - Lyketsos, Constantine G.
AU - Miller, Michael I.
AU - Mori, Susumu
AU - Oishi, Kenichi
N1 - Funding Information:
The authors thank Ms. Mary McAllister for the help with manuscript editing, and Ms. Terri Brawner and Mr. Hugh Wall for the technological support. This publication was made possible by grants from the National Institutes of Health ( R01DC011317 , R21AG033774 , R01HD065955 , K24DA61427 , U54NS56883 , P41EB015909 , U24RR021382 , P01EB00195 , R01AG20012 , K23EY015802 , R21NS059830 , M01RR000052 , R01NS056307 , RC1NS068897 and P50AG005146 ), the National Center for Research Resources grant G12-RR003061 , the State Scholarship Fund (File No. 2011616055 ), and the Yousem Family Research Fund . Its contents are solely the responsibility of the authors and do not necessarily represent the official view of any of these institutes. The images acquired on Alzheimer's patients and age-matched controls were supported by a methods development grant from Glaxo-Smith-Kline .
PY - 2013
Y1 - 2013
N2 - We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.
AB - We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.
KW - Alzheimer's disease
KW - Atlas
KW - Feature recognition
KW - Huntington's disease
KW - Primary progressive aphasia
KW - Spinocerebellar ataxia
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U2 - 10.1016/j.nicl.2013.08.006
DO - 10.1016/j.nicl.2013.08.006
M3 - Article
C2 - 24179864
AN - SCOPUS:84883884981
SN - 2213-1582
VL - 3
SP - 202
EP - 211
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
ER -