Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI

• Published in: NeuroImage clinical Vol. 3; pp. 202 - 211
• Main Authors: Qin, Yuan-Yuan, Hsu, Johnny T, Yoshida, Shoko, Faria, Andreia V, Oishi, Kumiko, Unschuld, Paul G, Redgrave, Graham W, Ying, Sarah H, Ross, Christopher A, van Zijl, Peter C M, Hillis, Argye E, Albert, Marilyn S, Lyketsos, Constantine G, Miller, Michael I, Mori, Susumu, Oishi, Kenichi
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier 01-01-2013
• Subjects: Atlas; Huntington's disease; Primary progressive aphasia; Alzheimer's disease; Feature recognition; Spinocerebellar ataxia
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2013.08.006

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.