title: Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis.
abstract: Radiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI) were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA). PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8) was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support.
authors: The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors: Andreia V
authors: AV
authors: Faria
authors: The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors: Kenichi
authors: K
authors: Oishi
authors: The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors: Shoko
authors: S
authors: Yoshida
authors: Department of Neurology, Johns Hopkins University, Baltimore, MD, USA ; Department of Physical Medicine & Rehabilitation Medicine, Johns Hopkins University, Baltimore, MD, USA ; Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA.
authors: Argye
authors: A
authors: Hillis
authors: Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors: Michael I
authors: MI
authors: Miller
authors: The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA ; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
authors: Susumu
authors: S
authors: Mori
mesh_terms: D000368:Aged; D000465:Algorithms; D018888:Aphasia, Primary Progressive / Q000473:pathology; D001271:Atlases as Topic; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D057386:Search Engine / Q000379:methods*
keywords: Atlas-based analysis; Automated parcellation; Brain; Content-based image retrieval; MRI