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Neuroimage Clin. 2015 Jan 15;7:367-76. doi: 10.1016/j.nicl.2015.01.008. eCollection 2015.

Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis.

Author information

1
The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
2
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.
3
Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
4
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.

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.

KEYWORDS:

Atlas-based analysis; Automated parcellation; Brain; Content-based image retrieval; MRI

PMID:
25685706
PMCID:
PMC4309952
DOI:
10.1016/j.nicl.2015.01.008
[Indexed for MEDLINE]
Free PMC Article

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