Journal
MEDICAL PHYSICS
Volume 41, Issue 1, Pages -Publisher
WILEY
DOI: 10.1118/1.4845115
Keywords
Parkinson's disease; I-123-ioflupane; computer aided diagnosis; Haralick texture features; support vector machines
Funding
- The Michael J. Fox Foundation
- Abbott
- Biogen Idec
- F. Hoffman-La Roche Ltd
- GE Healthcare
- Genentech
- Pfizer Inc
- MICINN [TEC2008-02113, TEC2012-34306]
- Consejer a de Innovacion, Ciencia y Empresa (Junta de Andalucia, Spain) [P07-TIC-02566, P09-TIC-4530, P11-TIC-7103]
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Purpose: A novel approach to a computer aided diagnosis system for the Parkinson's disease is proposed. This tool is intended as a supporting tool for physicians, based on fully automated methods that lead to the classification of 123I-ioflupane SPECT images. Methods: I-123-ioflupane images from three different databases are used to train the system. The images are intensity and spatially normalized, then subimages are extracted and a 3D gray-level co-occurrence matrix is computed over these subimages, allowing the characterization of the texture using Haralick texture features. Finally, different discrimination estimation methods are used to select a feature vector that can be used to train and test the classifier. Results: Using the leave-one-out cross-validation technique over these three databases, the system achieves results up to a 97.4% of accuracy, and 99.1% of sensitivity, with positive likelihood ratios over 27. Conclusions: The system presents a robust feature extraction method that helps physicians in the diagnosis task by providing objective, operator-independent textural information about I-123-ioflupane images, commonly used in the diagnosis of the Parkinson's disease. Textural features computation has been optimized by using a subimage selection algorithm, and the discrimination estimation methods used here makes the system feature-independent, allowing us to extend it to other databases and diseases. (C) 2014 American Association of Physicists in Medicine.
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