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Parametrization of textural patterns in 123I-ioflupane imaging for the automatic detection of Parkinsonism.
- Source :
-
Medical Physics . Jan2014, Vol. 41 Issue 1, pn/a-N.PAG. 13p. - Publication Year :
- 2014
-
Abstract
- 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 of123I-ioflupane SPECT images. Methods: 123I-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 about123I-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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00942405
- Volume :
- 41
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Medical Physics
- Publication Type :
- Academic Journal
- Accession number :
- 119806122
- Full Text :
- https://doi.org/10.1118/1.4845115