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Title: Parametrization of textural patterns in {sup 123}I-ioflupane imaging for the automatic detection of Parkinsonism

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 of{sup 123}I-ioflupane SPECT images. Methods: {sup 123}I-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{sup 123}I-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 itmore » to other databases and diseases.« less

Authors:
;  [1]; ;  [2]
  1. Signal Processing and Biomedical Applications Research Group, Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071 (Spain)
  2. Department of Nuclear Medicine, Virgen de las Nieves Hospital, Granada 18071 (Spain)
Publication Date:
OSTI Identifier:
22251245
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 41; Journal Issue: 1; Other Information: (c) 2014 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ACCURACY; ALGORITHMS; CALCULATION METHODS; DIAGNOSIS; IMAGES; IODINE 123; NERVOUS SYSTEM DISEASES; SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY; VALIDATION

Citation Formats

Martinez-Murcia, F. J., E-mail: fjesusmartinez@ugr.es, Górriz, J. M., Ramírez, J., Moreno-Caballero, M., and Gómez-Río, M. Parametrization of textural patterns in {sup 123}I-ioflupane imaging for the automatic detection of Parkinsonism. United States: N. p., 2014. Web. doi:10.1118/1.4845115.
Martinez-Murcia, F. J., E-mail: fjesusmartinez@ugr.es, Górriz, J. M., Ramírez, J., Moreno-Caballero, M., & Gómez-Río, M. Parametrization of textural patterns in {sup 123}I-ioflupane imaging for the automatic detection of Parkinsonism. United States. doi:10.1118/1.4845115.
Martinez-Murcia, F. J., E-mail: fjesusmartinez@ugr.es, Górriz, J. M., Ramírez, J., Moreno-Caballero, M., and Gómez-Río, M. Wed . "Parametrization of textural patterns in {sup 123}I-ioflupane imaging for the automatic detection of Parkinsonism". United States. doi:10.1118/1.4845115.
@article{osti_22251245,
title = {Parametrization of textural patterns in {sup 123}I-ioflupane imaging for the automatic detection of Parkinsonism},
author = {Martinez-Murcia, F. J., E-mail: fjesusmartinez@ugr.es and Górriz, J. M. and Ramírez, J. and Moreno-Caballero, M. and Gómez-Río, M.},
abstractNote = {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{sup 123}I-ioflupane SPECT images. Methods: {sup 123}I-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{sup 123}I-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.},
doi = {10.1118/1.4845115},
journal = {Medical Physics},
issn = {0094-2405},
number = 1,
volume = 41,
place = {United States},
year = {2014},
month = {1}
}