Parametrization of textural patterns in 123I‐ioflupane imaging for the automatic detection of Parkinsonism. Issue 1 (17th December 2013)

• Record Type: Journal Article
• Authors: Martinez‐Murcia, F. J.; Górriz, J. M.; Ramírez, J.; Moreno‐Caballero, M.; Gómez‐Río, M.
• Is Part Of: Medical physics. Volume 41:Issue 1(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-12-17
• Subjects: Single photon emission computed tomography (SPECT) -- Degenerative diseases (Alzheimer's, ALS, etc) -- Image quality -- Image analysis; diseases -- feature extraction -- image classification -- image texture -- iodine compounds -- medical image processing -- neurophysiology -- sensitivity -- single photon emission computed tomography; Parkinson's disease -- 123I‐ioflupane -- computer aided diagnosis -- Haralick texture features -- support vector machines; Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of texture -- Scintigraphy -- Measuring radioactive content of objects, e.g. contamination (whole‐body counters G01T011/63); Databases -- Brain -- Image analysis -- Spatial analysis -- Multivariate analysis -- Single photon emission computed tomography -- Three dimensional image processing -- Entropy -- Vector calculus; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4845115
• Languages: English
• ISSNs: 0094-2405

Abstract

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 123 I‐ioflupane SPECT images. Methods: : 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 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.