Improved parkinsonism diagnosis using a partial least squares based approach.

Autor: Segovia F; Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain. fsegovia@ugr.es, Gorriz JM, Ramirez J, Alvarez I, Jimenez-Hoyuela JM, Ortega SJ
Jazyk: angličtina
Zdroj: Medical physics [Med Phys] 2012 Jul; Vol. 39 (7), pp. 4395-403.
DOI: 10.1118/1.4730289
Abstrakt: Purpose: An accurate and early diagnosis of Parkinsonian syndrome (PS) is nowadays a challenge. This syndrome includes several pathologies with similar symptoms (Parkinson's disease, multisystem atrophy, progressive supranuclear palsy, corticobasal degeneration and others) which make the diagnosis more difficult. (123)I-ioflupane allows to obtain in vivo images of the brain that can be used to assist the PS diagnosis and provides a way to improve its accuracy.
Methods: In this paper, we show a novel method to automatically classify (123)I-ioflupane images into two groups: controls or PS. The proposed methodology analyzes separately each hemisphere of the brain by means of a novel approach based on partial least squares (PLS) and support vector machine.
Results: A database with 189 (123)I-ioflupane images (94 controls and 95 pathological images) was used for evaluation purposes. The application of the proposed method based on PLS yields high accuracy rates up to 94.7% with sensitivity = 93.7% and specificity = 95.7%, outperforming previous approaches based on singular value decomposition, which are used as a reference.
Conclusions: The use of advanced techniques based on classical signal analysis and their application to each hemisphere of the brain separately improves the (assisted) diagnosis of PS.
Databáze: MEDLINE
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