RT Journal Article
T1 Ordinal classification of the affectation level of 3D-images in Parkinson diseases
A1 Durán-Rosal, Antonio M.
A1 Camacho-Cañamón, Julio
A1 Gutiérrez, Pedro Antonio
A1 Guiote Moreno, Maria Victoria
A1 Rodríguez-Cáceres, Ester
A1 Vallejo Casas, Juan Antonio
A1 Hervás-Martínez, César
K1 Parkinson disease
K1 Enfermedad de parkinson
K1 Imaging, three-dimensional
K1 Imagenología tridimensional
K1 Image processing, computer-assisted
K1 Procesamiento de imagen asistido por computador
K1 Dopamine
K1 Dopamina
AB Parkinson's disease is characterised by a decrease in the density of presynaptic dopamine transporters in the striatum. Frequently, the corresponding diagnosis is performed using a qualitative analysis of the 3D-images obtained after the administration of [Formula: see text]I-ioflupane, considering a binary classification problem (absence or existence of Parkinson's disease). In this work, we propose a new methodology for classifying this kind of images in three classes depending on the level of severity of the disease in the image. To tackle this problem, we use an ordinal classifier given the natural order of the class labels. A novel strategy to perform feature selection is developed because of the large number of voxels in the image, and a method for generating synthetic images is proposed to improve the quality of the classifier. The methodology is tested on 434 studies conducted between September 2015 and January 2019, divided into three groups: 271 without alteration of the presynaptic nigrostriatal pathway, 73 with a slight alteration and 90 with severe alteration. Results confirm that the methodology improves the state-of-the-art algorithms, and that it is able to find informative voxels outside the standard regions of interest used for this problem. The differences are assessed by statistical tests which show that the proposed image ordinal classification could be considered as a decision support system in medicine.
PB Nature Publishing Group
YR 2021
FD 2021-03-29
LK http://hdl.handle.net/10668/3978
UL http://hdl.handle.net/10668/3978
LA en
NO Durán-Rosal AM, Camacho-Cañamón J, Gutiérrez PA, Guiote Moreno MV, Rodríguez-Cáceres E, Vallejo Casas JA. Ordinal classification of the affectation level of 3D-images in Parkinson diseases. Sci Rep. 2021 Mar 29;11(1):7067.
DS RISalud
RD Apr 7, 2025