Ordinal classification of the affectation level of 3D-images in Parkinson diseases
| Autor: | Pedro Antonio Gutiérrez, Maria Victoria Guiote Moreno, Juan Antonio Vallejo Casas, Julio Camacho-Cañamón, Antonio Manuel Durán-Rosal, Ester Rodríguez-Cáceres, César Hervás-Martínez |
|---|---|
| Přispěvatelé: | [Durán-Rosal,AM] Department of Quantitative Methods, Universidad Loyola Andalucía, Córdoba, Spain. [Camacho-Cañamón,J, Gutiérrez,PA, Hervás-Martínez,C] Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain. [Guiote Moreno,MV, Vallejo Casas,JA] UGC Medicina Nuclear, Hospital Universitario 'Reina Sofía', IMIBIC, University of Córdoba, Córdoba, Spain. [Rodríguez-Cáceres,E] Provincial TICS Team, Hospital Universitario 'Reina Sofía', IMIBIC, University of Córdoba, Córdoba, Spain., This research has been partially supported by the 'Ministerio de Economía, Industria y Competitividad' of Spain (Ref. TIN2017-85887-C2-1-P) and the 'Fondo Europeo de Desarrollo Regional (FEDER) y de la Consejería de Economía, Conocimiento, Empresas y Universidad' of the 'Junta de Andalucía' (Spain) (Ref. UCO-1261651). |
| Rok vydání: | 2020 |
| Předmět: |
Imaging
three-dimensional Computer science Science Dopamine Parkinson's disease Enfermedad de parkinson Imagenología tridimensional Dopamina Feature selection 02 engineering and technology computer.software_genre Article Image (mathematics) Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans [Medical Subject Headings] 03 medical and health sciences 0302 clinical medicine Qualitative analysis Imaging Three-Dimensional Voxel Image processing computer-assisted Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering Image Processing Computer-Assisted Humans Statistical hypothesis testing Information Science::Information Science::Computing Methodologies::Image Processing Computer-Assisted [Medical Subject Headings] Multidisciplinary Analytical Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Imaging Three-Dimensional [Medical Subject Headings] business.industry Statistics Pattern recognition Parkinson Disease Class (biology) Parkinson disease Binary classification Medicine Diseases::Nervous System Diseases::Neurodegenerative Diseases::Parkinson Disease [Medical Subject Headings] 020201 artificial intelligence & image processing Procesamiento de imagen asistido por computador Artificial intelligence Information Science::Information Science::Computing Methodologies::Algorithms [Medical Subject Headings] business computer 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) |
| ISSN: | 2045-2322 |
| Popis: | 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 $$^{123}$$ 123 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. |
| Databáze: | OpenAIRE |
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