Parkinson’s disease (PD) is an overwhelming neurodegenerative disorder caused by deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger in the brain impairs several brain regions and yields to various movement and non-motor symptoms. The incidence of PD is considered to be doubled in the next two decades and this urges more researches on its early diagnosis and treatment. In this paper, we propose an approach to diagnose PD using magnetic resonance imaging (MRI) data. We first introduce a joint feature-sample selection method to select the optimal subset of samples and features for a reliable training process. This procedure selects the most discriminative features and discards poor sample (outliers). Then, a robust classification framework is proposed that can simultaneously de-noise the selected subset of features and samples, and learn a classification model. Our model can further de-noise the test samples based on the cleaned training data. Experimental results on both synthetic and a publicly available PD dataset show promising results.
|Title of host publication||Medical Computer Vision|
|Subtitle of host publication||Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers|
|Editors||Michael Kelm, Henning Müller, Bjoern Menze, Shaoting Zhang, Dimitris Metaxas, Georg Langs, Albert Montillo, Weidong Cai|
|Number of pages||10|
|Publication status||Published - 2016|
|Event||International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany|
Duration: 2015 Oct 9 → 2015 Oct 9
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI|
|Period||15/10/9 → 15/10/9|
Bibliographical notePublisher Copyright:
© Springer International Publishing Switzerland 2016.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)