Automated Diagnosis of Parkinsonian Syndromes by Deep Sparse Filtering-Based Features

Parkinsonian Syndrome (PS) or Parkinsonism is the second most common neurodegenerative disorder in the elderly. Currently there is no cure for PS, and it has important socio-economic implications due to the fact that PS progressively disables people in their ordinary daily tasks. However, precise and early diagnosis can definitely help to start the treatment in the early stages of the disease, improving the patient's quality of life. The study of neurodegenerative diseases has been usually addressed by visual inspection and semi-quantitative analysis of medical imaging, which results in subjective outcomes. However, recent advances in statistical signal processing and machine learning techniques provide a new way to explore medical images yielding to an objective analysis, dealing with the Computer Aided Diagnosis ( CAD) paradigm. In this work, we propose a method that selects the most discriminative regions on 123I-FP-CIT SPECT (DaTSCAN) images and learns features using deep-learning techniques. The proposed system has been tested using images from the Parkinson Progression Markers Initiative (PPMI), obtaining accuracy values up to 95 %, showing its robustness for PS pattern detection and outperforming the baseline Voxels-as-Features (VAF) approach, used as an approximation of the visual analysis.