Feature analysis for Parkinson's disease detection based on transcranial sonography image.

Transcranial sonography (TCS) is a new tool for the diagnosis of Parkinson's disease (PD) according to a distinct hyperechogenic pattern in the substantia nigra (SN) region. However a procedure including rating scale of SN hyperechogenicity was required for a standard clinical setting with increased use. We applied the feature analysis method to a large TCS dataset that is relevant for clinical practice and includes the variability that is present under real conditions. In order to decrease the influence to the image properties from the different settings of ultrasound machine, we propose a local image analysis method using an invariant scale blob detection for the hyperechogenicity estimation. The local features are extracted from the detected blobs and the watershed regions in half of mesencephalon area. The performance of these features is evaluated by a feature-selection method. The cross validation results show that the local features could be used for PD detection.