Multiple feature extraction for early Parkinson risk assessment based on transcranial sonography image

Transcranial sonography (TCS) is a new tool for the diagnosis of Parkinson's disease (PD) at a very early state. The TCS image of mesencephalon shows a distinct hyperechogenic pattern in about 90% PD patients. This pattern is usually manually segmented and the substantia nigra (SN) region can be used as an early PD indicator. However this method is based on manual evaluation of examined images. The extraction of multiple features from TCS images characterizing the half mesencephalon morphology and structure can be used to validate the observer-independent PD indicator. We propose hybrid feature extraction methods which includes statistical, geometrical and texture features for the early PD risk assessment. These features are tested with support vector machines (SVMs). Furthermore five features are selected with the sequential feature selection methods. The results show that the correct rate of the classification with these five features is reaching 96%.