Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson Disease

作者：Rojas A; Gorriz J M^*; Ramirez J; Illan I A; Martinez Murcia F J; Ortiz A; Gomez Rio M; Moreno Caballero M

来源：Expert Systems with Applications, 2013, 40(7): 2756-2766.

DOI：10.1016/j.eswa.2012.11.017

• Parkinsonian Syndrome (PS)
• Parkinson Disease (PD)
• Computer Aided Diagnosis (CAD)
• Empirical Mode Decomposition (EMD)
• Principal Component Analysis (PCA)
• Independent Component Analysis (ICA)
• Support Vector Machines (SVM)
• DaTSCAN

Parkinsonism is the second most common neurodegenerative disorder. It includes several pathologies with similar symptoms, what makes the diagnosis really difficult. I-ioflupane allows to obtain in vivo images of the brain that can be used to assist the PS diagnosis and provides a way to improve its accuracy. In this paper a new method for brain SPECT image feature extraction is shown. This novel Computer Aided Diagnosis (CAD) system is based on the Empirical Mode Decomposition (EMD), which decomposes any non-linear and non-stationary time series into a small number of oscillatory Intrinsic Mode Functions (IMF) a monotonous Residuum. A 80-DaTSCAN image database from the %26quot;Virgen de las Nieves%26quot; Hospital in Granada (Spain) was used to evaluate this method, yielding up to 95% accuracy, which greatly improves the baseline Voxel-As-Feature (VAF) approach.

• 出版日期2013-6-1