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

  • Authors:

  • A. Rojas;J. M. GóRriz;J. RamíRez;I. A. IlláN;F. J. MartíNez-Murcia;A. Ortiz;M. GóMez RíO;M. Moreno-Caballero

  • Affiliations:

  • Dept. of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain;Dept. of Ingeniera de Comunicaciones, University of Malaga, 29071 Malaga, Spain;Virgen de las Nieves Hospital, Granada, Spain;Virgen de las Nieves Hospital, Granada, Spain

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

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 ''Virgen de las Nieves'' 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.