Texture Features Based Detection of Parkinson's Disease on DaTSCAN Images

In this work, a novel approach to Computer Aided Diagnosis (CAD) system for the Parkinson’s Disease (PD) is proposed. This tool is intended for physicians, and is based on fully automated methods that lead to the classification of Ioflupane/FP-CIT-I-123 (DaTSCAN) SPECT images. DaTSCAN images from the Parkinson Progression Markers Initiative (PPMI) are used to have in vivo information of the dopamine transporter density. These images are normalized, reduced (using a mask), and then a GLC matrix is computed over the whole image, extracting several Haralick texture features which will be used as a feature vector in the classification task. Using the leave-one-out cross-validation technique over the whole PPMI database, the system achieves results up to a 95.9% of accuracy, and 97.3% of sensitivity, with positive likelihood ratios over 19, demonstrating our system’s ability on the detection of the Parkinson’s Disease by providing robust and accurate results for clinical practical use, as well as being fast and automatic.

[1]  Nicholas Ayache,et al.  Automatic Classification of SPECT Images of Alzheimer's Disease Patients and Control Subjects , 2004, MICCAI.

[2]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[3]  F Segovia,et al.  Automatic assistance to Parkinson's disease diagnosis in DaTSCAN SPECT imaging. , 2012, Medical physics.

[4]  K. S. Nijran,et al.  Automatic classification of 123I-FP-CIT (DaTSCAN) SPECT images , 2011, Nuclear medicine communications.

[5]  Juan Manuel Górriz,et al.  Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson Disease , 2013, Expert Syst. Appl..

[6]  C. Edwards,et al.  The application of network mapping in differential diagnosis of parkinsonian disorders , 2007, Clinical Neuroscience Research.

[7]  Michael J Aminoff,et al.  Clinical differentiation of parkinsonian syndromes: prognostic and therapeutic relevance. , 2004, The American journal of medicine.

[8]  Roongroj Bhidayasiri.  How useful is (123I) beta-CIT SPECT in the diagnosis of Parkinson's disease? , 2006, Reviews in neurological diseases.

[9]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[10]  F Segovia,et al.  Improved Parkinsonism diagnosis using a partial least squares based approach. , 2012, Medical physics.

[11]  A. Singleton,et al.  The Parkinson Progression Marker Initiative (PPMI) , 2011, Progress in Neurobiology.

[12]  Steven McGee,et al.  Simplifying likelihood ratios , 2002, Journal of General Internal Medicine.

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[14]  Vladimir Vapnik,et al.  Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics) , 1982 .

[15]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[16]  Jacob D. Furst,et al.  Directional Invariance of Co-occurrence Matrices within the Liver , 2008, 2008 International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies.

[17]  Pierre Hellier,et al.  Level Set Methods in an EM Framework for Shape Classification and Estimation , 2004, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[18]  A. Rajput,et al.  Prevalence of movement disorders in elderly community residents. , 1994, Neuroepidemiology.

[19]  Francisco Jesús Martínez-Murcia,et al.  Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test , 2012, Expert Syst. Appl..