Automatic assistance to Parkinson's disease diagnosis in DaTSCAN SPECT imaging.

Purpose: In this work, an approach to computer aided diagnosis(CAD) system is proposed as a decision-making aid in Parkinsonian syndrome (PS) detection. This tool, intended for physicians, entails fully automatic preprocessing, normalization, and classification procedures for brain single-photon emission computed tomography images. Methods: Ioflupane[123I]FP-CIT images are used to provide in vivo information of the dopamine transporter density. These images are preprocessed using an automated template-based registration followed by two proposed approaches for intensity normalization. A support vector machine (SVM) is used and compared to other statistical classifiers in order to achieve an effective diagnosis using whole brainimages in combination with voxel selection masks. Results: The CAD system is evaluated using a database consisting of 208 DaTSCAN images (100 controls, 108 PS). SVM-based classification is the most efficient choice when masked brainimages are used. The generalization performance is estimated to be 89.02 (90.41–87.62)% sensitivity and 93.21 (92.24–94.18)% specificity. The area under the curve can take values of 0.9681 (0.9641–0.9722) when the image intensity is normalized to a maximum value, as derived from the receiver operating characteristics curves. Conclusions: The present analysis allows to evaluate the impact of the design elements for the development of a CAD-system when all the information encoded in the scans is considered. In this way, the proposed CAD-system shows interesting properties for clinical use, such as being fast, automatic, and robust.