In this work, we propose a novel imaging preprocessing step based on the use of the gradient magnitude for medical DaTSCAN SPECT images. As Parkinson’s Disease (PD) is characterized by a marked reduction of intensity at striatum area, measuring intensities in this region is considered as a good marker for this neurological disorder. To extend this idea, we have been studying how quick these values decrease. A simple way to do this was using the gradient of each image. Applying Machine Learning algorithms, we have classified the gradient images and obtained an accuracy improvement of almost 2%. These results prove that the gradient magnitude is even a better marker for PD diagnosis and opens the door to new future investigations about this pathology.
CITATION STYLE
Castillo-Barnes, D., Segovia, F., Martinez-Murcia, F. J., Salas-Gonzalez, D., Ramírez, J., & Górriz, J. M. (2019). Classification Improvement for Parkinson’s Disease Diagnosis Using the Gradient Magnitude in DaTSCAN SPECT Images. In Advances in Intelligent Systems and Computing (Vol. 771, pp. 100–109). Springer Verlag. https://doi.org/10.1007/978-3-319-94120-2_10
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