Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology Segovia, Fermín Gorriz Sáez, Juan Manuel Ramírez Pérez De Inestrosa, Javier Martínez Murcia, Francisco Jesús Castillo Barnes, Diego Parkinsonism Machine learning Striatal morphology Striatum Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing the possible dopamine deficiency. During the last decade, a number of computer systems have been proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the visual examination of the data. In this work, we propose a computer system based on machine learning to separate Parkinsonian patients and control subjects using the size and shape of the striatal region, modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel the striatum. This region is then divided into two according to the brain hemisphere division and characterized with 152 measures, extracted from the volume and its three possible 2-dimensional projections. Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally, the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as a feature. 2019-11-22T12:15:03Z 2019-11-22T12:15:03Z 2019-05-14 journal article Segovia, F., Górriz, J. M., Ramírez, J., Martínez-Murcia, F. J., & Castillo-Barnes, D. (2019). Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology. International journal of neural systems, 1950011-1950011. http://hdl.handle.net/10481/58030 10.1142/S0129065719500114 eng http://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España World Scientific Publishing Company