Applications of Gaussian mixture models and mean squared error within DatSCAN SPECT imaging

This work highlights the exploitation of Gaussian Mixture Model (GMM) and Mean squared Error (MSE) in DaTSCAN SPECT brain images for intensity normalization purposes over two proposed approaches. The first proposed methodology is based on a nonlinear image filtering by means of GMM, which considers not only the intensity levels of each voxel but also its coordinates inside the so-defined spatial Gaussian functions. It is achieved according to a probability threshold that measures the weight of each kernel or cluster on the striatum area, the voxels in the non-specific regions are intensity normalized by removing clusters whose likelihood is negligible. The second normalization method based on MSE which is performed by a linear intensity transformation in each voxel. This approach is based on predicting jointly different intensity normalization parameters that leads to the joint minimization of the squared sum errors between the template image and the optimal linear estimated image (normalized image). We compare these methods of normalization together with another approach widely used based on specific-to-non-specific binding ratio. This comparison is based on DaTSCAN image analysis and classification for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome detection.