Polynomial transformation for MRI feature extraction
Hamid Soltanian‐Zadeh, Mahmood Kharrat, Donald J. Peck · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
We present a non-linear (polynomial) transformation to minimize scattering of data points around normal tissue clusters in a normalized MRI feature space, in which normal tissues are clustered around pre-specified target positions. This transformation is motivated by non-linear relationship between MRI pixel intensities and intrinsic tissue parameters (e.g., T1, T2, PD). To determine scattering amount, we use ratio of summation of within-class distances fro clusters to summation of their between-class distances. We find the transformation by minimizing the scattering amount. Next, we generate a 3D visualization of the MRI feature space and define regions of interest (ROI's) on clusters seen for normal and abnormal tissues. We use these ROI's to estimate signature vectors (cluster centers). Finally, we use the signature vectors for segmenting and characterizing tissues. We used simulation, phantom, and brain MRI to evaluate the polynomial transformation and compare it to the linear transformation. In all studies, we were able to identify clusters for normal and abnormal tissues and segment the images. Compared to the linear method, the non-linear approach yields enhanced clustering properties and better separation of normal and abnormal tissues. ON the other hand, the linear transformation is more appropriate than the non-linear method for capturing partial volume information.