A feature identification system for electron magnetic resonance tomography: Fusion of principal components transform, color quantization and boundary information
A windows-based, object-oriented, feature identification system for electron magnetic resonance imaging (EMRI) is presented. Identification of region of interest (ROI) is achieved by fusing the standard principal component transform (PCT) and a color quantization method. The performance of the system is evaluated using renal and a series of murine RIF tumor data imaged on different days after the implantation of the tumor. The integrated ROI identification system clearly brings out the capability of EMRI to detect changes in the tumor redox status when thiol levels are lowered. Prior application of PCT reduces the computation load on the color quantization process, enabling the system to be twice faster than an earlier technique reported by the authors. The system is implemented in Visual C++ using Micro Soft Foundation Classes (MFC). Both visual evaluation as well as quantitative metrics shows the performance of the system to be optimal for 8-color quantization.
feature identification electron magnetic resonance tomography principal components transform color quantization color image segmentation