Automatic Segmentation System of Emission Tomography Data Based on Classification System

摘要

Segmentation and delineation of tumour boundaries are important and difficult step in emission tomography imaging, where acquired and reconstructed images presents large noise and a blurring level. Several methods have been previously proposed and can be used in single photon emission tomography (SPECT) or positron emission tomography (PET) imaging. Some of them relies on the standard uptake value (SUV) used in PET imaging. Presented approach can be used in both (SPECT and PET) modalities and it is based on support vector machines (SVM) classification system. System has been tested on standard phantom, widely used for testing the emission tomography devices. Results are presented for two classifiers SVM and DLDA.