Using Image Processing for Tumor Area Allocation in PET and Color Hybrid Scan Images (PET/CT)

Abstract

Background: In clinical oncology, precise segmentation of the target tumor is essential. The positron emission tomography (PET)/computed tomography (CT) scanner effectively combines anatomical information from computed tomography with functional information from PET for accurate tumor identification, which can comprehensively describe tumor volumes, cancer is an acute disease that kills a large number of people around the world, so early detection is a vital need.                                                                                                                                                       

Materials and Methods: In this study, proposed techniques, hybrid 2 and the HSV-based hybrid techniques are presented to isolate and extract abnormal regions in PET and PET/CT images, these methods were implemented on eight, images.

Results: Results showed that the applied methods were sufficient to detect, isolate and extract areas of tumor.

Conclusion: The calculated tumor area was compared with that of the radiologist delineation it was most percent relative differences are acceptable, for PET and PET/CT images.