Title :
Automatic method for tumor segmentation from 3-points dynamic PET acquisitions
Author :
Verdoja, Francesco ; Grangetto, Marco ; Bracco, Christian ; Varetto, Teresio ; Racca, Manuela ; Stasi, Michele
Author_Institution :
Comput. Sci. Dept., Univ. degli Studi di Torino, Turin, Italy
Abstract :
In this paper a novel technique to segment tumor voxels in dynamic positron emission tomography (PET) scans is proposed. An innovative anomaly detection tool tailored for 3-points dynamic PET scans is designed. The algorithm allows the identification of tumoral cells in dynamic FDG-PET scans thanks to their peculiar anaerobic metabolism experienced over time. The proposed tool is preliminarily tested on a small dataset showing promising performance as compared to the state of the art in terms of both accuracy and classification errors.
Keywords :
image segmentation; medical image processing; positron emission tomography; security of data; tumours; 3-points dynamic PET acquisitions; automatic method; classification errors; dynamic FDG-PET scans; dynamic positron emission tomography scans; innovative anomaly detection tool; peculiar anaerobic metabolism; tumor voxel segmentation; tumoral cells; Algorithm design and analysis; Biomedical imaging; Cancer; Detectors; Image segmentation; Positron emission tomography; Tumors; Medical diagnostic imaging; anomaly detection; image segmentation; positron emission tomography; tumors;
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
DOI :
10.1109/ICIP.2014.7025188
