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.
Original language | English |
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Title of host publication | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 937-941 |
Number of pages | 5 |
ISBN (Print) | 9781479957514 |
DOIs | |
Publication status | Published - Jan 28 2014 |
Keywords
- anomaly detection
- image segmentation
- Medical diagnostic imaging
- positron emission tomography
- tumors
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition