Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37756
Title: Automated Detection of the Occurrence and Changes of Hot-Spots in Intro-subject FDG-PET Images from Combined PET-CT Scanners
Authors: Xia, Y
Wang, J
Feng, D
Keywords: PET-CT imaging
Change detection
Region growing
Thresholding
Treatment response
Issue Date: 2010
Source: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA'2010), Sydney, Australia, 1-3 Dec. 2010, p. 63-68 How to cite?
Abstract: Dual-modality PET-CT imaging has been prevalently used as an essential diagnostic tool for monitoring treatment response in malignant disease patients. However, evaluation of treatment outcomes in serial scans by visual inspecting multiple PET-CT volumes is time consuming and laborious. In this paper, we propose an automated algorithm to detect the occurrence and changes of hot-spots in intro-subject FDG-PET images from combined PET-CT scanners. In this algorithm, multiple CT images of the same subject are aligned by using an affine transformation, and the estimated transformation is then used to align the corresponding PET images into the same coordinate system. Hot-spots are identified using thresholding and region growing with parameters determined specifically for different body parts. The changes of the detected hot-spots over time are analysed and presented. Our results in 19 clinical PET-CT studies demonstrate that the proposed algorithm has a good performance.
URI: http://hdl.handle.net/10397/37756
ISBN: 978-1-4244-8816-2
978-0-7695-4271-3 (E-ISBN)
Appears in Collections:Conference Paper

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