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Novel multimodality segmentation using level sets and Jensen-Rényi divergence

Markel, Daniel
El Naqa, Issam
Published in Medical Physics. 2013, vol. 40, no. 12, p. 121908
Abstract Positron emission tomography (PET) is playing an increasing role in radiotherapy treatment planning. However, despite progress, robust algorithms for PET and multimodal image segmentation are still lacking, especially if the algorithm were extended to image-guided and adaptive radiotherapy (IGART). This work presents a novel multimodality segmentation algorithm using the Jensen-Rényi divergence (JRD) to evolve the geometric level set contour. The algorithm offers improved noise tolerance which is particularly applicable to segmentation of regions found in PET and cone-beam computed tomography.
PMID: 24320519
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Article (Published version) (1.4 MB) - public document Free access
Research group Imagerie Médicale (TEP et TEMP) (542)
Project FNS: 31003A-149957
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MARKEL, Daniel, ZAIDI, Habib, EL NAQA, Issam. Novel multimodality segmentation using level sets and Jensen-Rényi divergence. In: Medical Physics, 2013, vol. 40, n° 12, p. 121908. https://archive-ouverte.unige.ch/unige:40173

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Deposited on : 2014-09-12

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