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Kinetic Modeling based Probabilistic Segmentation for Molecular Images

Abstract

We propose a semi-supervised, kinetic modeling based segmentation technique for molecular imaging applications. It is an iterative, self-learning algorithm based on uncertainty principles, designed to alleviate low signal-to-noise ratio (SNR) and partial volume effect (PVE) problems. Synthetic fluorodeoxyglucose (FDG) and simulated Raclopride dynamic positron emission tomography (dPET) brain images with excessive noise levels are used to validate our algorithm. We show, qualitatively and quantitatively, that our algorithm outperforms state-of-the-art techniques in identifying different functional regions and recovering the kinetic parameters.

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Authors
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Supplemental Material
Citation
Category
Paper in Conference Proceedings or in Workshop Proceedings (Full Paper in Proceedings)
Event Title
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008
Divisions
Visualization and Data Analysis
Event Location
New York
Event Type
Conference
Event Dates
Sep. 6 - 10, 2008
Publisher
Springer
Date
2008
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