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SU‐E‐J‐110: A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the Jensen‐Rényi Divergence
Author(s) -
Markel D,
Naqa I El,
Freeman C,
Vallières M
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4734946
Subject(s) - multimodality , medical imaging , joint (building) , divergence (linguistics) , algorithm , segmentation , image registration , artificial intelligence , active contour model , image segmentation , level set (data structures) , computer science , mathematics , pattern recognition (psychology) , computer vision , image (mathematics) , architectural engineering , linguistics , philosophy , engineering , world wide web
Purpose: To present a novel joint segmentation/registration for multimodality image‐guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen‐Renyi (JR) divergence to achieve improved noise robustness in a multi‐modality imaging space. Methods: To present a novel joint segmentation/registration for multimodality image‐guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen‐Renyi (JR) divergence to achieve improved noise robustness in a multi‐modality imaging space. Results: It was found that JR divergence when used for segmentation has an improved robustness to noise compared to using mutual information, or other entropy‐based metrics. The MI metric failed at around 2/3 the noise power than the JR divergence. Conclusions: The JR divergence metric is useful for the task of joint segmentation/registration of multimodality images and shows improved results compared entropy based metric. The algorithm can be easily modified to incorporate non‐intensity based images, which would allow applications into multi‐modality and texture analysis.