Co-segmentation of Functional and Anatomical Images View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Ulas Bagci , Jayaram K. Udupa , Jianhua Yao , Daniel J. Mollura

ABSTRACT

This paper presents a novel method for segmenting functional and anatomical structures simultaneously. The proposed method unifies domains of anatomical and functional images (PET-CT), represents them in a product lattice, and performs simultaneous delineation of regions based on a random walk image segmentation. In addition, we propose a simple yet efficient object/background seed localization method, where background and foreground object cues are automatically obtained from PET images and propagated onto the corresponding anatomical images (CT). In our experiments, abnormal anatomies on PET-CT images from human subjects are segmented synergistically by the proposed fully automatic co-segmentation method with high precision (mean DSC of 91.44%) in seconds (avg. 40 seconds). More... »

PAGES

459-67

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-33454-2_57

DOI

http://dx.doi.org/10.1007/978-3-642-33454-2_57

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016683769

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23286163


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