Paper Info
Title
Brain Lesion Detection In 3d Pet Images Using Max-Trees And A New Spatial Context Criterion
Abstract
In this work, we propose a new criterion based on spatial context to select relevant nodes in a max-tree representation of an image, dedicated to the detection of 3D brain tumors for F-18-FDG PET images. This criterion prevents the detected lesions from merging with surrounding physiological radiotracer uptake. A complete detection method based on this criterion is proposed, and was evaluated on five patients with brain metastases and tuberculosis, and quantitatively assessed using the true positive rates and positive predictive values. The experimental results show that the method detects all the lesions in the PET images.
Year
DOI
Venue
2017
10.1007/978-3-319-57240-6_37
MATHEMATICAL MORPHOLOGY AND ITS APPLICATIONS TO SIGNAL AND IMAGE PROCESSING (ISMM 2017)
Keywords
Field
DocType
Max-tree representation, Spatial context, Brain tumors, Positron Emission Tomography, Detection
Computer vision,Lesion,Computer science,Positron emission tomography,Artificial intelligence,Spatial contextual awareness,Merge (version control)
Conference
Volume
ISSN
Citations 
10225
0302-9743
0
PageRank 
References 
Authors
0.34
7
5
Name
Order
Citations
PageRank
Hélène Urien100.68
Irène Buvat24613.68
Nicolas Rougon310.72
Michaël Soussan400.34
Isabelle Bloch52123170.75