Conference contribution
(Original article)


Regression Forest-Based Organ Detection in Normalized PET Images


Publication Details
Author(s): Fischer P, Daum V, Hahn D, Prümmer M, Hornegger J
Title edited volumes: Informatik aktuell
Publisher: Springer
Publishing place: Berlin Heidelberg
Publication year: 2014
Title of series: Informatik aktuell
Conference Proceedings Title: Bildverarbeitung für die Medizin 2014
Pages range: 384-389
ISSN: 1431-472X

Event details
Event: Bildverarbeitung für die Medizin 2014
Event location: Aachen
Start date of the event: 16/03/2014
End date of the event: 18/03/2014

Abstract

The detection of organs from full-body PET images is a challenging task due to the high noise and the limited amount of anatomical information of PET imaging. The knowledge of organ locations can support many clinical applications like image registration or tumor detection. This paper is the first to propose an organ localization framework tailored on the challenges of PET. The algorithm involves intensity normalization, feature extraction and regression forests. Linear and nonlinear intensity normalization methods are compared theoretically and experimentally. From the normalized images, long-range spatial context visual features are extracted. A regression forest predicts the organ bounding boxes. Experiments show that percentile normalization is the best preprocessing method. The algorithm is evaluated on 25 clinical images with a spatial resolution of 5 mm. With 13.8mm mean absolute bounding box error, it achieves state-of-the-art results.



How to cite
APA: Fischer, P., Daum, V., Hahn, D., Prümmer, M., & Hornegger, J. (2014). Regression Forest-Based Organ Detection in Normalized PET Images. In Bildverarbeitung für die Medizin 2014 (pp. 384-389). Berlin Heidelberg: Springer.

MLA: Fischer, Peter, et al. "Regression Forest-Based Organ Detection in Normalized PET Images." Proceedings of the Bildverarbeitung für die Medizin 2014, Aachen Berlin Heidelberg: Springer, 2014. 384-389.

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