Zusammenfassung
Automatic segmentation of medical images requires accurate detection of the desired organ as a first step. In contrast to application specific approaches, learning-based object detection algorithms are easily adaptable to new applications. We present a learning-based object detection approach based on the Viola-Jones algorithm. We propose several extensions to the original approach, including a new 3D feature type and a multi-organ detection scheme. The algorithm is used to detect six different organs in CT scans as well as the prostate in MRI data. Our evaluation shows that the algorithm provides fast and reliable detection results in all cases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Kainmüller D, Lange T, Lamecker H. Shape constrained automatic segmentation of the liver based on a heuristic intensity model. Proc MICCAI Grand Challenge: 3D Segmentation in the Clinic. 2007; p. 109–16.
Ruppertshofen H, Lorenz C, Schmidt S, et al. Discriminative generalized hough transform for localization of joints in the lower extremities. CSRD. 2011;26:97–105.
Ling H, Zhou SK, Zheng Y, et al. Hierarchical, learning-based automatic liver segmentation. Proc IEEE CVPR. 2008; p. 1–8.
Zheng Y, Barbu A, Georgescu B, et al. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging. 2008;27(11):1668–81.
Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proc CVPR. 2001;1:511–18.
Li C, Xu C, Anderson A, et al. MRI tissue classification and bias field estimation based on coherent local intensity clustering: A unified energy minimization framework. IPMI. 2009; p. 288–99.
Kirschner M, Jung F, Wesarg S. Automatic prostate segmentation in MR images with a probabilistic active shape model. Proc MICCAI Grand Challenge: Prostate MR Image Segmentation. 2012.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jung, F., Kirschner, M., Wesarg, S. (2013). A Generic Approach to Organ Detection Using 3D Haar-Like Features. In: Meinzer, HP., Deserno, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2013. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36480-8_56
Download citation
DOI: https://doi.org/10.1007/978-3-642-36480-8_56
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36479-2
Online ISBN: 978-3-642-36480-8
eBook Packages: Computer Science and Engineering (German Language)