Automatic organ localizations on 3D CT images by using majority-voting of multiple 2D detections based on local binary patterns and Haar-like features

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 8670, Issue , 2013, Pages

  Zhou, Xiangrong ^a   Yamaguchi, Shoutarou ^a   Zhou, Xinin ^b   Chen, Huayue ^a   Hara, Takeshi ^a   Yokoyama, Ryujiro ^c   Kanematsu, Masayuki ^c   Fujita, Hiroshi ^a  

^a GIFU UNIVERSITY   (Japan)

^b NAGOYA BUNRI UNIVERSITY   (Japan)

^c GIFU UNIVERSITY HOSPITAL   (Japan)

Author keywords

Haar like feature; Local binary patterns; Majority voting; Object detection; Organ localization; Torso CT images

Indexed keywords

HAAR-LIKE FEATURES; LOCAL BINARY PATTERNS; MAJORITY VOTING; OBJECT DETECTION; ORGAN LOCALIZATION; TORSO CT IMAGES;

COMPUTER AIDED DIAGNOSIS; COMPUTER ARCHITECTURE; COMPUTERIZED TOMOGRAPHY; DETECTORS; MEDICAL IMAGING; OBJECT RECOGNITION; PARALLEL ALGORITHMS; PARALLEL ARCHITECTURES; TEMPLATE MATCHING; THREE DIMENSIONAL COMPUTER GRAPHICS; TISSUE;

THREE DIMENSIONAL;

EID: 84878418874     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.2007466     Document Type: Conference Paper

References (10)

1. P. Viola and M. J. Jones: Rapid Object Detection using a Boosted Cascade of Simple Features, IEEE CVPR, 2001.
2. R. Lienhart and J. Maydt: An Extended Set of Haar-like Features for Rapid Object Detection, IEEE ICIP 2002, vol. 1, pp. 900-903, 2002.
3. Y. Zheng, B. Adrian, G. Bogdan, S. Michael, C. Dorin: Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features, IEEE TMI, vol.27, pp.1668-1681, 2008.
4. H. Ling, S. K. Zhou, Y. Zheng, B. Georgescu, M. Suehling, and C. Dorin: Hierarchical, Learning-based Automatic Liver Segmentation, IEEE CVPR, 2008.
5. M. Dikmen, Y. Zhan, and X. S. Zhou: Joint Detection and Localization of Multiple Anatomical Landmarks Through Learning, SPIE Medical Imaging 2008, vol.6915, 2008.
6. A. Criminisi, J. Shotton, and S. Bucciarelli: Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes, in MICCAI workshop on Probabilistic Models for Medical Image Analysis (MICCAI-PMMIA), 2009.
7. X. Zhou, and H. Fujita: Automatic Organ Localization on X-ray CT Images by Using Ensemble Learning Techniques, in Machine Learning in Computer-aided Diagnosis: Medical Imaging Intelligence and Analysis, ed. by K. Suzuki, IGI Global, USA, pp. 403-418, 2012.
8. X. Zhou, S. Wang, H. Chen, T. Hara, R. Yokoyama, M. Kanematsu, H. Hoshi, and H. Fujita: Automatic Localization of Solid Organs on 3D CT Images by A Collaborative Majority Voting Decision Based on Ensemble Learning, Computerized Medical Imaging and Graphics, vol.36, 4, pp. 304-313, 2012 .
9. Y. Freund, and R.E.Schapire: A Decision-theoretic Generalization of On-line Learning and an Application to Boosting. In Computational Learning Theory: Eurocolt 95, Springer-Verlag, pp. 23-37, 1995.
10. T. Ojala, M. Pietikäinen, and D. Harwood: Performance Evaluation of Texture Measures with Classification based on Kullback Discrimination of Distributions, Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582 - 585, 1994.