Abstract
CBCT images suffer from acute shading artifacts primarily due to scatter. Numerous image-domain correction algorithms have been proposed in the literature that use patient-specific planning CT images to estimate shading contributions in CBCT images. However, in the context of radiosurgery applications such as gamma knife, planning images are often acquired through MRI which impedes the use of polynomial fitting approaches for shading correction. We present a new shading correction approach that is independent of planning CT images. Our algorithm is based on the assumption that true CBCT images follow a uniform volumetric intensity distribution per material, and scatter perturbs this uniform texture by contributing cupping and shading artifacts in the image domain. The framework is a combination of fuzzy C-means coupled with a neighborhood regularization term and Otsu’s method. Experimental results on artificially simulated craniofacial CBCT images are provided to demonstrate the effectiveness of our algorithm. Spatial non-uniformity is reduced from 16% to 7% in soft tissue and from 44% to 8% in bone regions. With shading-correction, thresholding based segmentation accuracy for bone pixels is improved from 85% to 91% when compared to thresholding without shading-correction. The proposed algorithm is thus practical and qualifies as a plug and play extension into any CBCT reconstruction software for shading correction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
LA Feldkamp, LC Davis, and JW Kress. Practical cone-beam algorithm. JOSA A, 1(6):612–619, 1984.
DA Jaffray and JH Siewerdsen. Cone-beam computed tomography with a flat-panel imager: initial performance characterization. Medical physics, 27(6):1311–1323, 2000.
David A Jaffray, Jeffrey H Siewerdsen, John W Wong, and Alvaro A Martinez. Flat-panel cone-beam computed tomography for image-guided radiation therapy. International Journal of Radiation Oncology* Biology* Physics, 53(5):1337–1349, 2002.
Daniel Létourneau, John W Wong, Mark Oldham, Misbah Gulam,Lindsay Watt, David A Jaffray, Jeffrey H Siewerdsen, and Alvaro A Martinez. Cone-beam-CT guided radiation therapy: technical implementation. Radiotherapy and Oncology, 75(3):279–286, 2005.
Awais Ashfaq. Segmentation of cone beam CT in stereotactic radiosurgery. Master’s thesis, KTH, School of Technology and Health (STH), 2016.
Thomas E Marchant, Christopher J Moore, Carl G Rowbottom, Ranald I Mackay, and Peter C Williams. Shading correction algorithm for improvement of cone-beam CT images in radiotherapy. Physics in medicine and biology, 53(20):5719, 2008.
Stephen Brunner, Brian E Nett, Ranjini Tolakanahalli, and Guang-Hong Chen. Prior image constrained scatter correction in cone-beam computed tomography image-guided radiation therapy. Physics in medicine and biology, 56(4):1015, 2011.
Tianye Niu, Ahmad Al-Basheer, and Lei Zhu. Quantitative cone-beam CT imaging in radiation therapy using planning CT as a prior: first patient studies. Medical physics, 39(4):1991–2000, 2012.
Tianye Niu, Mingshan Sun, Josh Star-Lack, Hewei Gao, Qiyong Fan, andLei Zhu. Shading correction for on-board cone-beam CT in radiation therapy using planning MDCT images. Medical physics, 37(10):5395–5406, 2010.
Maria A Schmidt and Geoffrey S Payne. Radiotherapy planning using MRI. Physics in medicine and biology, 60(22):R323, 2015.
Pengwei Wu, Xiaonan Sun, Hongjie Hu, Tingyu Mao, Wei Zhao, Ke Sheng, Alice ACheung, and Tianye Niu. Iterative CT shading correction with no prior information. Physics in medicine and biology, 60(21):8437, 2015.
Richard Nock and Frank Nielsen. On weighting clustering. IEEE transactions on pattern analysis and machine intelligence, 28(8):1223–1235, 2006.
Nobuyuki Otsu. A threshold selection method from gray-level histograms. Automatica, 11(285-296):23–27, 1975.
Deng-Yuan Huang and Chia-Hung Wang. Optimal multi-level thresholding using a two-stage Otsu optimization approach. Pattern Recognition Letters, 30(3):275–284, 2009.
Keisuke Usui, Yasunobu Ichimaru, Yasuhiro Okumura, Katsuki Murakami, Makoto Seo, Etsuo Kunieda, and Koichi Ogawa. Dose calculation with a cone beam CT image in image-guided radiation therapy. Radiological physics and technology, 6(1):107–114, 2013.
Wei Zhao, Stephen Brunner, Kai Niu, Sebastian Schafer, Kevin Royalty, and Guang-Hong Chen. A patient-specific scatter artifacts correction method. In SPIE Medical Imaging, pages 903310–903310. International Society for Optics and Photonics, 2014.
Edwin T Jaynes. Bayesian methods: General background. 1986.
Mohamed N Ahmed, Sameh M Yamany, Nevin Mohamed, Aly A Farag, and ThomasMoriarty. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE transactions on medical imaging, 21(3):193–199, 2002.
Jeetashree Aparajeeta, Pradipta Kumar Nanda, and Niva Das. Bias field estimation and segmentation of MR image using modified fuzzy-C means algorithms. In Advances in Pattern Recognition (ICAPR), 2015 Eighth International Conference on, pages 1–6. IEEE, 2015.
Bei Yan, Mei Xie, Jing-Jing Gao, and Wei Zhao. A fuzzy C-means based algorithm for bias field estimation and segmentation of MR images. In Apperceiving Computing and Intelligence Analysis (ICACIA), 2010 International Conference on, pages 307–310. IEEE, 2010.
Dzung L Pham and Jerry L Prince. Adaptive fuzzy segmentation of magnetic resonance images. IEEE transactions on medical imaging, 18(9):737–752, 1999.
Stephen J Wright. Coordinate descent algorithms. Mathematical Programming, 151(1):3–34, 2015.
Andrei Nikolaevich Tikhonov. Regularization of incorrectly posed problems. SOVIET MATHEMATICS DOKLADY, 1963.
George W Snedecor and Witiiam G Cochran. Statistical methods, 8thEdn. Ames: Iowa State Univ. Press Iowa, 1989.
Wei Zhao, Stephen Brunner, Kai Niu, Sebastian Schafer, Kevin Royalty, and Guang-Hong Chen. Patient-specific scatter correction for flat-panel detector-based cone-beam CT imaging. Physics in medicine and biology, 60(3):1339, 2015.
Wei Zhao, Don Vernekohl, Jun Zhu, Luyao Wang, and Lei Xing. A model-based scatter artifacts correction for cone beam CT. Medical physics, 43(4):1736–1753, 2016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ashfaq, A., Adler, J. (2017). A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images. In: Badnjevic, A. (eds) CMBEBIH 2017. IFMBE Proceedings, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-10-4166-2_81
Download citation
DOI: https://doi.org/10.1007/978-981-10-4166-2_81
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4165-5
Online ISBN: 978-981-10-4166-2
eBook Packages: EngineeringEngineering (R0)