Ventricle Boundary in CT: Partial Volume Effect and Local Thresholds

International Journal of Biomedical Imaging - Tập 2010 Số 1 - 2010
Ihar Volkau1, Fiftarina Puspitasari1, Wiesław L. Nowinski1
1Biomedical Imaging Laboratory, Agency for Science, Technology and Research, Matrix, Singapore

Tóm tắt

We present a mathematical frame to carry out segmentation of cerebrospinal fluid (CSF) of ventricular region in computed tomography (CT) images in the presence of partial volume effect (PVE). First, the image histogram is fitted using the Gaussian mixture model (GMM). Analyzing the GMM, we find global threshold based on parameters of distributions for CSF, and for the combined white and grey matter (WGM). The parameters of distribution of PVE pixels on the boundary of ventricles are estimated by using a convolution operator. These parameters are used to calculate local thresholds for boundary pixels by the analysis of contribution of the neighbor pixels intensities into a PVE pixel. The method works even in the case of an almost unimodal histogram; it can be useful to analyze the parameters of PVE in the ground truth provided by the expert.

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Tài liệu tham khảo

10.1117/1.1631315

Otsu N., 1979, A threshold selection method from gray-level histogram, IEEE Transactions on Systems Man and Cybernetics, 8, 62, 10.1109/TSMC.1979.4310076

Liao P.-S., 2001, A fast algorithm for multilevel thresholding, Journal of Information Science and Engineering, 17, 713

WangH.andDongY. ZhouL. An improved image segmentation algorithm based on otsu method 6625 International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications 2008 66250I-1–66250I-8 Proceedings of SPIE 2-s2.0-0033284733 https://doi.org/10.1117/12.790781.

10.1145/321119.321123

10.1111/j.1749-6632.1965.tb11715.x

10.1109/TSMC.1983.6313118

10.1016/0165-1684(80)90020-1

10.1016/0734-189X(85)90125-2

10.1016/0734-189X(85)90133-1

10.1006/nimg.2001.0800

10.1109/42.700743

BaillardC. HellierP. andBarillotC. Segmentation of 3D brain structures using level sets and dense registration Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis 2000 94–101 2-s2.0-0033689952.

10.1109/42.921475

10.1016/j.neuroimage.2003.09.029

10.1007/s12021‐009‐9046‐1

LeeT. H. [email protected] FauziM. F. A. andKomiyaR. Segmentation of CT brain images using K-means and EM clustering Proceedings of the Computer Graphics Imaging and Visualisation Modern Techniques and Applications (CGIV ′08) 2008 339–344 2-s2.0-18344401406 https://doi.org/10.1109/CGIV.2008.17.

KaipingW. [email protected] BinH. [email protected] TaoZ. andXianjunS. A novel method for segmentation of CT head images Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE ′07) 2007 717–720 2-s2.0-0029535737 https://doi.org/10.1109/ICBBE.2007.187.

HuQ. [email protected] QianG. [email protected] AzizA. [email protected] NowinskiW. L. [email protected] Segmentation of brain from computed tomography head images Proceedings of the 27th Annual International Conference of the IEEE-MBS Engineering in Medicine and Biology 2005 3375–3378 2-s2.0-0027608265.

ChenW. [email protected] SmithR. [email protected] JiS.-Y. andNajarianK. [email protected] Automated segmentation of lateral ventricles in brain CT images Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW ′08) 2008 48–55 2-s2.0-0035263302 https://doi.org/10.1109/BIBMW.2008.4686208.

10.1002/0471721182

10.1111/j.2517-6161.1977.tb01600.x

10.1007/BF00133570

Blatter D. D., 1997, MR-based brain and cerebrospinal fluid measurement after traumatic brain injury: correlation with neuropsychologicai outcome, American Journal of Neuroradiology, 18, 1

10.1006/cbmr.1995.1020

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International Journal of Biomedical Imaging

Tập 2010 Số 1

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