Multiple-Channel Local Binary Fitting Model for Medical Image Segmentation

GUO Qi; WANG Long; SHEN Shuting

doi:10.1049/cje.2015.10.023

Volume 24 Issue 4

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2015 > 24(4): 802-806

GUO Qi, WANG Long, SHEN Shuting, “Multiple-Channel Local Binary Fitting Model for Medical Image Segmentation,” Chinese Journal of Electronics, vol. 24, no. 4, pp. 802-806, 2015, doi: 10.1049/cje.2015.10.023

Citation:

GUO Qi, WANG Long, SHEN Shuting, “Multiple-Channel Local Binary Fitting Model for Medical Image Segmentation,” Chinese Journal of Electronics, vol. 24, no. 4, pp. 802-806, 2015, doi: 10.1049/cje.2015.10.023

Citation:

PDF( 498 KB)

Multiple-Channel Local Binary Fitting Model for Medical Image Segmentation

doi: 10.1049/cje.2015.10.023

1.
Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China;
2.
Health Science Center, Peking University, Beijing 100871, China

Funds: This work is supported by Natural Science Foundation of Heilongjiang Province (No.A201112).

Received Date: 2015-03-10
Rev Recd Date: 2015-06-15
Publish Date: 2015-10-10

Abstract

Abstract

This study proposes an innovative M-L (Multiple-channel local binary fitting) model for medical image segmentation. Designed to improve upon existing image segmentation models, the M-L model introduces a regional limit function to the multi-band active contour model to enable multilayer image segmentation. The Gaussian kernel function is used to improve the previous model's robustness, necessitating the use of a new initialization curve which enhances the accuracy of segmentation results. Compared to existing image segmentation methods, the proposed M-L model improves numerical stability and efficiency through the introduction of a new penalty term and an increased step length. This simulation experiment verifies the advantages of the new M-L model for improved medical image segmentation, including increased efficiency and usability of the model.
- Gaussian curve,
- Penalty term,
- Multi-band active contour model,
- Image segmentation

FullText(HTML)

References(9)

References

J.H. Kim, B.Y. Park, F. Akram, et al., "Multipass active contours for an adaptive contour map", Sensors, Vol.13, No.3, pp.3724-3738, 2013.

C. Li, C.Y. Kao, J.C. Gore, et al., "Minimization of regionscalable fitting energy for image segmentation", IEEE Transactions on Image Processing, Vol.17, No.10, pp.1940-1949, 2008.

C. Li, C.Y. Kao, J.C. Gore, et al., "Implicit active contours driven by local binary fitting energy", IEEE Conference on Computer Vision and Pattern Recognition, pp.1-7, 2007.

Q. Ge, L. Xiao, J. Zhang, et al., "Active contour model for simultaneous MR image segmentation and denoising", Digital Signal Processing, Vol.23, No.4, pp.1186-1196, 2013.

K. Lu, N. He and J. Xue, " A new geometric deformable model for medical image segmentation", Chinese Journal of Electronics, Vol.18, No.2, pp.200-204, 2009.

J.H. Kim, B.Y. Park, F. Akram, et al., "Multipass active contours for an adaptive contour map", Sensors, Vol.13, No.3, pp.3724-3738, 2013.

C. Li, C. Xu, C. Gui, et al., "Distance regularized level set evolution and its application to image segmentation", IEEE Transactions on Image Processing, Vol.19, No.12, pp.324-325, 2010.

X.F.Wang, D.S. Huang and H. Xu, "An efficient local chan-vese model for image segmentation", Pattern Recognition, Vol.43, No.3, pp.603-618, 2010.

C. He, Y. Wang, and Q. Chen, "Active contours driven by weighted region-scalable fitting energy based on local entropy", Signal Processing, Vol.92, No.2, pp.587-600, 2012.

Relative Articles

Supplements(0)

Cited By

Proportional views