Advanced search
Publication Cover
Computer Assisted Surgery Volume 22, 2017 - Issue sup1: Innovation in Biomedical Science and Engineering
Submit an article Journal homepage
1,554
Views
0
CrossRef citations to date
0
Altmetric
Innovation in Biomedical Science and Engineering

Brain MR image segmentation using NAMS in pseudo-color

Hua LiSchool of Software Engineering, Huazhong University of Science and Technology, Wuhan, China;
,
Chuanbo ChenSchool of Software Engineering, Huazhong University of Science and Technology, Wuhan, China;
,
Shaohong FangSchool of Software Engineering, Huazhong University of Science and Technology, Wuhan, China; Correspondence[email protected]
&
Shengrong ZhaoSchool of Information, Qilu University of Technology, Jinan, China
Pages 170-175 | Published online: 28 Oct 2017

References

  • Erdt M, Steger S, Sakas G. Regmentation: a new view of image segmentation and registration. J Radiat Oncol Informatics. 2012;4:1–23.
  • Khoo VS, Dearnaley DP, Finnigan DJ, et al. Magnetic resonance imaging (mri): considerations and applications in radiotherapy treatment planning. Radiother Oncol. 1997;42:1–15.
  • Grimson WEL, Ettinger G, Kapur T, et al. Utilizing segmented mri data in image-guided surgery. Int J Patt Recogn Artif Intell. 1997;11:1367–1397.
  • Zhang Z, Zhu Q, Xie Y. Mri segmentation of brain tissue based on spatial prior and neighboring pixels affinities. In: Pattern Recognition (ICPR), 2012 21st International Conference. IEEE; Tsukuba, Japan; 2012: 89–92.
  • Wang YH, Guan YH. Application of medical image segmentation technology based on fuzzy set theory. Comput Technol Develop. 2008;11:223–225.
  • Sharma N, Ray AK, Shukla KK, et al. Automated medical image segmentation techniques. J Med Phys. 2010;35:3.
  • Gordillo N, Montseny E, Sobrevilla P. State of the art survey on mri brain tumor segmentation. J Magn Reson Imaging. 2013;31:1426–1438.
  • Son LH, Tuan TM. A cooperative semi-supervised fuzzy clustering framework for dental x-ray image segmentation. Expert Syst Appl. 2016;46:380–393.
  • Gilanie G, Attique M, Naweed S. Object extraction from t2 weighted brain mr image using histogram based gradient calculation. Pattern Recognit Lett. 2013;34:1356–1363.
  • Kalaiselvi T, Somasundaram K. Fuzzy c-means technique with histogram based centroid initialization for brain tissue segmentation in MRI of head scans. In: Humanities, Science & Engineering Research (SHUSER), 2011 International Symposium on. IEEE; Kuala Lumpur, Malaysia; 2011: 149–154.
  • Portela NM, Cavalcanti GD, Ren TI. Semi-supervised clustering for MR brain image segmentation. Expert Syst Appl. 2014;41:1492–1497.
  • Mahmood Q, Chodorowski A, Persson M. Automated MRI brain tissue segmentation based on mean shift and fuzzy c-means using a priori tissue probability maps. IRBM. 2015;36:185–196.
  • Hayat A, Ahmed A. MR Brain Image Segmentation Based on Unsupervised and Semi-Supervised Fuzzy Clustering Methods. In: 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. Gold Coast, QLD, Australia.
  • Luo W, Heileman GL, Pizano CE. A fast and robust watermarking method for JPEG images. Comput Model New Technol. 2004;8:39–47.
  • Yang J, Sheng HJ, Shao L, et al. Research on pseudo-color image generation technology of the distribution of gaseous pollutants. In: Proceedings of the SPIE. Shanghai, China. 2016: 10141–1014117.
  • “The internet brain segmentation repository (ibsr).” 2011. [Internet]. Available from: http://www.cma.mgh.harvard.edu/ibsr/
  • Chen CB, Wang GW, Sarem M. A new non-symmetry and anti-packing model and its application to image contrast enhancement. Comput Electr Eng. 2011;37:669–680.
  • Zheng YP, Sarem M. A fast algorithm for computing moments of gray images based on NAM and extended shading approach. Front Comput Sci China. 2011;5:57–65.
  • Liang H, Zhao SR, Chen CB, et al. The NAMlet transform: A novel image sparse representation method based on non-symmetry and anti-packing model. Signal Process. 2017;137:251–263.
  • Zheng YP, Zhou WW, Xie XM, et al. A fast algorithm for computing the lower order moments of binary images by using non-symmetry and anti-packing model. J Computat Inform Syst. 2013;9:1949–1956.
  • Zheng YP, Zhou WW, Mo X. A new NAM-based algorithm for computing Hu moments of binary images. J Inf Comput Sci. 2013;10:2481–2488.
  • Yin X, Shu T, Huang Q. Semi-supervised fuzzy clustering with metric learning and entropy regularization. Knowledge-Based Systems. 2012;35:304–311.
  • Hore A., Ziou D Image Quality metrics: PSNR vs. SSIM. In: Pattern Recognition (ICPR), 20th International Conference. IEEE; Istanbul, Turkey; 2010: 2366–2369.
  • Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297–302.
  • Huttenlocher DP, Klanderman GA, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. 1993;15:850–863.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.