Advanced search
Publication Cover
International Journal of Intelligent Computing in Medical Sciences & Image Processing Volume 5, 2013 - Issue 2: Special Issue on Computer Intelligence
Journal homepage
12
Views
2
CrossRef citations to date
0
Altmetric
Special Issue on Computer Intelligence

Newborn Brain MR Image Segmentation Using Deformable Surface Model Based on Fuzzy Knowledge Models

Syoji KobashiHimeji Initiative in Computational Medical and Health Technology, Graduate School of Engineering, University of Hyogo, Hyogo, Japan;WPI Immunology Frontier Research Center, Osaka University, Osaka, Japan
,
Kei KuramotoHimeji Initiative in Computational Medical and Health Technology, Graduate School of Engineering, University of Hyogo, Hyogo, Japan;WPI Immunology Frontier Research Center, Osaka University, Osaka, Japan
,
Yuki WakataHyogo College of Medicine, Hyogo, Japan
,
Kumiko AndoHyogo College of Medicine, Hyogo, Japan
,
Reiichi IshikuraHyogo College of Medicine, Hyogo, Japan
,
Tomomoto IshikawaIshikawa Hospital, Hyogo, Japan
,
Shozo HirotaHyogo College of Medicine, Hyogo, Japan
&
Yutaka HataHimeji Initiative in Computational Medical and Health Technology, Graduate School of Engineering, University of Hyogo, Hyogo, Japan;WPI Immunology Frontier Research Center, Osaka University, Osaka, Japan
 show all
Pages 115-124 | Published online: 04 Dec 2013

REFERENCES

  • Atkins, M. S., & Mackiewich, B. T. (1998). Fully automatic segmentation of the brain in MRI. IEEE Transactions on Medical Imaging, 17, 98–107.
  • Barkovich, A. J. (2000). Concepts of myelin and myelination in neuroradiology. American Journal of Neuroradiology, 21, 1099–1109.
  • Boesen, K., Rehm, K., Schaper, K., Stoltzner, S., Woods, R., Lüders, E., & Rottenberg, D. (2004). Quantitative comparison of four brain extraction algorithms. NeuroImage, 22, 1255–1261.
  • Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica, 82, 239–259.
  • Gousias, I. S., Rueckert, D., Heckemann, R. A., Dyet, L. E., Boardman, J. P., Edwards, A. D., & Hammers, A. (2008). Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest. NeuroImage, 40, 672–684.
  • Hashioka, A., Kobashi, S., Kuramoto, K., Wakata, Y., Ando, K., Ishikura, R., …, Hata, Y. (2012). A neonatal brain MR image template of 1 week newborn. International Journal of Computer Assisted Radiology and Surgery, 7, 273–280.
  • Hashioka, A., Kuramoto, K., Kobashi, S., Wakata, Y., Ando, K., Ishikura, R., & Hata, Y. (2012). Fuzzy object shape model for newborn brain MR image segmentation. Proceedings of the 6th international conference on soft computing and intelligent systems and 13th international symposium on advanced intelligent systems (pp. 1253–1258)
  • Hata, E., Kobashi, H., Hirano, S., Kitagaki, S., & Mori, Y. (2000). Automated segmentation of human brain MR images aided by fuzzy information granulation and fuzzy inference. IEEE Transactions on Systems, Man Cybernetics, Part C (Applications Reviews), 30, 381–395.
  • Jenkinson, M., Pechaud, M., & Smith, S. (2005). BET2: MR-based estimation of brain, skull and scalp surfaces. Eleventh annual meeting of the organization for human brain mapping.
  • Kitagaki, H., Mori, E., Yamaji, S., Ishii, K., Hirono, N., Kobashi, S., & Hata, Y. (1998). Frontotemporal dementia and Alzheimer disease: Evaluation of cortical atrophy with automated hemispheric surface display generated with MR images. Radiology, 208, 431–439.
  • Kubat, M., Holte, R., & Matwin, S. (1997). Learning when negative examples abound. Machine Learning: ECML-97Lecture Notes in Computer Science, (1224, 146–153). Retrieved from https://doi.org/http://link.springer.com/chapter/10.1007%2F3-540-62858-4_79.
  • Prastawa, M., Gilmore, J. H., Lin, W., & Gerig, G. (2005). Automatic segmentation of MR images of the developing newborn brain. Medical Image Analysis, 9, 457–466.
  • Song, Z., Awate, S. P., Licht, D. J., & Gee, J. C. (2007). Clinical newborn brain MRI segmentation using adaptive nonparametric data models and intensity-based markov priorsLecture Notes in Computer Science, (4791, 883–890). Retrieved from https://doi.org/http://www.ncbi.nlm.nih.gov/pubmed/18051142.
  • Studholme, C., Hill, D. L. G., & Hawkes, D. J. (1997). Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. Medical Physics, 24, 25–35.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

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.