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International Journal of Biomedical Imaging
Volume 2007 (2007), Article ID 25182, 9 pages
http://dx.doi.org/10.1155/2007/25182
Research Article

Molecular Image Segmentation Based on Improved Fuzzy Clustering

Department of Electronic Engineering, Fudan University, Shanghai 200433, China

Received 18 January 2007; Revised 28 April 2007; Accepted 17 July 2007

Academic Editor: Jie Tian

Copyright © 2007 Jinhua Yu and Yuanyuan Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

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  • Christine Decaestecker, Xavier Moles Lopez, Nicky D'Haene, Isabelle Roland, Saad Guendouz, Christophe Duponchelle, Alix Berton, Olivier Debeir, and Isabelle Salmon, “Requirements for the valid quantification of immunostains on tissue microarray materials using image analysis,” Proteomics, vol. 9, no. 19, pp. 4478–4494, 2009. View at Publisher · View at Google Scholar
  • Zulaikha Beevi, Mohammed Sathik, Senthamaraikannan, and Jaseema Yasmin, “A robust fuzzy clustering technique with spatial neighborhood information for effective medical image segmentation: An efficient variants of fuzzy clustering technique with spatial information for effective noisy medical image segmentation,” 2010 2nd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2010, 2010. View at Publisher · View at Google Scholar
  • Saoussen Belhassen, and Habib Zaidi, “A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET,” Medical Physics, vol. 37, no. 3, pp. 1309, 2010. View at Publisher · View at Google Scholar
  • S Ahmed, K M Iftekharuddin, and A Vossough, “Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI,” IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 2, pp. 206–213, 2011. View at Publisher · View at Google Scholar
  • Jemila R. Rose, and Allwin, “Ultrasound cervical cancer based abnormality segmentation using adaptive fuzzy c-mean clustering,” Academic Journal of Cancer Research, vol. 6, no. 1, pp. 01–07, 2013. View at Publisher · View at Google Scholar