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BioMed Research International
Volume 2014 (2014), Article ID 963032, 13 pages
http://dx.doi.org/10.1155/2014/963032
Research Article

Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map

1Department of Electrical Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan
2Department of Computer Science and Information Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan
3Department of Neurology, Landseed Hospital, Pingzhen City, Taoyuan County 32449, Taiwan
4Department of Neurology, National Taiwan University Hospital, Taipei City 10002, Taiwan
5Department of Medical Imaging, Landseed Hospital, Pingzhen City, Taoyuan County 32449, Taiwan
6Department of Radiology, Taipei Medical University-Shuang Ho Hospital, New Taipei City 23561, Taiwan
7Department of Neurology, Chi-Mei Medical Center, Tainan City 71004, Taiwan
8Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada T2N 1N4
9Epilepsy Center, Buddhist Tzu Chi General Hospital, Hualian City, Hualian County 97002, Taiwan
10Biomedical Electronics Translational Research Center, National Chiao Tung University, Hsinchu City 30010, Taiwan
11Department of Neurology, Chung Shan Medical University Hospital, Taichung City 40201, Taiwan

Received 5 November 2013; Revised 31 December 2013; Accepted 9 January 2014; Published 12 March 2014

Academic Editor: George Pengas

Copyright © 2014 Jang-Zern Tsai et al. 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.

Linked References

  1. S. C. Smith Jr., “Reducing the global burden of ischemic heart disease and stroke: a challenge for the cardiovascular community and the United Nations,” Circulation, vol. 124, no. 3, pp. 278–279, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. G. Vogt, R. Laage, A. Shuaib, and A. Schneider, “Initial lesion volume is an independent predictor of clinical stroke outcome at day 90: an analysis of the Virtual International Stroke Trials Archive (VISTA) database,” Stroke, vol. 43, no. 5, pp. 1266–1272, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. S. F. Zaidi, A. Aghaebrahim, X. Urra, M. A. Jumaa, B. Jankowitz, and M. Hammer, “Final infarct volume is a stronger predictor of outcome than recanalization in patients with proximal middle cerebral artery occlusion treated with endovascular therapy,” Stroke, vol. 43, no. 12, pp. 3238–3244, 2012. View at Google Scholar
  4. S. Rangaraju, K. Owada, A. R. Noorian, R. G. Nogueira, F. Nahab, and B. A. Glenn, “Comparison of final infarct volumes in patients who received endovascular therapy or intravenous thrombolysis for acute intracranial large-vessel occlusions,” Journal of the American Medical Association, vol. 70, no. 7, pp. 831–836, 2013. View at Google Scholar
  5. M. Al-Khaled, C. Matthis, T. F. Munte, and J. Eggers, “QugSS2-Study. The incidence and clinical predictors of acute infarction in patients with transient ischemic attack using MRI including DWI,” Neuroradiology, vol. 55, no. 2, pp. 157–163, 2013. View at Google Scholar
  6. M. Lettau and M. Laible, “3-T high-b-value diffusion-weighted MR imaging in hyperacute ischemic stroke,” Journal of Neuroradiology, vol. 40, no. 3, pp. 149–157, 2013. View at Google Scholar
  7. H. L. Lutsep, G. W. Albers, A. DeCrespigny, G. N. Kamat, M. P. Marks, and M. E. Moseley, “Clinical utility of diffusion-weighted magnetic resonance imaging in the assessment of ischemic stroke,” Annals of Neurology, vol. 41, no. 5, pp. 574–580, 1997. View at Publisher · View at Google Scholar · View at Scopus
  8. V. F. Newcombe, T. Das, and J. J. Cross, “Diffusion imaging in neurological disease,” Journal of Neurology, vol. 260, no. 1, pp. 335–342, 2013. View at Google Scholar
  9. N. Perez de la Ossa, M. Hernandez-Perez, S. Domenech, P. Cuadras, A. Massuet, and M. Millan, “Hyperintensity of distal vessels on FLAIR is associated with slow progression of the infarction in acute ischemic stroke,” Cerebrovascular Diseases, vol. 34, no. 5-6, pp. 376–384, 2012. View at Google Scholar
  10. K. N. Bhanu Prakash, V. Gupta, H. Jianbo, and W. L. Nowinski, “Automatic processing of diffusion-weighted ischemic stroke images based on divergence measures: slice and hemisphere identification, and stroke region segmentation,” International Journal of Computer Assisted Radiology and Surgery, vol. 3, no. 6, pp. 559–570, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Li, J. Tian, E. Li, and J. Dai, “Robust unsupervised segmentation of infarct lesion from diffusion tensor MR images using multiscale statistical classification and partial volume voxel reclassification,” NeuroImage, vol. 23, no. 4, pp. 1507–1518, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. K. Bhanu Prakash, V. Gupta, M. Bilello, N. J. Beauchamp, and W. L. Nowinski, “Identification, segmentation, and image property study of acute infarcts in diffusion-weighted images by using a probabilistic neural network and adaptive gaussian mixture model,” Academic Radiology, vol. 13, no. 12, pp. 1474–1484, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. N. Hevia-Montiel, J. R. Jimenez-Alaniz, V. Medina-Banuelos et al., “Robust nonparametric segmentation of infarct lesion from diffusion-weighted MR images,” in Proceedings of the IEEE Engineering in Medicine and Biology Society, pp. 2102–2105, 2007.
  14. V. Gupta, B. Prakash, and W. L. Nowinski, “Automatic and rapid identification of infarct slices and hemisphere in DWI scans,” Academic Radiology, vol. 15, no. 1, pp. 24–39, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Shen, A. J. Szameitat, and A. Sterr, “Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location—a 3-D automatic approach,” IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 4, pp. 532–540, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, Mass, USA, 1981.
  17. F.-I. Hsieh, L.-M. Lien, S.-T. Chen et al., “Get with the guidelines-stroke performance indicators: surveillance of stroke care in the taiwan stroke registry: get with the guidelines-stroke in Taiwan,” Circulation, vol. 122, no. 11, pp. 1116–1123, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Transactions on Medical Imaging, vol. 16, no. 2, pp. 187–198, 1997. View at Google Scholar · View at Scopus
  19. S. M. Smith, “Fast robust automated brain extraction,” Human Brain Mapping, vol. 17, no. 3, pp. 143–155, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986. View at Google Scholar
  21. Y. W. Chen, M. E. Gurol, J. Rosand et al., “Progression of white matter lesions and hemorrhages in cerebral amyloid angiopathy,” Neurology, vol. 67, no. 1, pp. 83–87, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. H.-H. Chang, A. H. Zhuang, D. J. Valentino, and W.-C. Chu, “Performance measure characterization for evaluating neuroimage segmentation algorithms,” NeuroImage, vol. 47, no. 1, pp. 122–135, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, no. 3, pp. 297–302, 1945. View at Google Scholar
  24. R. G. Steen, T. Emudianughe, M. Hunte et al., “Brain volume in pediatric patients with sickle cell disease: evidence of volumetric growth delay?” American Journal of Neuroradiology, vol. 26, no. 3, pp. 455–462, 2005. View at Google Scholar · View at Scopus
  25. K. O. McGraw and S. P. Wong, “Forming inferences about some intraclass correlation coefficients,” Psychological Methods, vol. 1, no. 1, pp. 30–46, 1996. View at Google Scholar · View at Scopus
  26. P. Marcoň, K. Bartušek, R. Kořínek, and Z. Dokoupil, “Correction of artifacts in diffusion-weighted MR images,” in Proceedings of the 34th International Conference on Telecommunications and Signal Processing (TSP '11), pp. 391–397, August 2011. View at Publisher · View at Google Scholar · View at Scopus