A Software Tool Design for Cerebral Infarction of MR Images

Authors: Kyoung-Jong Park, Woong-Gi Jeon, Hee-Cheol Kim, Dong-Eog Kim, Heung-Kook Choi

Abstract:

The brain MR imaging-based clinical research and analysis system were specifically built and the development for a large-scale data was targeted. We used the general clinical data available for building large-scale data. Registration period for the selection of the lesion ROI and the region growing algorithm was used and the Mesh-warp algorithm for matching was implemented. The accuracy of the matching errors was modified individually. Also, the large ROI research data can accumulate by our developed compression method. In this way, the correctly decision criteria to the research result was suggested. The experimental groups were age, sex, MR type, patient ID and smoking which can easily be queries. The result data was visualized of the overlapped images by a color table. Its data was calculated by the statistical package. The evaluation for the utilization of this system in the chronic ischemic damage in the area has done from patients with the acute cerebral infarction. This is the cause of neurologic disability index location in the center portion of the lateral ventricle facing. The corona radiate was found in the position. Finally, the system reliability was measured both inter-user and intra-user registering correlation.

Keywords: Software tool design, Cerebral infarction, Brain MR image, Registration

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1075206

References:

[1] The Korea National Statistical Office, The cause of death statistics (Nationwide,2009), 2010.
[2] I. D. Dinov, M. S. Mega, P. M. Thompson, Linda. Lee, Roger. P. Woods, C. J. Holmes, DW. L. and Sumners, A. W. Toga, "Analyzing Functional Brain Images in a Probabilistic Atlas: A Validation of Subvolume Thresholding," Journal of Computer Assisted Tomography, Vol. 24(1), pp.128-138, Feb. 2000.
[3] A. W. Toga and P. M. Thompson, "Maps of the Brain," Advances in Biomedical Imaging, Vol 265(2), pp 37-53, Apr. 2001.
[4] T. Ball, B. Rahm, S. B. Eickhoff, A. Schulze-Bonhage, O. Speck, "Response Properties of Human Amugdala Subregions: Evidence Based on Functional MRI Combined with Probabilistic Anatomical Maps," PLoS ONE, Vol 2(3), Mar. 2007.
[5] M. Brett, I. S. Johnsrude, and A. M. Owen, "The problem of functional localization in the human brain," Nature Reviews Neuroscience, Vol 3, pp 243-249, Mar. 2002.
[6] J. S. Kim, D. S. Lee, B. I. Lee, J. S. Lee, H. W. Shin, JK Chung, and M. C. Lee, "Probabilistic Anatomical Labeling of Brain Structures Using Statistical Probabilistic Anatomical Maps," The Korean Society of Nuclear Medicine, Vol 36(6), Dec. 2002.
[7] LG. H. Derek, G. B. Philipp, and H. Mark, "Medical Image Registration.," Physics in Medicine and Biology, Vol 46(3), pp 1-45, 2001.
[8] C. R. Maurer and M. Fitzpatrick, "A Review of Medical Image Registration.," American Association of Neurological Surgeons, pp 17-44, 1993.
[9] PA. V. Elsen, EJ. D. Pol, M. A. Viergever, "Medical Image Matching: a review with classification.,