OJMI  Vol.3 No.2 , June 2013
Ideal Midline Detection Using Automated Processing of Brain CT Image
Abstract: Brain ideal midline estimation is vital in medical image processing, especially in analyzing the severity of a brain injury in clinical environments. We propose an automated computer-aided ideal midline estimation system with a two-step process. First, a CT Slice Selection Algorithm (SSA) can automatically select an appropriate subset of slices from a large number of raw CT images using the skull’s anatomical features. Next, an ideal midline detection is implemented on the selected subset of slices. An exhaustive symmetric position search is performed based on the anatomical features in the detection. In order to enhance the accuracy of the detection, a global rotation assumption is applied to determine the ideal midline by fully considering the connection between slices. Experimental results of the multi-stage algorithm were assessed on 3313 CT slices of 70 patients. The accuracy of the proposed system is 96.9%, which makes it viable for use under clinical settings.
Cite this paper: X. Qi, A. Belle, S. Shandilya, W. Chen, C. Cockrell, Y. Tang, K. Ward, R. Hargraves and K. Najarian, "Ideal Midline Detection Using Automated Processing of Brain CT Image," Open Journal of Medical Imaging, Vol. 3 No. 2, 2013, pp. 51-59. doi: 10.4236/ojmi.2013.32007.

[1]   G. C. S. Ruppert, L. Teverovskiyz, C. Yu, A. X. Falcao, and Y. Liu, “A New Symmetry-Based Method for Mid-Sagittal Plane Extraction in Neuroimages,” IEEE International Symposium on Biomedical Imaging, Chicago, 30 March 2011, pp. 285-288.

[2]   J. S. Broder, “Head Computed Tomography Interpretation in Trauma: A Primer,” The Psychiatric Clinics of North America, Vol. 33, No. 4, 2010, pp. 821-854. doi:10.1016/j.psc.2010.08.006

[3]   Q. Hu and W. L. Nowinski, “A Rapid Algorithm for Robust and Automatic Extraction of the Midsagittal Plane of the Human Cerebrum from Neuroimages Based on Local Symmetry and Outlier Removal,” NeuroImage, Vol. 20, No.4, 2003, pp. 2153-2165. doi:10.1016/j.neuroimage.2003.08.009

[4]   C. C. Liao, F. Xiao, J. Wong, and I. Chiang, “A Simple Genetic Algorithm for Tracing the Deformed Midline on a Single Slice of Brain CT Using Quadratic Bezier Curves,” Sixth IEEE International Conference on Data Mining Workshops, Hong Kong, 18-22 December 2006, pp. 463-467. doi:10.1109/ICDMW.2006.22

[5]   F. P. G. Bergo, G. C. S. Ruppert and A. X. Falcao, “Fast and Robust Mid-Sagittal Plane Location in 3D MR Images of the Brain,” International Conference on Bioinspired Systems and Signal Processing, 2008, pp. 92-99.

[6]   R. Guillemaud, P. Marais, A. Zisserman, B. McDonald, T. J. Crow and M. Brady, “A Three Dimensional Mid-Sagittal Plane for Brain Asymmetry Measurement,” Schizophrenia Research, Vol. 18, No. 2, 1996, pp. 183-184. doi:10.1016/0920-9964(96)85575-7

[7]   W. Chen, R. Smith, S. Y. Ji and K. Najarian, “Automated Segmentation of Lateral Ventricales in Brain CT Images,” IEEE International Conference on Bioinformatics and Biomeidcine Workshops, Philadelphia, 3-5 November 2008, pp. 48-55.

[8]   W. Chen, R. Smith, S. Y. Ji, K. Ward and K. Najarian, “Automated Ventricular Systems Segmentation in Brain CT Images by Combining Low-Level Segmentation and High-Level Template Matching,” BMC Medical Informatics and Decision Making, Vol. 9, No. 1, 2009, p. S4. doi:10.1186/1472-6947-9-S1-S4

[9]   R. M. Haralick and L. G. Shapiro, “Computer and Robot Vision,” 1992.

[10]   L. Teverovskiy and Y. Liu, “Truly 3D Mid-Sagittal Plane Extraction for Robust Neuroimage Registration,” The 3rd IEEE International Symposium on Biomedical Imaging, April 2006, pp. 860-863.