Automated separation of gray and white matter in brain MRIs by fastened segments of geodesic contours

Gray matter (GM) and white matter (WM) are adjacent tissues in the brain separated by an interface. Extraction of this boundary is very important for quantitative analysis and monitoring of atrophy. A perfect capture for this purpose is a missing vital item for which research continues. In order to get close to such precision, two novel systems are presented for segmentation of cortical GM and WM in brain MRIs. The system imitates human perceptual sensitiveness to contrast, which is the basic principle of edge detection algorithms, and completes its shortcoming feature of segmentation. As well as being completely automatic, the system is also unsupervised. The correct GM-WM boundary rate gets close to 77% and the segmentation accuracy is over 95%, which are promising results. In comparison tests with SPM, the proposed technique showed 6% higher accuracy with both noisy and normal data and better recovery of small cavities in sulci, being confirmed by experts drawings.

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[1] Tofts P. Quantitative MRI of the Brain: Measuring Changes Caused by Disease. Chichester, UK: John Wiley & Sons, 2003.
[2] Lao Z, Shen D, Liu D, Jawad AF, Melhem ER, Launer LJ, Bryan RN, Davatzikos C. Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Acad Radiol 2008; 15: 300-313.
[3] Iskurt A, Becerikli Y, Mahmutyazıcıo˘glu K. Automatic identification of landmarks for standard slice positioning in brain MRI. J Magn Reson Imaging 2011; 34: 499-510.
[4] Yu ZQ, Zhu Y, Yang J, Zhu YM. A hybrid region-boundary model for cerebral cortical segmentation in MRI. Comput Med Imag Graph 2006; 30: 197-208.
[5] De Boer R, Vrooman HA, Van der Lijn F, Vernooij MW, Ikram MA, Van der Lugt A, Breteler M, Niessen WJ. White matter lesion extension to automatic brain tissue segmentation on MRI. NeuroImage 2009; 45: 1151-1161.
[6] Shen S, Sandham W, Granat M, Sterr A. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE T Inf Technol B 2005; 9: 459-67.
[7] Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, Metaxas D, Whitaker R. Engineering and algorithm design for an image processing api: a technical report on ITK - the insight toolkit. In: Proceedings of Medicine Meets Virtual Reality; 2002.
[8] Friston KJ, Ashburner J, Kiebel SJ, Nichols TE, Penny WD. Statistical Parametric Mapping: The Analysis of Functional Brain Images. New York, NY, USA: Elsevier Ltd.; 2007.
[9] Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM. Bayesian analysis of neuroimaging data in FSL. NeuroImage 2009; 45: 173-186.
[10] Dzung LP, Chenyang X, Jerry LP. Current methods in medical image segmentation. Annu Rev Biomed Eng 2000; 2: 315-337.
[11] Cuadra MB, Cammoun L, Butz T, Cuisenaire O, Thiran JP. Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images. IEEE T Med Imaging 2005; 24: 1548-1565.
[12] Kochunov P, Mangin JF, Coyle T, Lancaster J, Thompson P, Riviere D, Cointepas Y, Regis J, Schiosser A, Royall DR et al. Age-related morphology trends of cortical sulci. Hum Brain Mapp 2005; 26: 210-220
[13] Larsen R, Nielsen M, Sporring J. A fast and automatic method to correct intensity inhomogeneity in MR brain images. Lect Notes Comp Sci 2006; 4191: 324-331.
[14] Lewis EB, Fox NC. Correction of differential intensity inhomogeneity in longitudinal MR images. NeuroImage 2004; 23: 75-83.
[15] Iskurt A, Becerikli Y. Automatic extraction of cortical gray matter by geodesic contours. In: International Symposium on Information, Communication and Automation Technologies; 2011. pp. 1-6.
[16] Iskurt A, Becerikli Y. Fast and automatic brain extraction by geodesic passive contours. Int J Innov Comput I 2010; 6: 5665-5684.
[17] Boesen K, Rehm K, Schaper K, Stoltzner S, Woods R, Luders E, Rottenberg D. Quantitative comparison of four brain extraction algorithms. NeuroImage 2004; 22: 1255-1261.
[18] Ge Y, Grossman RI, Babb JS, Rabin ML, Mannon LJ, Kolson DL. Age-related total gray matter and white matter changes in normal adult brain. Part I: Volumetric MR imaging analysis. Am J Neuroradiol 2002; 23: 1327-1341.
[19] Kwan RKS, Evans AC, Pike GB. MRI simulation-based evaluation of image-processing and classification methods. IEEE T Med Imaging 1999; 18: 1085-1097.
[20] Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, Evans AC. Design and construction of a realistic digital brain phantom. IEEE T Med Imaging 1998; 17: 463-468.
[21] Liao L, Lin T, Li B. MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recogn Lett 2008; 29: 1580-1588.
[22] Angelini ED, Song T, Mensh BD, Laine AF. Brain MRI segmentation with multiphase minimal partitioning: a comparative study. Int J Biomed Imaging 2007; 2007: 10526.
[23] Huang A, Abugharbieh R, Tam R, Traboulsee A. Automatic MRI Brain Tissue Segmentation Using a Hybrid Statistical and Geometric Model. Arlington, VA, USA: ISBI, 2006.
[24] El-Zehiry NY, Elmaghraby A. Brain MRI tissue classification using graph cut optimization of the Mumford-Shah functional. In: Proceedings of Image and Vision Computing; 2007. pp. 321-326.
[25] Ohnishi T, Matsuda H, Tabira T, Asada T, Uno M. Changes in brain morphology in Alzheimer disease and normal aging: is Alzheimer disease an exaggerated aging process? Am J Neuroradiol 2001; 22: 1680-1685.