JBiSE  Vol.6 No.3 A , March 2013
Automated measurement of three-dimensional cerebral cortical thickness in Alzheimer’s patients using localized gradient vector trajectory in fuzzy membership maps

Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.

Cite this paper: Tokunaga, C. , Arimura, H. , Yoshiura, T. , Ohara, T. , Yamashita, Y. , Kobayashi, K. , Magome, T. , Nakamura, Y. , Honda, H. , Hirata, H. , Ohki, M. and Toyofuku, F. (2013) Automated measurement of three-dimensional cerebral cortical thickness in Alzheimer’s patients using localized gradient vector trajectory in fuzzy membership maps. Journal of Biomedical Science and Engineering, 6, 327-336. doi: 10.4236/jbise.2013.63A042.

[1]   National Institute of Health (NIH) in USA (2010) National Institute on Aging.

[2]   Ministry of Health, Labour and Welfare (MHLW) in Japan (2010) For people, for life, for the future.

[3]   Whitwell, J.L. and Josephs, K.A. (2007) Voxel-based morphometry and its application to movement disorders. Parkinsonism and Related Disorders, 13, S406-S416. doi:10.1016/S1353-8020(08)70039-7

[4]   Whitwell, J.L., Jack Jr., C.R., Pankrats, V.S., Parisi, J.E., Knopman, D.S., Boeve, B.F., Petersen, R.C., Dickson, D.W. and Josephs, K.A. (2008) Rates of brain atrophy over time in autopsy-proven frontotemporal dementia and Alzheimer’s disease. Neuroimage, 39, 1034-1040. doi:10.1016/j.neuroimage.2007.10.001

[5]   Querbers, O., Aubry, F., Pariente, J., Lotterie, J.A., Démonet, J.F., Duret, V., Puel, M., Berry, I., Fort, J.C. and Celsis, P. (2009) Early diagnosis of Alzheimer’s disease using cortical thickness: Impact of cognitive reserve. Brain, 132, 2036-2047. doi:10.1093/brain/awp105

[6]   Seltzer, T.B., Zolnouni, P., Nunez, M., Goldman, R., Kumar, D., Ieni, J. and Richardson, S. (2004) Efficacy of donepezil in early-stage Alzheimer disease a randomized placebo-controlled. Archives Neurology, 61, 1852-1856. doi:10.1001/archneur.61.12.1852

[7]   Petersen, R.C., Thomas, R.G., Grundman, M., Bennett, D., Doody, R., Ferris, S., Galasko, D., Jin, S., Kaye, J., Levey, A., Pfeiffer, E., Sano, M., van Dyck, C.H. and Thal, L.J. (2005) Vitamin E and donepezil for the treatment of mild cognitive impairment. The New England Journal of Medicine, 352, 2379-2388. doi:10.1056/NEJMoa050151

[8]   Zeng, X., Staib, L.H., Schultz, R.T. and Duncan, J.S. (1999) Segmentation and measurement of the cortex from 3-D MR images using coupled surfaces propagation. IEEE Transactions on Medical Imaging, 18, 100-101.

[9]   Jones, S.E., Buchbinder, B.R. and Aharon, I. (2000) Three-dimensional mapping of the cortical thickness using Laplace’s equation. Human Brain Mapping, 11, 12-32. doi:10.1002/1097-0193(200009)11:1<12::AID-HBM20>3.0.CO;2-K

[10]   Fischl, B. and Dale, A.M. (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America, 97, 11050-11055. doi:10.1073/pnas.200033797

[11]   Lerch, J.P., Jason, P., Zijdenbos, A., Hampel, H., Teipel, S.J. and Evans, A.C. (2005) Focal decline of cortical thickness in Alzheimer’s disease identified by computational neuroanatomy. Cerebral Cortex, 15, 995-1001. doi:10.1093/cercor/bhh200

[12]   Arimura, H., Yoshiura, T., Kumazawa, S., Tanaka, K., Koga, H., Mihara, H., Honda, H., Sakai, S., Toyofuku, F. and Higashida, Y. (2008) Automated method for identification of patients with Alzheimer’s disease based on three-dimensional MR Images. Academic Radiology, 15, 274-284. doi:10.1016/j.acra.2007.10.020

[13]   Acosta, O., Bourgeat, P., Zuluaga, A.M., Fripp, J., Salvado, O., Ourselin, S. and Alzheimer’s Disease Neuroimaging Initiative (2009) Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian- Eulerian PDE approach using partial volume maps. Medical Image Analysis, 13, 730-743. doi:10.1016/

[14]   Bezdek, J.C. (1980) A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-2, 1-8. doi:10.1109/TPAMI.1980.4766964

[15]   Pham, D.L. (2001) Spatial models for fuzzy clustering. Computer Vision and Image Understanding, 84, 285-297. doi:10.1006/cviu.2001.0951

[16]   Pham, D.L. and Prince, J.L. (1999) Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging, 18, 737-752. doi:10.1109/42.802752

[17]   Miyamoto, S. (1999) Fuzzy c-means clustering. How to clustering analysis. Morikita Publishing Corporation, Tokyo, 27-33.

[18]   Hajnal, J.V., Hill, D.L.G. and Hawkes, D.J. (2001) Medical image registration. CRC Press, Boca Raton. doi:10.1201/9781420042474

[19]   Burger, W. and Burge, M. (2007) Digital image processing: An algorithmic introduction using Java. Springer-Verlag New York Inc, New York.

[20]   Forsey, D.R. and Bartels, R.H. (1988) Hierarchical B-spline refinement. Computer Graphics, 22, 205-212. doi:10.1145/378456.378512

[21]   Lee, S., Wolberg, G. and Shin, S.Y. (1997) Scattered Data interpolation with multilevel B-splines. IEEE Transactions on Visualization and Computer Graphics, 3, 228- 244. doi:10.1109/2945.620490

[22]   Rohlfing, T., Maurer Jr., C.R., Bluemke, D.A. and Jacobs, M.A. (2003) Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Transactions on Medical Imaging, 22, 730-741. doi:10.1109/TMI.2003.814791

[23]   Arimura, H., Tokunaga, C., Yasuo, Y. and Kuwazuru, J. (2012) Section 4 brain imaging, chapter 13 magnetic resonance image analysis for brain CAD systems with machine learning. In: Suzuki, K., Ed., Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, IGI Global, Pennsylvania, 256-294.

[24]   Laboratory of Neuro Imaging (2012) International consortium for brain mapping.