Computerized White Matter and Gray Matter Extraction from MRI of Brain Image

Sudipta Roy¹, Debayan Ganguly², Kingshuk Chatterjee², Samir Kumar Bandyopadhyay³

¹Department of Computer Science and Engineering, Academy of Technology, Adisaptagram, India.
²E.C.S.U, Indian Statistical Institute, Kolkata, India.
³Department of Computer Science and Engineering, University of Calcutta, Kolkata, India.

DOI: 10.4236/jbise.2015.89054                

Abstract

Automated segmentation of white matter (WM) and gray matter (GM) is a very important task for detecting multiple diseases. The paper proposed a simple method for WM and GM extraction form magnetic resonance imaging (MRI) of brain. The proposed methods based on binarization, wavelet decomposition, and convexhull produce very effective results in the context of visual inspection and as well as quantifiably. It tested on three different (Transvers, Sagittal, Coronal) types of MRI of brain image and the validation of experiment indicate accurate detection and segmentation of the interesting structures or particular region of MRI of brain image.

Keywords

Automated Segmentation, Convexhull, Relative Area, White Matter, Gray Matter, Standard Deviation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Grau, V., Kikinis, R., Alcaniz, M. and Wareld, S.K. (2003) Cortical Gray Matter Segmentation Using an Improved Watershed Transforms. Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 17-21 Sept. 2003, Vol. 1, 618-621. http://dx.doi.org/10.1109/iembs.2003.1279828
[2]	Valsasina, P., Benedetti, B., Rovaris, M., Sormani, M.P., Comi, G. and Filippi, M. (2001) Evidence for Progressive Gray Matter Loss in Patients with Relapsing Remitting MS. Neurology, 1126-1128.
[3]	Kochunov, P., Thompson, P.M., Lancaster, J.L., Bartzokis, G., Smith, S., Coyle, T., Royall, R., Laird, A. and Foxa, P.T. (2006) Relationship between White Matter Fractional Anisotropy and Other Indices of Cerebral Health in Normal Aging: Tract-Based Spatial Statistics Study of Aging. Elsevier, Neuroimage.
[4]	van Eimeren, L., Niogi, S.N., McCandliss, B.D., Holloway, I.D. and Ansari, D. (2008) White Matter Microstructures Underlying Mathematical Abilities in Children. Cognitive Neuroscience and Neuropsychology, 19, 1117-1121. http://dx.doi.org/10.1097/wnr.0b013e328307f5c1
[5]	Tijms, B.M., Serie, P., Willshaw, D.J. and Lawrie, S.M. (2012) Similarity-Based Extraction of Individual Networks from Gray Matter MRI Scans. Cerebral Cortex, 22, 1530-1541.
[6]	Roy, S., Chatterjee, K., Maitra, I.K. and Bandyopadhyay, S.K. (2013) Artefact Removal from MRI of Brain Image. International Refereed Journal of Engineering and Science (IRJES), 2, 24-30.
[7]	Ahmed, M.M. and Mohammad, D.B. (2013) Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model. International Journal of Image Processing, 2, 27-34.
[8]	Roy, S., Dey, A., Chatterjee, K. and Bandyopadhyay, S.K. (2012) An Efficient Binarization Method for MRI of Brain Image. Signal & Image Processing: An International Journal (SIPIJ), 3, 35-51. http://dx.doi.org/10.5121/sipij.2012.3604
[9]	http://www.bic.mni.mcgill.ca/brainweb (April 2013)
[10]	Roy, S., Ghosh, P. and Bandyopadhyay, S.K. (2014) Segmentation and Contour Extraction of Cerebral Hemorrhage from MRI of Brain by Gamma Transformation Approach. FICTA, Proc. Springer, AISC, Advances in Intelligent Systems and Computing, 328, 383-394. http://dx.doi.org/10.1007/978-3-319-12012-6_42