Abstract
The primary aim of this work was to develop a fast and accurate method for tissue segmentation based on a 4D feature map to be used in stereotactic neurosurgery and the evaluation of multiple sclerosis, MS. Secondly, we wanted to validate our method with biological tissue studied in-vivo obtained by biopsy. Tissue segmentation based on both 3D and 4D feature maps were derived from high resolution MR images and was performed in five normal individuals, six patients with MS plaques in the brain, and six patients with malignant brain tumors from which four had undergone stereotactic biopsy. Three inputs: proton density, T2, and T1-weighted MR images were routinely utilized. As a fourth input, magnetization transfer was used in some patients, and T1-weighted post contrast MRI in others.
To speed up computation, our k-Nearest Neighbor segmentation algorithm was optimized by: 1) discarding redundant seed points, 2) discarding points within one half of a standard deviation from the cluster center that were non-overlapping with other tissue classes, and 3) discarding outlying seed points located beyond five standard deviations from the cluster center of each tissue class.
After segmentation, a stack of color-coded segmented images was created. Our new technique, utilizing all four MRI inputs provided better segmentation than that based on only three inputs. The tissues were smoother due to the reduction of statistical noise, and the delineation of the tissues was increased. Details that were previously blurred or invisible now became apparent. For example, in normal persons detailed depiction of deep gray matter nuclei was obtained. In malignant tumors, up to five abnormal tissues were identified: 1) solid tumor core, 2) cystic tumor, 3) white matter edema, 4) gray matter edema, and 5) tissue necrosis. Subsequent stereotactic biopsy and histological analysis confirmed the results of the tissue segmentations. In MS patients, delineation of MS plaque became much sharper.
In conclusion, the proposed 4D methodology warrants further development and clinical evaluation.
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Cline HE, Lorensen WE, Kikinis R, Jolesz F. 3D segmentation of MR images of the head using probability and connectivity. J CAT 14:1037–1042, 1990.
Vinitski S, Gonzalez C, Burnett C, Seshagiri S, Mohamed FB, Lublin FD, Knobler RL, Frazer G. Tissue segmentation by high resolution MRI: improved accuracy and stability. Proc. IEEE Eng. Med. Biol. 16:577–578, 1994.
Vinitski S, Seshagiri S, Mohamed FB, et al. Tissue characterization by MR: data segmentation using 3D feature map. In: Vernazza G, Venetsanopoulos AN, Bracccini C (eds), Image processing theory and applications. Amsterdam: Elsevier Science Publishers B.V., 325–328, 1993.
Vinitski S, Gonzalez C, Mohamed FB, et al. “Improved intracranial lesion characterization by tissue segmentation based on a 3D feature map.” Magn Reson Med 37:457–469, 1996.
Vinitski S, Gonzalez C, et al. “Tissue Segmentation in MRI as an Informative Indicator of Disease Activity in the Brain” In: DeFloriani L., Vernazza G, (eds), Image Analysis Processing. Berlin: Springer-Verlag, 265–270, 1995.
Vinitski S, Gonzalez C, et al. Validation of 3D Tissue Segmentation by Animal Model of Brain Tumor. Proc. IEEE Eng. Med. Biol. 18:606.1-606.3, 1996.
R. Brasch, MRI Contrast Enhancement in the Central Nervous System: A Case Study Approach, Raven Press, N.Y. 1993.
S Vinitski, R.H. Griffey et al., Lactate Observation by Spectral Editing, “Magn. Reson. Imag. 6:707–710, 1988.
B.S. Hu, S.M. Connely et al., “Pulsed saturation transfer contrast,” Magn. Reson. Med 26:231–228, 1992.
T.M. Cover and P.E. Hart, classification “IEEE Transaction on Information Theory”, Vol. 13, 1967. pp 21–27, Nearest Neighborhood Pattern.
Cline HE, Dumoulin CL, et al. 3D reconstruction of the brain from MRI using a connectivity algorithm. Magn Reson Imaging 5:345–349, 1987.
Cline HE, Lorensen WE, Ludke S, Crawford CR. Two algorithms for the three-dimensional reconstruction of tomograms. Med Phys 15:320–327, 1988.
G. Gerig, et al., Nonlinear Anisotropic Filtering of MRI Data, IEEE Transaction on Medical Imaging Vol. II. No.2, 1992.
Proceedings of the NIH Workshop “Evaluation of multiple sclerosis lesion load: Comparison of segmentation image processing techniques,” held at Montreal Neurological Institute, November 6–7, 1995.
Mohamed FB, Vinitski S, Gonzalez C, Faro S, Burnett C, Ortega HV, Iwanaga T. “Image nonuniformity correction in high field (1.5T) MRI.” Proc IEEE Eng Med Bio 17:36–37, 1995.
Gonzalez C, Mitchell DR, Sacchetti T, Seward JD, Knobler RL, Lubin FD: Correlation between structural brain lesions and emotional and cognitive function in patients with multiple sclerosis: an MRI study. Neuroradiology; (suppl) 123–124, 1991.
Vinsitski S, Iwanaga T, Gonzalez C, Andrews D, Knobler R, Mack J, Madi S. “Tissue Segmentation Based on a 4D Feature Map”. Proceed of the Fifth Meeting of the International Society for Magnetic Resonance in Medicine, Vancouver, BC, April 15–20, 1997; 476.
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Vinitski, S., Iwanaga, T., Gonzalez, C., Andrews, D., Knobler, R., Mack, J. (1997). Fast tissue segmentation based on a 4D feature map: Preliminary results. In: Del Bimbo, A. (eds) Image Analysis and Processing. ICIAP 1997. Lecture Notes in Computer Science, vol 1311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63508-4_154
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DOI: https://doi.org/10.1007/3-540-63508-4_154
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