Advertisement

Tissue segmentation in MRI as an informative indicator of disease activity in the brain

  • Simon Vinitski
  • Carlos Gonzalez
  • Claudio Burnett
  • Feroze Mohamed
  • Tad Iwanaga
  • Hector Ortega
  • Scott Faro
Biomedical Applications I
Part of the Lecture Notes in Computer Science book series (LNCS, volume 974)

Abstract

The presented tissue segmentation technique is based on a multispectral analysis approach. The input data were derived from high resolution MR images. Usually, only two inputs, proton density (PD) and T2-weighted images, are utilized to calculate the 2D feature map. In our method, we introduced a third input, T1-weighted MR image, for segmentation based on 3D feature map. k-Nearest Neighborhood segmentation algorithm was utilized. Tissue segmentation was performed in phantoms, normal humans and those with brain tumors and MS. Our technique utilizing all three inputs provided the best segmentation (p<0.001). The inclusion of T1 based images into segmentation produced dramatic improvement in tissue identification. Using our method, we identified the two distinctly different classes of tissue within the same MS plaque. We presume that these tissues represent the different stages involved in the evolution of the MS lesions. Further, our methodology for measuring MS lesion burden was also used to obtain its regional distribution as well as to follow its changes over time. The segmentation results were in full accord with neuropsychological findings.

Key words

MRI Tissue segmentation Multiple Sclerosis brain tumor 3D Feature Map 

References

  1. 1.
    Cline HE, Lorensen WE, Kikinis R, Jolesz F. Three-dimensional segmentation of MR images of the head using probability and connectivity. J Comp Assist Tomogr 1990; 14:1037–1042.Google Scholar
  2. 2.
    Vinitski S, Seshagiri S, Mohamed FB, et al. Tissue characterization by MR: data segmentation using 3D feature map. In: Vernazza G, Venetsanopoulos AN, Braccini C (eds), Image processing theory and applications. Amsterdam:Elsevier Science Publishers B.V., 1993; 325–328.Google Scholar
  3. 3.
    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. 1994; 16:577–578.Google Scholar
  4. 4.
    Vinitski S, Gonzalez C, et al.: Improved Intracranial Lesion Characterization by Tissue Segmentation Based on 3D Feature Map. Radiology 1994; 193(P):253.Google Scholar
  5. 5.
    Perona P, Malik J. Scalespace and edge detection using anisotropic diffusion. Proc IEEE Workshop on Computer Vision, Miami, FL 1987; 6–22.Google Scholar
  6. 6.
    Clarke LP, Velthuizen RP, et al.: MRI stability of three supervised segmentation techniques. Magn Reson Imaging 1993; 11:95–106.Google Scholar
  7. 7.
    Cline HE, Dumoulin Cl, Hart Jr HR, et al. 3D reconstruction of the brain from MRI using a connectivity algorithm. Magn Reson Imaging 1987; 345–349.Google Scholar
  8. 8.
    Cline HE, Lorensen WE, Ludke S, Crawford CR, Teeter BC. Two algorithms for the three-dimensional reconstruction of tomograms. Med Phys 1988; 15:320–327.Google Scholar
  9. 9.
    Dawson-Saunders B, Trapp RG. Basic and Clinical Biostatistics. Norwalk:Appleton & Langes, 1990; 79–99.Google Scholar
  10. 10.
    Rao SM. Neuropsychology of multiple sclerosis: a critical review. J Clin Exp Neuropsych 1986; 8:503–542.Google Scholar
  11. 11.
    Haughton VM, Yetkin FZ, Rao SM, et al.: Quantitative MR in the diagnosis of multiple sclerosis. Magn Res Med 1992; 26:71–74.Google Scholar
  12. 12.
    Gonzalez CF, Mitchell DR, Sacchetti T, Seward JD, Knobler RL, Lublin FD. Correlation between structural brain lesions and emotional and cognitive function in patients with multiple sclerosis: an MRI study. Neuroradiology 1991 (suppl) 123–124.Google Scholar
  13. 13.
    Mitchell DR, Swirsky-Sacchetti T, Knobler RL, Gonzalez CF, Seward J, Field HL, Santiago RS, Lublin FD. Analysis and correlation of mood state with cerebral MRI and severity of illness in patients with multiple sclerosis. Neurology 1991; 41 (suppl 1):145.Google Scholar
  14. 14.
    Sacchetti T, Mitchell DR, Seward JD, Gonzalez CF, Lublin FD, Fnobler RL, Field H. Neuropsychological and structural brain lesions in multiple sclerosis: a regional analysis. Neurology 1992; 42:1291–1295.Google Scholar
  15. 15.
    Paty DW, Li DKB, The UBC MS/MRI Study Group, The IFNB MSII Study Group. MRI analysis results of a multicenter, randomized, double-blind, placebocontrolled trial. Neurology 1993; 43:655–661.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Simon Vinitski
    • 1
  • Carlos Gonzalez
    • 1
  • Claudio Burnett
    • 1
  • Feroze Mohamed
    • 1
  • Tad Iwanaga
    • 1
  • Hector Ortega
    • 1
  • Scott Faro
    • 1
    • 2
  1. 1.Department of RadiologyThomas Jefferson University HospitalPhiladelphia
  2. 2.Department of RadiologyMedical College of PennsylvaniaPhiladelphia

Personalised recommendations