Three Dimensional Tissue Classifications in MR Brain Images

  • Runa Parveen
  • Cliff Ruff
  • Andrew Todd-Pokropek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4241)


This paper presents an algorithm for classifying different tissue types in T1-weighted MR brain images using fuzzy segmentation. The main aim in this study is to compensate for the blurring effect on tissue boundaries due to partial volume effects. This paper is organized as follows: first, an adaptive greedy contour model has been developed to separate the intracranial volume (ICV) from the scalp and skull. Second, in order to deal with the problem of the partial volume effect, an algorithm for fuzzy segmentation is presented which has integrated fuzzy spatial affinity with statistical distributions of image intensities for each of the three tissues – cerebrospinal fluid, white matter and grey matter. This algorithm is tested on well-established simulated MR brain volumes to generate an extensive quantitative comparison with different noise levels and different slice thicknesses ranging from 1mm to 5mm. Finally, the results of this algorithm on clinical MR brain images are demonstrated.


Grey Matter Percentage Error Partial Volume Effect Active Contour Model Fuzzy Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Runa Parveen
    • 1
  • Cliff Ruff
    • 2
  • Andrew Todd-Pokropek
    • 1
  1. 1.Department of Medical Physics and BioengineeringUniversity College LondonLondonUnited Kingdom
  2. 2.Department of Medical PhysicsUniversity College London HospitalsLondonUnited Kingdom

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