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Segmentation Techniques in the Quantification of Multiple Sclerosis Lesions in MRI

  • Rakesh Sharma
  • Jasjit S. Suri
  • Ponnada A. Narayana
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Volumetry of the brain can provide fundamental information about the development and function of the normal human brain and can yield important clues for pathology in patients suffering from neurological brain disorders (see Jernigan et al. [713]). Valuable information has been gained about the pathological processes in epilepsy (see Stone et al. [714]) and Alzheimer’s disease (see Tanabe et al. [715]) from the volume measurements of various brain structures. Brain tissue in Alzheimer’s disease was compared with elderly control volunteers by using an MR-based computerized segmentation program. Semi- automated segmentation of MR brain images revealed significant brain atrophy with significant white matter hyperintensities. In many focal diseases such as Multiple Sclerosis (MS) and cancer, the total lesion volume is indicative of the overall disease burden and may be useful in the quantification and objective evaluation of therapeutic intervention in disease (see Dastidar et al. [716] and Fillippi et al. [717]). These investigators demonstrated that MRI images provide excellent quantitative MRI tissue volume measurement. Different tissues can be identified on the images, either manually or by computer-assisted means for computing the volumes. The process of identifying and isolating a given tissue is generally referred to as segmentation. Segmentation allows color-coding of different tissues for improved delineation and makes for easier visual identification of pathology. Segmentation is evaluated as being useful in radiation therapy (see Vaidyanathan et al. [718]) and for simulating sensitive procedures for interventional neurosurgery (see Dickson et al. [719]).

Keywords

Feature Space Multiple Sclerosis Lesion Segmentation Technique Fast Spin Echo Magnetization Transfer Ratio 
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 London 2002

Authors and Affiliations

  • Rakesh Sharma
  • Jasjit S. Suri
  • Ponnada A. Narayana

There are no affiliations available

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