Segmentation and visualization of Multispectral medical images with interactive control of parameters for a set of unsupervised classifiers

Eileen M. McMahon, Armando Manduca, Richard A. Robb

Research output: Contribution to journalConference article

7 Scopus citations

Abstract

Multispectral classification uses registered 3-D image volumes from more than one imaging modality or from different sequences within a modality to classify tissues within those volumes. The complementary information contained within the different image volumes may allow for the separation of tissue class types in multidimensional feature space when the same tissue classes would be indistinct using just one image volume. When segmentation is complete, attributes of these classes may be determined (e.g., volumes), or the classes may be visualized as objects in 3-D. There are two main types of classification algorithms: supervised and unsupervised. Unsupervised classifiers offer the promise of totally automated classification of tissue types and calculation of tissue volumes and other tissue properties in medical images. This would have two benefits: (1) elimination of the time-consuming process of manual segmentation by medical experts, and (2) ensuring reproducible results. While accurate performance by unsupervised classifiers is, in general, still impossible, an intermediate step is the development of tools to allow users to obtain useful results in a relatively short period of time. This paper describes such a tool which allows users to quickly and easily experiment with various choices of unsupervised classification algorithms and their input parameters and evaluate the results.

Original languageEnglish (US)
Pages (from-to)174-184
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume2434
DOIs
StatePublished - May 12 1995
EventMedical Imaging 1995: Image Processing - San Diego, United States
Duration: Feb 26 1995Mar 2 1995

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Keywords

  • Classifier
  • Cluster
  • Features
  • Fuzzy
  • Multimodality
  • Multispectral
  • Segmentation
  • Tissue
  • Unsupervised
  • Visualization

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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