Implementation of high-dimensional feature map for segmentation of MR images

Renjie He, Balasrinivasa Rao Sajja, Ponnada A. Narayana

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A method that considerably reduces the computational and memory complexities associated with the generation of high-dimensional (≥3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating 3D and 4D feature maps for segmenting MR brain images of multiple sclerosis patients.

Original languageEnglish (US)
Pages (from-to)1439-1448
Number of pages10
JournalAnnals of biomedical engineering
Volume33
Issue number10
DOIs
StatePublished - Oct 1 2005

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Image segmentation
Brain
Data storage equipment

Keywords

  • Feature map
  • Feature space
  • KNN classification
  • MRI
  • Multispectral segmentation

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Implementation of high-dimensional feature map for segmentation of MR images. / He, Renjie; Sajja, Balasrinivasa Rao; Narayana, Ponnada A.

In: Annals of biomedical engineering, Vol. 33, No. 10, 01.10.2005, p. 1439-1448.

Research output: Contribution to journalArticle

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