Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms

Miin Shen Yang*, Karen Chia Ren Lin, Hsiu Chih Liu, Jiing Feng Lirng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

In this article, we propose batch-type learning vector quantization (LVQ) segmentation techniques for the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The proposed LVQ segmentation techniques are compared with the generalized Kohonen's competitive learning (GKCL) methods, which were proposed by Lin et al. [Magn Reson Imaging 21 (2003) 863-870]. Three MRI data sets of real cases are used in this article. The first case is from a 2-year-old girl who was diagnosed with retinoblastoma in her left eye. The second case is from a 55-year-old woman who developed complete left side oculomotor palsy immediately after a motor vehicle accident. The third case is from an 84-year-old man who was diagnosed with Alzheimer disease (AD). Our comparisons are based on sensitivity of algorithm parameters, the quality of MRI segmentation with the contrast-to-noise ratio and the accuracy of the region of interest tissue. Overall, the segmentation results from batch-type LVQ algorithms present good accuracy and quality of the segmentation images, and also flexibility of algorithm parameters in all the comparison consequences. The results support that the proposed batch-type LVQ algorithms are better than the previous GKCL algorithms. Specifically, the proposed fuzzy-soft LVQ algorithm works well in segmenting AD MRI data set to accurately measure the hippocampus volume in AD MR images.

Original languageEnglish
Pages (from-to)265-277
Number of pages13
JournalMagnetic Resonance Imaging
Volume25
Issue number2
DOIs
StatePublished - Feb 2007

Keywords

  • Fuzzy c-means (FCM)
  • Fuzzy clustering
  • Fuzzy-soft LVQ (FSLVQ)
  • Image segmentation
  • Learning vector quantization (LVQ)
  • Magnetic resonance imaging (MRI)

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