Interleaved k-NN Classification and Bias Field Estimation for MR Image with Intensity Inhomogeneity

Jingjing GAO  Mei XIE  Ling MAO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.4   pp.1011-1015
Publication Date: 2014/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E97.D.1011
Type of Manuscript: LETTER
Category: Biological Engineering
Keyword: 
intensity inhomogeneity,  bias field estimation,  k-NN classification,  minimizing energy,  

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Summary: 
k-NN classification has been applied to classify normal tissues in MR images. However, the intensity inhomogeneity of MR images forces conventional k-NN classification into significant misclassification errors. This letter proposes a new interleaved method, which combines k-NN classification and bias field estimation in an energy minimization framework, to simultaneously overcome the limitation of misclassifications in conventional k-NN classification and correct the bias field of observed images. Experiments demonstrate the effectiveness and advantages of the proposed algorithm.