TY - JOUR

T1 - Quantitative Analysis of MR Brain Image Sequences by Adaptive Self-Organizing Finite Mixtures

AU - Wang, Yue

AU - Adali, Tülay

AU - Lau, Chi Ming

AU - Kung, Sun Yuan

N1 - Funding Information:
⁄This work was partially supported by a grant from the Office of Naval Research (N00014-94-1-0743) and a grant from the University of Maryland Graduate School.

PY - 1998

Y1 - 1998

N2 - This paper presents an adaptive structure self-organizing finite mixture network for quantification of magnetic resonance (MR) brain image sequences. We present justification for the use of standard finite normal mixture model for MR images and formulate image quantification as a distribution learning problem. The finite mixture network parameters are updated such that the relative entropy between the true and estimated distributions is minimized. The new learning scheme achieves flexible classifier boundaries by forming winner-takes-in probability splits of the data allowing the data to contribute simultaneously to multiple regions. Hence, the result is unbiased and satisfies the asymptotic optimality properties of maximum likelihood. To achieve a fully automatic quantification procedure that can adapt to different slices in the MR image sequence, we utilize an information theoretic criterion that we have introduced recently, the minimum conditional bias/variance (MCBV) criterion. MCBV allows us to determine the suitable number of mixture components to represent the characteristics of each image in the sequence. We present examples to show that the new method yields very efficient and accurate performance compared to expectation-maximization, K-means, and competitive learning procedures.

AB - This paper presents an adaptive structure self-organizing finite mixture network for quantification of magnetic resonance (MR) brain image sequences. We present justification for the use of standard finite normal mixture model for MR images and formulate image quantification as a distribution learning problem. The finite mixture network parameters are updated such that the relative entropy between the true and estimated distributions is minimized. The new learning scheme achieves flexible classifier boundaries by forming winner-takes-in probability splits of the data allowing the data to contribute simultaneously to multiple regions. Hence, the result is unbiased and satisfies the asymptotic optimality properties of maximum likelihood. To achieve a fully automatic quantification procedure that can adapt to different slices in the MR image sequence, we utilize an information theoretic criterion that we have introduced recently, the minimum conditional bias/variance (MCBV) criterion. MCBV allows us to determine the suitable number of mixture components to represent the characteristics of each image in the sequence. We present examples to show that the new method yields very efficient and accurate performance compared to expectation-maximization, K-means, and competitive learning procedures.

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M3 - Article

AN - SCOPUS:0032046549

SN - 1387-5485

VL - 18

SP - 219

EP - 239

JO - Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology

JF - Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology

IS - 3

ER -