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IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
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Neural Computation
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Statistical physics, mixtures of distributions, and the EM algorithm
Neural Computation
Neural Networks: A Comprehensive Foundation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Segmentation of magnetic resonance brain image: integrating region growing and edge detection
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
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ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
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This paper presents an adaptive structure self-organizingfinite 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 formingwinner-takes-in probability splits of the data allowing the datato contribute simultaneously to multiple regions. Hence, the result is unbiased and satisfies the asymptotic optimality properties ofmaximum likelihood.To achieve a fully automatic quantification procedure that can adaptto 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 thenew method yields very efficient and accurate performance comparedto expectation-maximization, K-means, and competitive learning procedures.