Quantification of MR brain image sequence by adaptive structure probabilistic self-organizing mixture
Yue Julia Wang, Chi-Ming Lau, Tülay Adalı, Matthew T. Freedman, Seong K. Mun · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
This paper presents a neural network based technique for the quantification of MR brain image sequences. We studied image statistics to justify the correct use of the standard finite normal mixture model and formulated image quantification as a distribution learning problem. From information theory, we used relative entropy as the information distance measure and developed an adaptive structure probabilistic self- organizing mixture to estimate the parameter values. New learning scheme has the capability of achieving flexible classifier shapes in terms of winner-takes-in probability splits of data, allowing data to contribute simultaneously to multiple regions. The result is unbiased and holds the asymptotic properties of maximum likelihood estimation. To achieve a fully automatic function and incorporate the correlation between slices, we utilized a newly developed information theoretic criterion (minimum conditional bias/variance) to determine the suitable number of mixture components such that the network can adjust its structure to the characteristics of each image in the sequence. Compared with the results of the algorithms based on expectation- maximization, K-means, and Kohonen's self-organizing map, the new method yields a very efficient and accurate performance.