A two-dimensional autoregressive - moving average (ARMA) model has been recently developed by Penm (1999) which leads to optimum recursive enhancement procedures for realistic image data. This paper considers the application of these electronic healthcare informatics procedures to data whose spatial covariance function appears to vary exponentially with Euclidean distance. Specifically, the identification problem is considered, an optimal recursive algorithm based on a two-dimensional ARMA model is developed for a specific example, and this algorithm is compared with the ad-hoc method of successive orthogonalisation approximations.
|Title of host publication||Collaborative research in electronic healthcare|
|Subtitle of host publication||Bioinformatics, pharmacy informatics and computing|
|Editors||T.J. O'Neill, J. Penm, R.D. Terrell|
|Place of Publication||Rivett|
|Number of pages||21|
|Publication status||Published - 2008|
O'Neill, T., Penm, J. H. W., & Penm, J. (2008). Optimum healthcare image smoothing and restoration based on the two-dimensional ARMA model. In T. J. O'Neill, J. Penm, & R. D. Terrell (Eds.), Collaborative research in electronic healthcare: Bioinformatics, pharmacy informatics and computing (pp. 86-106). Rivett: Evergreen Publishing.