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Strategies for multivariate image regression

1992; Elsevier BV; Volume: 14; Issue: 1-3 Linguagem: Inglês

10.1016/0169-7439(92)80118-n

1873-3239

Kim H. Esbensen, Paul Geladi, Hans Grahn,

Spectroscopy Techniques in Biomedical and Chemical Research

Esbensen, K.H., Geladi, P.L. and Grahn, H.F., 1992. Strategies for multivariate image regression. Chemometrics and Inteligent Laboratory Systems, 14: 357–374. We present multivariate image regression (MIR) as a set of typically problem-dependent strategies for image decomposition guided by the nature of the Y variable and/or training data set delineation in the (X, Y) image domains. Regression techniques common in chemometrics may be applied also to the image regimen (in this paper we treat mainly two-dimensional images). We present applications of both IMPCR and IMPLS-DISCRIM in an effort to delineate the various possibilities for image regression. IMPCR builds directly on our earlier bilinear multivariate image analysis projection approach, while IMPLS-DISCRIM is trained on scene space binary classification masking with subsequent off-screen partial least squares analysis; the results are back-projected as images in the original scene space. Regression may either be carried out for modelling purposes and/or for subsequent prediction purposes. In the image domain this duality is accompanied by several optional training data set delineations in the scene space and/or in the spectral domain. We try to cover as complete a survey as possible of typical, representative regression problem types. We illustrate some of these MIR strategies with an MR-imaging example as well as a simple didactic MIR calibration from analytical chemistry.