A spectral color correction framework for medical applications.
This paper presents a new spectral approach to color correction for medical image analysis applications. Linear estimation with regularization by a constrained principal eigenvector method is used for calibration of the camera system and estimation of the illumination spectrum while spectral surface reflectivities are determined by Wiener inverse estimation. Nonlinear devices are handled by piecewise linear interpolation and any linear color preprocessing inside the camera is explicitly modeled. All measurement and estimation processes are combined into a spectral calibration framework for practical application in computer-assisted image analysis. The novelty of our approach lies in the generalization of the image formation model allowing for linear preprocessing inside the camera system. Such transforms would lead to erroneous results with positivity constraint based algorithms or a monochromator based measurement. We provide experimental results from a comprehensive set of reference measurements acquired with a video endoscopy system for gastroscopic application.