Dynamic programming algorithm for contrast correction in medical images

摘要

Abstract: Dynamic programming (DP) algorithms are frequently used in speech processing for dynamic time warping of speech signals. In this approach, DP is applied to image processing for contrast correction. Defining one image as reference, the object image is contrast-corrected by DP based on the (cumulative) grayscale value histograms of the images. DP ensures a nonlinear histogram warping, and therefore an optimal nonlinear mapping between grayscale values of the images. If no path restrictions are used, assignments can be ambiguous; i.e., the grayscale value of the object image can be assigned to more than one grayscale value of the reference image. In this case, histogram-based analysis selects the most probable grayscale value. The algorithm works on the condition that image contents of the images to be compared are equal and differences only based on contrast. Unfortunately, medical images of follow-up studies or slices of 3D data volumes differ not only in contrast, but also in image content. The condition can then be met only approximately. For example, neighboring slices of gradient-spoiled T$-1$/-weighted 3D- FLASH image data are contrast-corrected with this method. In experiments with simulated 3D MRI images, the DP approach performs best compared to another nonlinear method or to linear mapping. !9