Adaptive windowing for optimal visualization of medical images based on normalized information distance

There has been a growing recent interest of applying Kol-mogorov complexity and its related normalized information distance (NID) measures in real-world problems, but their application in the field of medical image processing remains limited. In this work we attempt to incorporate NID in the design of windowing operators for optimal visualization of high dynamic range (HDR) medical images, where predefined intensity interval of interest needs to be mapped to match the low dynamic range (LDR) of standard displays. By approximating NID using a Shannon entropy based method, we are able to optimize parametric windowing operators to maximize the information similarity between the HDR image and the LDR image after mapping. Experimental results demonstrate promising performance of the proposed approach.

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