Reversible Data Hiding-Based Local Contrast Enhancement With Nonuniform Superpixel Blocks for Medical Images

Abstract

Reversible data hiding-based contrast enhancement can be applied to medical images, which not only allows the storage of patient information through reversible embedding, but also achieves image contrast enhancement, thereby assisting doctors in accurately diagnosing patient diseases. In response to the existing problems of mainstream methods, a novel reversible data hiding-based local contrast enhancement method for medical images is proposed. This method utilizes superpixel segmentation to segment medical images into multiple pixel blocks, and performs reversible data embedding and contrast enhancement for the pixel blocks within the region of interest (ROI). Additionally, a new embedding strategy is proposed. According to the contrast and texture features of each pixel block, histogram expansion of different degrees is carried out to effectively enhance the pixel blocks with low contrast, while avoiding excessive enhancement of the pixel blocks with high contrast. Experimental results demonstrate that, compared with the state-of-the-art mainstream methods, the proposed method not only improves the contrast in the ROI but also ensures high visual quality of the medical images.