Reversible Data Hiding-Based Contrast Enhancement With Multi-Group Stretching for ROI of Medical Image
Guangyong Gao, Hui Zhang, Zhihua Xia, Xiangyang Luo, Yun-Qing Shi
Abstract
Reversible data hiding-based contrast enhancement (RDHCE) can be used in contrast enhancement for medical images, and it has been a popular research topic in recent years. However, the existing RDHCE methods suffer from the problem of inaccurate segmentation of the region of interest (ROI) in medical images, which can impact the contrast enhancement effect of the images. Moreover, some methods face limitations in their universality for ROI histograms with few empty bins on both sides, which results in unsatisfactory embedding capacity and contrast enhancement effect. To solve these problems, this study proposes an improved RDHCE method for medical images. The proposed method uses the UNet3+ network model, which makes the segmented ROI histograms more consistent with the subjective judgment of doctors compared to those obtained by traditional segmentation approaches. In addition, a multi-group stretching method is proposed to address the limitation of histogram expansion caused by the empty bins on both histogram sides, enabling adaptation to different ROI histograms with varying gray distributions. Compared to state-of-the-art RDHCE methods, the proposed method offers better generalizability, superior contrast enhancement performance and a larger ROI embedding capacity. It can greatly improve the visual quality of medical images in the field of medical imaging and aid doctors in making more accurate diagnoses.