Medical image segmentation, i.e., labeling structures of interest in medical images, is crucial for disease diagnosis and treatment in radiology. In reversible data hiding in medical images (RDHMI), segmentation consists of only two regions: the focal and nonfocal regions. The focal region mainly contains information for diagnosis, while the nonfocal region serves as the monochrome background. The current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images, and manual segmentation is time-consuming, poorly reproducible, and operator-dependent. Implementing state-of-the-art deep learning (DL) models will facilitate key benefits, but the lack of domain-specific labels for existing medical datasets makes it impossible. To address this problem, this study provides labels of existing medical datasets based on a hybrid segmentation approach to facilitate the implementation of DL segmentation models in this domain. First, an initial segmentation based on a $3\times 3$ kernel is performed to analyze identified contour pixels before classifying pixels into focal and nonfocal regions. Then, several human expert raters evaluate and classify the generated labels into accurate and inaccurate labels. The inaccurate labels undergo manual segmentation by medical practitioners and are scored based on a hierarchical voting scheme before being assigned to the proposed dataset. To ensure reliability and integrity in the proposed dataset, we evaluate the accurate automated labels with manually segmented labels by medical practitioners using five assessment metrics: dice coefficient, Jaccard index, precision, recall, and accuracy. The experimental results show labels in the proposed dataset are consistent with the subjective judgment of human experts, with an average accuracy score of 94% and dice coefficient scores between 90%– 99%. The study further proposes a ResNet-UNet with concatenated spatial and channel squeeze and excitation (scSE) architecture for semantic segmentation to validate and illustrate the usefulness of the proposed dataset. The results demonstrate the superior performance of the proposed architecture in accurately separating the focal and nonfocal regions compared to state-of-the-art architectures. Dataset information is released under the following URL: https://www.kaggle.com/lordamoah/datasets (accessed on 31 March 2025).

Detection of color images that have undergone double compression is a critical aspect of digital image forensics. Despite the existence of various methods capable of detecting double Joint Photographic Experts Group (JPEG) compression, they are unable to address the issue of mixed double compression resulting from the use of different compression standards. In particular, the implementation of Joint Photographic Experts Group 2000 (JPEG2000) as the secondary compression standard can result in a decline or complete loss of performance in existing methods. To tackle this challenge of JPEG+JPEG2000 compression, a detection method based on quaternion convolutional neural networks (QCNN) is proposed. The QCNN processes the data as a quaternion, transforming the components of a traditional convolutional neural network (CNN) into a quaternion representation. The relationships between the color channels of the image are preserved, and the utilization of color information is optimized. Additionally, the method includes a feature conversion module that converts the extracted features into quaternion statistical features, thereby amplifying the evidence of double compression. Experimental results indicate that the proposed QCNN-based method improves, on average, by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.