Improved Reversible Data Hiding in Medical images using Interpolation and Threshold based Embedding Strategy

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.

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Blockchain-Based Reversible Data Hiding for Securing Medical Images

Journal of Healthcare Engineering ◽

10.1155/2021/9943402 ◽

2021 ◽

Vol 2021 ◽

pp. 1-22

Author(s):

Ji-Hwei Horng ◽

Ching-Chun Chang ◽

Guan-Long Li ◽

Wai-Kong Lee ◽

Seong Oun Hwang

Keyword(s):

Data Hiding ◽

Private Information ◽

Medical Diagnosis ◽

Reversible Data Hiding ◽

Secure Communication ◽

Medical Images ◽

Personal Information ◽

Embedding Capacity ◽

Image Block ◽

Encrypted Images

Medical images carry a lot of important information for making a medical diagnosis. Since the medical images need to be communicated frequently to allow timely and accurate diagnosis, it has become a target for malicious attacks. Hence, medical images are protected through encryption algorithms. Recently, reversible data hiding on the encrypted images (RDHEI) schemes are employed to embed private information into the medical images. This allows effective and secure communication, wherein the privately embedded information (e.g., medical records and personal information) is very useful to the medical diagnosis. However, existing RDHEI schemes still suffer from low embedding capacity, which limits their applicability. Besides, such solution still lacks a good mechanism to ensure its integrity and traceability. To resolve these issues, a novel approach based on image block-wise encryption and histogram shifting is proposed to provide more embedding capacity in the encrypted images. The embedding rate is over 0.8 bpp for typical medical images. On top of that, a blockchain-based system for RDHEI is proposed to resolve the traceability. The private information is stored on the blockchain together with the hash value of the original medical image. This allows traceability of all the medical images communicated over the proposed blockchain network.

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Integrated HNAS Network Model Based Lossless Compression with Data Hiding using Parity Check in Medical Images

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1756.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 2656-2662

Keyword(s):

Network Model ◽

Data Hiding ◽

Compression Ratio ◽

Reversible Data Hiding ◽

Medical Image ◽

Medical Images ◽

Care Delivery ◽

Lossless Compression ◽

Parity Check ◽

Compression Technique

A massive volume of medical data is generating through advanced medical image modalities. With advancements in telecommunications, Telemedicine, and Teleradiologyy have become the most common and viable methods for effective health care delivery around the globe. For sufficient storage, medical images should be compressed using lossless compression techniques. In this paper, we aim at developing a lossless compression technique to achieve a better compression ratio with reversible data hiding. The proposed work segments foreground and background area in medical images using semantic segmentation with the Hierarchical Neural Architecture Search (HNAS) Network model. After segmenting the medical image, confidential patient data is hidden in the foreground area using the parity check method. Following data hiding, lossless compression of foreground and background is done using Huffman and Lempel-Ziv-Welch methods. The performance of our proposed method has been compared with those obtained from standard lossless compression algorithms and existing reversible data hiding methods. This proposed method achieves better compression ratio and a hundred percent reversible when data extraction.

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