Abstract
In this paper, a new reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical image is proposed. The original medical image is firstly converted into frequency domain with integer-to-integer wavelet transform (IWT) algorithm, and then the secret data are embedded into the medium frequency subbands of medical image robustly with CDM and machine learning algorithms. According to the orthogonality of different spreading sequences employed in CDM algorithm, the secret data are embedded repeatedly, most of the elements of spreading sequences are mutually canceled, and the proposed method obtained high data embedding capacity at low image distortion. Simultaneously, the to-be-embedded secret data are represented by different spreading sequences, and only the receiver who has the spreading sequences the same as the sender can extract the secret data and original image completely, by which the security of the RDH is improved effectively. Experimental results show the feasibility of the proposed scheme for data embedding in medical image comparing with other state-of-the-art methods.
Highlights
Most hospitals have developed medical information management system to provide better, safer, and efficient service for patients
The experimental results show that the scheme proposed in this paper could achieve high data embedding capacity and security at low image distortion, which is sufficient for the protection of medical image and patient’s privacy
This paper presents a novel reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical images
Summary
Most hospitals have developed medical information management system to provide better, safer, and efficient service for patients. Li et al [11] proposed a two-dimensional difference histogram modification based RDH scheme, by which the redundancy of the cover image is better exploited and high data embedding performance is achieved. Li et al [16] suggested embedding secret information into scalable pixels according to local complexity of the cover image and adopting an adaptive prediction-error expansion method to achieve large data embedding capacity with low image distortion simultaneously. According to the orthogonality of the spreading sequence employed in the scheme, the proposed method achieves high reversible data hiding performance especially at large data embedding capacity. Agrawal et al [26] introduced the IWT and HS algorithms based RDH scheme for data embedding in medical images and achieved better data hiding performance comparing with other methods.
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