Abstract
In a telemedicine diagnosis system, the emergence of 3D imaging enables doctors to make clearer judgments, and its accuracy also directly affects doctors’ diagnosis of the disease. In order to ensure the safe transmission and storage of medical data, a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper. The proposed algorithm employs the principal component analysis (PCA) transform to reduce the data dimension, which can minimize the error between the extracted components and the original data in the mean square sense. Especially, this algorithm helps to create a bacterial foraging model based on particle swarm optimization (BF-PSO), by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding, thereby achieving the optimal balance between embedding capacity and imperceptibility. A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.
Highlights
In the current Internet of Medical Things, the emergence of new diagnosis and treatment modes such as telemedicine ensures that doctors can use CT, MRI, ultrasound or other medical equipment to diagnose diseases [1,2]
To address the above challenge, a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper, where the watermark is embedded by selecting the feature points whose normal vector changes greatly after dimension reduction
This study proposes a bacterial foraging algorithm based on particle swarm optimization, which combines the optimal solution with watermark pixels to achieve an optimal balance between embedding capacity and imperceptibility
Summary
In the current Internet of Medical Things, the emergence of new diagnosis and treatment modes such as telemedicine ensures that doctors can use CT, MRI, ultrasound or other medical equipment to diagnose diseases [1,2]. Watermark information such as patient's name, gender, age, and symptoms, etc., is embedded into medical data to prevent from being tampered in the process of transmission and storage. This operation can protect the legitimate rights and interests of doctors and patients, and provide an effective way to solve the increasingly critical doctor-patient relationship [11]. To address the above challenge, a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper, where the watermark is embedded by selecting the feature points whose normal vector changes greatly after dimension reduction. The extraction process does not need the original 3D model and can realize a reversible embedding on the premise of protecting the integrity and security of medical data, which avoids the limitation of existing 3D watermarking technologies in low robustness and poor visual invisibility
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