Robust watermarking algorithm for medical images based on log-polar transform

Medical images stored in a hospital system are generally confidential and integrated and require strict security. However, medical information stored on digital medical imaging systems, as well as picture archiving and communication systems (PACS), are vulnerable to attack when the data are transferred over wireless or wired communication networks. To solve this problem, a watermarking algorithm for medical images is proposed using a bit threshold map based on just noticeable distortion (JND) in the discrete cosine transform (DCT) method. The low-frequency component comprises a considerable amount of the signal energy for most images. As a result, it has a crucial effect on the image quality. Therefore, in this paper, the proposed algorithm embeds watermarks based on the low-frequency components of the image, such as the DC coefficient of the DCT. When watermarks are embedded in a low frequency area, the subjective image quality is often degraded. To compensate for the degradation of the imperceptibility of the watermarking system, which results from embedding watermarks in the low-frequency component, this research considers the human visual system. In addition, the embedding strength of the JND value is used to improve the watermarking imperceptibility. We applied the proposed watermarking algorithm to a variety of medical images using a computer simulation. The algorithm's performance was verified using a variety of attacks for eliminating watermarks. The simulation results show that the proposed algorithm robustly provides protection against a variety of possible attacks.

In this paper, we propose a novel medical color image digital watermarking algorithm, which can realize watermark capacity, transparency and robustness effective compromise. We encrypt the watermark image by Arnold Transform at first and then take it into a random sequence. The principal component of medical image is divided into some sub-blocks with 4×4 and each block is transformed by DCT. One bit of watermark is embedded by comparing the size of specific DCT coefficients in the adjacent sub-blocks. Watermark can be extracted without the original medical image. The experimental results show that the digital watermarking algorithm has a high transparency and big capacity as well as certain robustness.

In this paper, we propose a novel medical color image digital watermarking algorithm, which can realize watermark capacity, transparency and robustness effective compromise. We encrypt the watermark image by Arnold Transform at first and then take it into a random sequence. The principal component of medical image is divided into some sub-blocks with 4×4 and each block is transformed by DCT. One bit of watermark is embedded by comparing the size of specific DCT coefficients in the adjacent sub-blocks. Watermark can be extracted without the original medical image. The experimental results show that the digital watermarking algorithm has a high transparency and big capacity as well as certain robustness.

Increasing demand and utilization of telemedicine require transmission of medical information and images over internet. Since authenticity of received images is crucial and patient's information should be included with minimum changes in images, robust watermarking schemes are needed. In this paper, we propose a robust watermark method that embeds patient's information outside the region of interest (ROI) in medical image. In order to find appropriate regions for embedding, we use saliency as a means of measuring importance of regions and find blocks having minimum overlap with the ROI. The algorithm employs wavelet transform and also discrete cosine transform (DCT) domains in the embedding stage and redundantly embeds watermark to increase robustness against possible alterations. Moreover, voting is utilized in the extraction phase. Experimental results show the efficiency of the proposed method and better results are obtained as compared to comparable methods with same size of the watermarked data.

Security and authentication of the people information is effectively provided through various watermarking algorithms. Most of the conventional watermarking algorithms have their own drawbacks in maintaining the quality and security of the image against various attacks. In order to overcome the above stated issues, a watermarking technique based on optimization is required for preserving image quality. In this paper, singular value decomposition (SVD) based digital image watermarking (DIW) scheme is proposed in hybrid discrete cosine transform (DCT) and discrete wavelet transform (DWT) using optimization algorithms like particle swarm optimization (PSO) and Jaya algorithm. The watermark image (WI) is embedded into low frequency DWT sub-band of DCT of cover image. PSO and Jaya algorithms are applied to improve the security threats and quality by assessing the fitness function. The work involves in utilizing the feature points in embedding of watermarks and analyzing the impact of various types of attacks with respect to image quality. The performance parameters like peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation coefficient of fitness function given by PSO and Jaya algorithm.

Medical images exchanged over vulnerable networks are sensitive to modifications or tampering which could lead to misdiagnosis, risking the lives of the patients. Therefore, to provide safe and secure transmission of medical images, key security requirements such as authenticity, integrity, and confidentiality must be addressed. In this paper, a novel and efficient algorithm is proposed which provides security services for transmitted medical images in tele-medicine applications. The proposed algorithm employs joint encryption/watermarking procedures to provide the required security services, as well as to provide control access privileges at the receiving side. The algorithm is based on reversible data hiding principles that guarantee the extraction of the hidden data while restoring the original image unaffected. The embedded data is basically two watermarks; a spatial domain watermark and an encrypted domain watermark. The dual watermarks allow the authenticity and integrity of the transmitted medical images to be verified in the spatial and encrypted domains. The proposed algorithm has been extensively evaluated using three performance metrics; embedding capacity, visual image quality, and entropy. The ability of the algorithm to fulfill the security, reversibility, and separability requirements has been validated.

A coding layer robust reversible watermarking algorithm for digital image in multi-antenna system

Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans. Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image. Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN). The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature. This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express. Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm. The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel. Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed. The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved. Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.

Under the background of telemedicine, a new registration and mosaic algorithm for medical images is proposed in this paper to solve the problems of electronic noise, uneven illumination and ray scattering in the real-time medical process. The improved Retinex algorithm by trilateral filter and homomorphic filtering algorithm can enhance the image effectively in preprocessing. The improved phase correlation algorithm based on log polar transformation was used to calculate parameters, such as rotation, scaling and translation. Then, the SUSAN corner matching points were extracted in overlapping positions, the improved KD tree was used for enhancing matching efficiency. Later, matching points were purified by the improved RANSAC algorithm. Finally, images were processed by Laplacian pyramid decomposition algorithm to make the image joint seemed smooth and natural. The results of experiments and evaluation criteria confirm that the new method has high robustness in the process of medical image registration and stitching in the network.

Digital image processing is versatile research in this era. Many researchers implement different types of organizations like image restoration, image enhancement, color image processing, image segmentation etc. Image enhancement technique is among the simplest and most appealing area of digital image processing. Enhancement techniques like brightness preservation, contrast enhancement highlight certain features means depend which part of the image want to be enhance some application some input image including noise, reduction or removal of noise is also form of image enhancement. Brightness preservation has enhanced visual quality of digital image so that the limitation contained in these images is used for various applications in a better way. A very popular technique for image enhancement is histogram equalization (HE) and curvelet transformation. HE technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. Another widely used technique is curvelet transformation. This technique is identified and separate bright regions of image but more error rate and low peak signal to noise ratio(PSNR), result of this technique is brightness preservation level is low and output image is gray. This paper design a hybrid model through discrete cosine transformation, discrete wavelet transformation and combine output of both techniques with image fusion. Proposed algorithm enhanced features and removal noise by decomposition of image using DWT and discrete cosine transformation, adaptive histogram equalization is very important part in this algorithm for smooth image. The tested results of different images are comparing with previous method, generating result with different parameters; less mean square error and high PSNR for improve the quality of an image. This paper presents a hybrid model used various parameter for enhance images like satellite images, medical images etc.

With the popularization of digital medicine, the protection of patients’ privacy has become extremely important in the medical field. Embedding watermarks into medical images is one of the effective ways to protect patients’ privacy. However, the existing watermarking algorithms have poor robustness under geometric attacks. To solve this problem, aiming at the particularity of medical image, a robust zero-watermarking algorithm using Curvelet-Dct and RSA pseudo-random sequence is proposed in this paper. First, extract the visual feature vectors of medical images where the energy is most concentrated via perform DCT transformation on the Coarse layer of Curvelet transformation. Then, the pseudo-random sequence generated by the RSA algorithm is used to encrypt the watermark which improved the security of the patients’ privacy. Combined with the concept of zero-watermark, the experimental results show that the proposed algorithm has good robustness in both conventional attacks and geometric attacks.

Together with the rapid growth of Internet service, copyright violation problems, such as unauthorized duplication and alteration of digital materials, have increased considerably (Langelaar et al., 2001). Therefore copyright protection over the digital materials is a very important issue that requires an urgent solution. The watermarking is considered as a viable technique to solve this problem. Until now, numerous watermarking algorithms have been proposed. Most of them are image watermarking algorithms and relatively few of them are related with video sequences. Although image watermarking algorithms can be used to protect the video signal, generally they are not efficient for this purpose, because image watermarking algorithms does not consider neither temporal redundancy of the video signal nor temporal attacks, which are efficient attacks against video watermarking (Swanson et al., 1998). Generally, in the watermarking schemes for copyright protection, the embedded watermark signal must be imperceptible and robust against common attacks, such as lossy compression, cropping, noise contamination and filtering (Wolfgang et al., 1999). In addition, video watermarking algorithms must satisfy the following requirements: a blind detection, high speed process and conservation of video file size. The blind detection means that the watermark detection process does not require original video sequence, and the temporal complexity of watermark detection must not affect video decoding time. Also the file size of video sequence must be similar, before and after watermarking. Due to the redundancy of the video sequence, some attacks such as frame dropping and frame averaging can effectively destroy the embedded watermark, without cause any degradation to the video signal. A design of an efficient video watermarking algorithm must consider this type of attacks (Wolfgang et al., 1999). Basically, video watermarking algorithms can be classified into three categories: watermarking in base band (Wolfgang et al., 1999; Hartung & Girod 1998; Swanson et al., 1998; Kong et al., 2006), watermarking during video coding process (Liu et al., 2004; Zhao et al., 2003; Ueno 2004; Noorkami & Mersereau 2006) and watermarking in coded video sequence (Wang et al., 2004; Biswas et al., 2005; Langelaar & Lagendijk 2002). In the base band technique, the watermarking process is realized in uncompressed video stream, in which almost all image watermarking algorithms can be used, however generally computational complexity for watermark embedding and detection is considerably high for

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images’ quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image.

Registration is the process of finding transformations that makes correspondence between related image pairs so that pixels in the two images precisely coincide to the same points in the scene. Once registered, the image can be combined or fused in a way that improves useful information extraction. The log-polar transform (LPT) is a well known space variant image registration scheme used for medical images. However though LPT is invariant to rotation and scale changes, it does not support translation faithfully. In this paper, a hybrid algorithm for image registration that produces faithful mapping under affine geometrical distortion using LPT, Fourier transform (FT), and phase correlation has been presented. The resultant algorithm is invariant to rotation, scale and translation. The proposed algorithm is verified on medical images with affine shifting, partial data, and in the presence of noise.

According to the traffic sign recognition process converges slowly, easy to fall into local minimum of defects in general BP algorithm, a BP algorithm is discussed in the paper, which is based on the improved conjugate gradient method on the basis of Fletcher-Reeves linear search method. At the beginning, the basic principle of the algorithm is introduced; meanwhile, an in-depth analysis is discussed from the theoretical aspects in the paper, and then the trained BP neural network is applied into the function approximation. Experimental results show that the algorithm has many advantages, such as it has a good effect in solving the rotation and scale invariance with the image, the extracted features with translation, scale and rotation invariance. The fast convergence speed and less iteration of this algorithm can meet the requirements of unmanned vehicle autonomous navigation.