Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
Traditional encryption algorithms are inefficient when applied to image encryption because image data have the characteristics of\nlarge data sizes and strong correlations between adjacent pixels. The shortcomings of the traditional Data encryption standard\n(DES) encryption algorithm when applied to image encryption are analyzed, and a new image encryption algorithm based on the\ntraditional DES encryption algorithm model, chaotic systems, DNA computing, and select cipher-text output is proposed. Select\ncipher-text output selects cipher image with the biggest entropy, and it can increase the randomness of cipher image and reduce\nthe risk of encryption system being broken down. This algorithm overcomes the shortcomings of high computational complexity\nand inconvenient key management that the traditional text encryption algorithm has when applied to image encryption. The\nexperimental results show that the security of this algorithm is verified by analyzing the information entropy, image correlation of\nadjacent pixels and other indexes. At the same time, this algorithm passes the noise attack test and the occlusion attack test, so it\ncan resist common attacks....
The traditional reversible data hiding technique is based on cover image modification which inevitably leaves some traces of\nrewriting that can be more easily analyzed and attacked by the warder. Inspired by the cover synthesis steganography-based\ngenerative adversarial networks, in this paper, a novel generative reversible data hiding (GRDH) scheme by image translation is\nproposed. First, an image generator is used to obtain a realistic image, which is used as an input to the image-to-image translation\nmodel with CycleGAN. After image translation, a stego image with different semantic information will be obtained. The secret\nmessage and the original input image can be recovered separately by a well-trained message extractor and the inverse transform of\nthe image translation. The experimental results have verified the effectiveness of the scheme....
With the development of computer vision and image segmentation technology, medical image segmentation and recognition\ntechnology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on\nartificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable\nenergy resources and peopleâ??s time but also requires certain expertise to obtain useful feature information, which no\nlonger meets the practical application requirements of medical image segmentation and recognition. As an efficient image\nsegmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical\nimage segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid\ndevelopment of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an\neffective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is\npresented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information\nrecovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural\nnetwork is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image\nclassifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and\nmorphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation\nmethod has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various\nmedical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new\nattempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and\nmethods for further development and improvement of adaptive medical image segmentation technology....
Magnetic flux leakage (MFL) detection is commonly employed to detect wire rope defects. However, nondestructive testing\n(NDT) of a wire rope still has problems. A wire rope nondestructive testing device based on a double detection board is designed to\nsolve the problems of large volume, complex operations, and limited circumferential resolution due to sensor size in traditional\ndevices. The device adopts two magnetic sensor arrays to form the double detection board and collects the MFL data of the\nmagnetized wire rope. These sensors on the double detection board are staggered and evenly arranged on the circumference of the\nwire rope. A super-resolution algorithm based on interpolation uses non-subsampled shearlet transform (NSST) combining\nprincipal component analysis (PCA) and Gaussian fuzzy logic (GFL) and fuses the data of double detection board to improve the\nresolution and quality of defect images. Image quality measurement and comparison experiments are designed to verify that defect\nimages are effectively enhanced. An AdaBoost classifier is designed to classify defects by texture features and invariant moments of\ndefect images. The experimental results show that the detection device not only improves the circumferential resolution, but also\nthe operation is simple; the resolution and quality of the defect images are improved by the proposed super-resolution algorithm,\nand defects are identified by using the AdaBoost classifier....
In this paper, we propose a rapid rigid registration method for the fusion visualization of intraoperative 2D X-ray angiogram (XA)\nand preoperative 3D computed tomography angiography (CTA) images. First, we perform the cardiac cycle alignment of a\npatientâ??s 2D XA and 3D CTA images obtained from a different apparatus. Subsequently, we perform the initial registration\nthrough alignment of the registration space and optimal boundary box. Finally, the two images are registered where the distance\nbetween two vascular structures is minimized by using the local distance map, selective distance measure, and optimization of\ntransformation function. To improve the accuracy and robustness of the registration process, the normalized importance value\nbased on the anatomical information of the coronary arteries is utilized.Theexperimental results showed fast, robust, and accurate\nregistration using 10 cases, each of the left coronary artery (LCA) and right coronary artery (RCA). Our method can be used as a\ncomputer-aided technology for percutaneous coronary intervention (PCI). Our method can be applied to the study of other types\nof vessels....
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