Encryption Algorithm for Medical Volume Data Based on Three-dimensional Arnold Transformation and Chaos

This paper proposed a compressed domain algorithm of robust blind watermarking method for medical volume data, which addresses the problems of authentication and protection of personal information. The scheme obtains the visual feature vectors of medical volume data in DCT compressed domain. Through the concept of zero-watermarking, the algorithm for watermarking of volume data that is robust to geometric attacks. The experimental results demonstrate that the proposed algorithm has good invisibility and robustness.

[This retracts the article on p. 131 in vol. 8.].

Applying digital watermarking technique for the security protection of medical information systems is a hotspot of research in recent years. In this paper, we present a robust watermarking algorithm for medical volume data using 3D DWT-DCT and Logistic Map. After applying Logistic Map to enhance the security of watermarking, the visual feature vector of medical volume data is obtained using 3D DWT-DCT. Combining the feature vector, the third party concept and Hash function, a zero-watermarking scheme can be achieved. The proposed algorithm can mitigate the illogicality between robustness and invisibility. The experiment results show that the proposed algorithm is robust to common and geometrical attacks.

Image encryption algorithm with random scrambling based on one-dimensional logistic self-embedding chaotic map

A novel quantum gray-level image encryption and decryption algorithm based on image correlation decomposition is proposed. The correlation among image pixels is established by utilizing the superposition and measurement principle of quantum states. And a whole quantum image is divided into a series of sub-images. These sub-images are stored into a complete binary tree array constructed previously and then randomly performed by one of the operations of quantum random-phase gate, quantum revolving gate and Hadamard transform. The encrypted image can be obtained by superimposing the resulting sub-images with the superposition principle of quantum states. For the encryption algorithm, the keys are the parameters of random phase gate, rotation angle, binary sequence and orthonormal basis states. The security and the computational complexity of the proposed algorithm are analyzed. The proposed encryption algorithm can resist brute force attack due to its very large key space and has lower computational complexity than its classical counterparts.

Geology-guided reconditioning of seismic data is the key to improving extraction of relevant geologic information from three-dimensional (3-D) data volumes. In the Fullerton Clear Fork reservoir, the most convenient and useful tools for data reconditioning are phase shifting and high-frequency enhancement. A simple seismic phase rotation (to 90°) reconditions seismic data for impedance representation, roughly linking seismic amplitude directly to log lithology and porosity and making stratigraphic correlation more accurate. High-frequency enhancement raises the dominant frequency of 3-D seismic data from 30 to 50 Hz, improving seismic resolution without significantly deteriorating signal-to-noise ratio. More accurate and finer scale seismic mapping of reservoir parameters is achieved from model-based progressive inversion that seamlessly integrates geologic interpretation of well-derived seismic data and model-based seismic inversion for high-resolution (2 ft [0.6 m]) impedance models.

To evaluate early diabetes-induced changes in retinal thickness and microvasculature in a type 2 diabetic mouse model by using optical coherence tomography (OCT)/optical microangiography (OMAG). Twenty-two-week-old obese (OB) BTBR mice (n = 10) and wild-type (WT) control mice (n = 10) were imaged. Three-dimensional (3D) data volumes were captured with spectral domain OCT using an ultrahigh-sensitive OMAG scanning protocol for 3D volumetric angiography of the retina and dense A-scan protocol for measurement of the total retinal blood flow (RBF) rate. The thicknesses of the nerve fiber layer (NFL) and that of the NFL to the inner plexiform layer (IPL) were measured and compared between OB and WT mice. The linear capillary densities within intermediate and deep capillary layers were determined by the number of capillaries crossing a 500-μm line. The RBF rate was evaluated using an en face Doppler approach. These quantitative measurements were compared between OB and WT mice. The retinal thickness of the NFL to IPL was significantly reduced in OB mice (P < 0.01) compared to that in WT mice, whereas the NFL thickness between the two was unchanged. 3D depth-resolved OMAG angiography revealed the first in vivo 3D model of mouse retinal microcirculation. Although no obvious differences in capillary vessel densities of the intermediate and deep capillary layers were detected between normal and OB mice, the total RBF rate was significantly lower (P < 0.05) in OB mice than in WT mice. We conclude that OB BTBR mice have significantly reduced NFL-IPL thicknesses and total RBF rates compared with those of WT mice, as imaged by OCT/OMAG. OMAG provides an unprecedented capability for high-resolution depth-resolved imaging of mouse retinal vessels and blood flow that may play a pivotal role in providing a noninvasive method for detecting early microvascular changes in patients with diabetic retinopathy.

Outstanding results have been achieved with magnetic resonance (MR) angiography, which has become competitive with conventional angiography as a vascular imaging technique. With the advances in gradient technology and the capability of imaging vessels in a single breath hold, one can track an intravenous bolus of paramagnetic contrast agent and evaluate the entire mesenteric circulation. Use of a breath-hold technique obviates the problem of motion artifact. Use of targeted maximum-intensity projection images from the three-dimensional (3D) volume data allows evaluation of the vascular anatomy at different phases (arterial and venous) of contrast enhancement. MR angiography performed with a contrast-enhanced breath-hold fat-suppressed 3D technique allows visualization of the superior mesenteric vessels, celiac artery, and portal vein in healthy subjects. Pathologic conditions of the mesenteric vessels that can be identified with this technique include stenosis or occlusion of the superior mesenteric artery, celiac artery aneurysm, thrombosis of the superior mesenteric vein, portal hypertension with varices, and vascular invasion by pancreatic carcinoma.

Virtual endoscopy is the simulation of endoscopic interventions using methods of virtual reality and computer graphics. Usually, three-dimensional (3-D) volume data from computed tomography (CT) scans, magnetic resonance imaging (MRI), 3-D ultrasound, rotational angiography or other sources are used to generate a 3-D view of the inside of the studied structures.

To evaluate the feasibility and accuracy of fetal gender assignment from three-dimensional ultrasound (3D US) data at 10-24 weeks' gestation. Three-dimensional ultrasound volume data on 47 fetuses were reviewed and divided into groups: 10-14, 15-18, and 19-24 weeks. Fetal genitalia were studied in axial and sagittal planes for gender assignment, using published criteria. The most valuable plane for assignment was noted. Gender assignment was compared with gender at birth. Gender assignment was possible in 44 of 47 fetuses; 25 assigned male and 19 female. Between 10 and 14 weeks the mid-sagittal plane alone was diagnostic for all fetuses. Accuracy of assignment between 11 and 14 weeks was 100%. Between 15 and 18 and 19 to 24 weeks, male assignment was 100% accurate. Female assignment was 100% accurate between 15 and 18 weeks. Accuracy decreased to 60% in the 19 to 24 week group, however, in two of the five cases in which gender was wrongly assigned to be male, the assignment was prospectively considered doubtful due to poor resolution of volume data. Excluding these two cases, accuracy for female assignment in the 19-24 week group was 100%. Using 3D US, gender assignment was possible in 44 of 47 of fetuses, as early as 11 weeks' gestation. In early gestation, 11-14 weeks, accuracy of male and female gender assignment was 100%.

Medical data security is an important guarantee for intelligent medical system. Medical video data can help doctors understand the patients’ condition. Medical video retargeting can greatly reduce the storage capacity of data on the premise of preserving the original content information as much as possible. The smaller volume of medical data can reduce the execution time of data encryption and threat detection algorithm and improve the performance of medical data security methods. The existing methods mainly focus on the temporal pixel relationship and foreground motion between adjacent frames, but these methods ignore the user’s attention to the video content and the impact of background movement on retargeting, resulting in serious deformation of important content and area. To solve the above problems, this paper proposes an innovative video retargeting method, which is based on visual attention and motion estimation. Firstly, the visual attention map is obtained from eye tracking data, by K-means clustering method and Euclidean distance factor equation. Secondly, the motion estimation map is generated from both the foreground and background displacements, which are calculated based on the feature points and salient object positions between adjacent frames. Then, the visual attention map, the motion estimation map, and gradient map are fused to the importance map. Finally, video retargeting is performed by mesh deformation based on the importance map. Experiment on open datasets shows that the proposed method can protect important area and has a better effect on salient object flutter suppression.

In 3D image processing, such as medical volume data and industrial CT volume data analysis, speckle noise suppressing significantly affects their accuracy. This paper utilizes a group of volume morphology arithmetic operators, mainly including volume open and volume close, by extending area morphology into 3-D space. Using these operators, the light and dark objects of small size could be removed directly from the 3-D space of the target objects, while the connectivity of the main 3-D target objects in the volume data is still preserved. To improve the volume morphology operations efficiency, we decompose volume data in bitplanes instead of in gray scale space to reduce the binary volumes. To verify the effect of the improved volume morphology operators, they are applied to suppress speckle noises in 3-D images of coral and rat skull and compared with original volume morphology operations. Experimental results show that the algorithm proposed in this paper can significantly reduce computing time, while maintaining comparable results to original operations results.

S-box mainly plays the role of confusion in the encryption process as an important component. For the new encryption algorithm PRESENT proposed in 2007, S-box impacts on the security of the encryption algorithm directly. This paper briefly describes the process of PRESENT algorithm and proposes an improved S-box to solve the problem that the original PRESENT S-box has anti-fixed point. Then a random PRESENT encryption algorithm based on dynamic S-box is proposed. The dynamic multiple S-boxes technology is used to implement random PRESENT algorithm, to enhance the security of the cryptographic algorithm. Finally, the security analysis is done, and it suggests that dynamic S-box has a superior ability to resist differential attack and linear attack. By comparison to the diffusion rate of original PRESENT S-box, our dynamic S-box has better avalanche effect. With the rapid development of the Internet of Things, the radio frequency identification (RFID), wireless sensor network (WSN), and other new technologies go deep into all aspects of people's work and life. However, RFID and WSN are based on wireless network to transfer information. The transmission of information is not difficult to be obtained, interfered, and even destroyed by the attackers. So information security technology is widely used in protecting networks to enhance the safety of secret information. And the traditional symmetric encryption algorithm is often restricted by hardware, which cannot achieve good effects under the condition of limited hardware and power consumption. Therefore, lightweight cipher algorithm such as PRESENT(1) and LED(2) has gradually become the realistic choice to ensure the information security of Internet of Things. The lightweight block encryption algorithm PRESENT (1), put forward by Bogdanov in 2007, is carefully designed with area and power constraints uppermost in limited conditions. So the PRESENT algorithm still has high security in the application of limited space and power consumption, such as sensor network node. However, the permutation transformation of PRESENT algorithm is different with the column confusion of AES. It uses bit as the permutation function input unit while AES uses byte for permutation. The permutation layer of PRESENT encryption algorithm is a perfectly symmetrical structure and easy implemented. Because the structure of this kind of design has poor diffusibility, the PRESENT algorithm diffusively quality mainly depends on the diffusion properties of S-box. So S-box, as a nonlinear component in lightweight block encryption algorithm, affects the strength of the whole structure of the cipher. To strengthen DES by using existing hardware, an improved method of DES which uses the S-box related to a key is put forward in paper (4), and the researcher has demonstrated the safety of the improved method. Meanwhile, a new key related

Visualization of classification uncertainty of the medical materials is very significant, as different classifications may lead to different visualization results, and different results may cause completely different diagnosis or pre-operative planning decisions, and thus have different consequences to patients. Traditional Direct Volume Rendering (DVR) enables medical experts to visualize the classification uncertainty by random adjustment of the transfer function. However, three main problems exist by using this method: first, the resulting renderings do not indicate any quantitative information about the classification uncertainty of different materials; second, the resulting renderings are randomly revealed by random adjustment of the TF; third, both classification task and optical property assignment task are mixed together in one step. These problems may make this method (1) fail to enable medical experts to make accurate diagnosis or pre-operative planning decision; (2) unable to provide medical experts with a clear concept of how medical volume data are classified. To address these problems, we proposed a probabilistic slider system, which compared to the traditional DVR, enables medical experts to make more accurate diagnosis or pre-operative planning decision, and have a clearer concept of how the medical volume data being classified.

An accurate and effective FMM-based approach for freehand 3D ultrasound reconstruction