Robust secret image sharing scheme resistance to maliciously tampered shadows by AMBTC and quantization

For (k, n)-threshold secret image sharing (SIS) scheme, only k or more than k complete parts can recover the secret information, and the correct image cannot be obtained if the count of shadow images is not enough or the shadow images are damaged. The existing schemes are weak in resisting large-area shadow image tampering. In this paper, we propose a robust secret image sharing scheme resisting to maliciously tampered shadow images by Absolute Moment Block Truncation Coding (AMBTC) and quantization (RSIS-AQ). The secret image is successively compressed in two ways: AMBTC and quantization. The sharing shadow images contain the sharing results of both compressed image from different parts, so that even the shadow images are faced with large-scale area of malicious tampering, the secret image can be recovered with acceptable visual quality. Compared with related works, our scheme can resist larger area of tampering and yield better recovered image visual quality. The experimental results prove the effectiveness of our scheme.     

A secure lossless recovery for medical images based on image encoding and data self-embedding

To protect the security of data outsourced to the cloud, the tampers detection and recovery for outsourced image have aroused the concern of people. A secure tampering detection and lossless recovery for medical images (MI) using permutation ordered binary (POB) number system is proposed. In the proposed scheme, the region of interest (ROI) of MI is first extracted, and then, ROI is divided into some no-overlapping blocks, and image encoding is conducted on these blocks based on the better compression performance of JPEG-LS for medical image. After that, the generated compression data by all the blocks are divided into high 4-bit and low 4-bit planes, and shuffling and combination are used to generate two plane images. Owing to the substantial redundancies space in the compressed data, the data of each plane are spread to the size of the original image. Lastly, authentication data of two bits is obtained for every pixel and inserted into the pixel itself within the each plane, and the corresponding 10-bit data is transformed into the POB value of 8-bit. Furthermore, encryption is implemented on the above image to produce two shares which can be outsourced to the cloud server. The users can detect tampered part and recover original image when they down load the shares from the cloud. Extensive experiments on some ordinary medical image and COVID-19 image datasets show that the proposed approach can locate the tampered parts within the MI, and the original MI can be recovered without any loss even if one of the shares are totally destroyed, or two shares are tampered at the ration not more than 50%. Some comparisons and analysis are given to show the better performance of the scheme.

     

Secure Similar Image Search and Copyright Protection over Encrypted Medical Image Databases

The recent advances in digital medical imaging and storage in cloud are bringing about more demands for efficient and secure image retrieval and management. Typically, medical images are very sensitive to changes where any modifications in its content may bring about an erroneous medical diagnosis. Therefore, securing medical images is a very essential process and the major task is, the medical image must maintain their sensitive contents at the time of reconstruction. The proposed methodology executes a secure image encryption and search of medical images proficiently over encrypted image database without leaking any sensitive data. It also ensures medical data integrity by introducing an efficient recovery mechanism on ROI of the image. The proposed scheme obtains recovery information about the image from the ROI of the medical data and embeds it in the RONI region using IWT transform which act as a reversible watermarking. If any alterations or tampers are caused to ROI at the third-party end, then it can be identified and recovered from the obtained recovery data. Besides, the model also executes a Copyright protection scheme to locate the authorized users, who illegally duplicate and distribute the retrieved image to unauthorized entities.     

Adaptive medical image encryption algorithm based on multiple chaotic mapping

Digital images are now widely used in modern clinic diagnosis. The diagnostic images with confidential information related to patients’ privacy are stored and transmitted via public networks. Secured schemes to guarantee confidentiality of patients’ privacy are becoming more and more vital. This paper proposes an adaptive medical image encryption algorithm based on improved chaotic mapping in order to overcome the defects of the existing chaotic image encryption algorithm. First, the algorithm used Logistic-sine chaos mapping to scramble the plain image. Then, the scrambled image was divided into 2-by-2 sub blocks. By using the hyper-chaotic system, the sub blocks were adaptively encrypted until all the sub block encryption was completed. By analyzing the key space, the information entropy, the correlation coefficient and the plaintext sensitivity of the algorithm, experimental results show that the proposed algorithm overcomes the shortcoming of lack of diffusion in single direction encryption. It could effectively resist all kinds of attacks and has better security and robustness.     

Dynamic Quality of Service Model for Improving Performance of Multimedia Real-Time Transmission in Industrial Networks

Nowadays, quality of service (QoS) is very popular in various research areas like distributed systems, multimedia real-time applications and networking. The requirements of these systems are to satisfy reliability, uptime, security constraints and throughput as well as application specific requirements. The real-time multimedia applications are commonly distributed over the network and meet various time constraints across networks without creating any intervention over control flows. In particular, video compressors make variable bit-rate streams that mismatch the constant-bit-rate channels typically provided by classical real-time protocols, severely reducing the efficiency of network utilization. Thus, it is necessary to enlarge the communication bandwidth to transfer the compressed multimedia streams using Flexible Time Triggered- Enhanced Switched Ethernet (FTT-ESE) protocol. FTT-ESE provides automation to calculate the compression level and change the bandwidth of the stream. This paper focuses on low-latency multimedia transmission over Ethernet with dynamic quality-of-service (QoS) management. This proposed framework deals with a dynamic QoS for multimedia transmission over Ethernet with FTT-ESE protocol. This paper also presents distinct QoS metrics based both on the image quality and network features. Some experiments with recorded and live video streams show the advantages of the proposed framework. To validate the solution we have designed and implemented a simulator based on the Matlab/Simulink, which is a tool to evaluate different network architecture using Simulink blocks.     

A Novel Medical Image Protection Scheme Using a 3-Dimensional Chaotic System

Recently, great concerns have been raised regarding the issue of medical image protection due to the increasing demand for telemedicine services, especially the teleradiology service. To meet this challenge, a novel chaos-based approach is suggested in this paper. To address the security and efficiency problems encountered by many existing permutation-diffusion type image ciphers, the new scheme utilizes a single 3D chaotic system, Chen''s chaotic system, for both permutation and diffusion. In the permutation stage, we introduce a novel shuffling mechanism, which shuffles each pixel in the plain image by swapping it with another pixel chosen by two of the three state variables of Chen''s chaotic system. The remaining variable is used for quantification of pseudorandom keystream for diffusion. Moreover, the selection of state variables is controlled by plain pixel, which enhances the security against known/chosen-plaintext attack. Thorough experimental tests are carried out and the results indicate that the proposed scheme provides an effective and efficient way for real-time secure medical image transmission over public networks.     

Artificial intelligence in image reconstruction: The change is here

Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)-based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.     

Cryptanalysis and Improvement of a Biometric-Based Multi-Server Authentication and Key Agreement Scheme

With the security requirements of networks, biometrics authenticated schemes which are applied in the multi-server environment come to be more crucial and widely deployed. In this paper, we propose a novel biometric-based multi-server authentication and key agreement scheme which is based on the cryptanalysis of Mishra et al.’s scheme. The informal and formal security analysis of our scheme are given, which demonstrate that our scheme satisfies the desirable security requirements. The presented scheme provides a variety of significant functionalities, in which some features are not considered in the most of existing authentication schemes, such as, user revocation or re-registration and biometric information protection. Compared with several related schemes, our scheme has more secure properties and lower computation cost. It is obviously more appropriate for practical applications in the remote distributed networks.     

A Generic Approach for Efficient Detection of Vascular Structures

Vascular segmentation is often required in medical image analysis for various imaging modalities. Despite the rich literature in the field, the proposed methods need most of the time adaptation to the particular investigation and may sometimes lack the desired accuracy in terms of true positive and false positive detection rate. This paper proposes a general method for vascular segmentation based on locally connected filtering applied in a multiresolution scheme. The filtering scheme performs progressive detection and removal of the vessels from the image relief at each resolution level, by combining directional 2D-3D locally connected filters (LCF). An important property of the LCF is that it preserves (positive contrasted) structures in the image if they are topologically connected with other similar structures in their local environment. Vessels, which appear as curvilinear structures, can be filtered out by an appropriate LCF set-up which will minimally affect sheet-like structures. The implementation in a multiresolution framework allows dealing with different vessel sizes. The outcome of the proposed approach is illustrated on several image modalities including lung, liver and coronary arteries. It is shown that besides preserving high accuracy in detecting small vessels, the proposed technique is less sensitive with respect to noise and the presence of pathologies of positive-contrast appearance on the images. The detection accuracy is compared with a previously developed approach on the 20 patient database from the VESSEL12 challenge.     

Efficient leakage-resilient public key encryption from DDH assumption

For an encryption scheme to be applied in practical applications, it should withstand various leakage attacks. In this paper, we present a new leakage-resilient public key encryption scheme whose security is based on the classical DDH (decisional Diffie-Hellman) assumption. In the computational cost, our proposed scheme is more efficient than the original Cramer-Shoup leakage-resilient public key encryption scheme. At the same time, our new scheme also enjoys a shorter (public and secret) key length, and a higher relative key leakage ratio. We formally prove our new proposal is semantically secure against adaptive posteriori chosen ciphertext key-leakage attacks assuming the hardness of the DDH problem without random models.     

Multi-level on-demand access control for flexible data sharing in cloud

The exponential growth of data storage and sharing in cloud demands an efficient access control mechanism for flexible data sharing. Attribute-Based Encryption (ABE) is a promising cryptographic solution to share data among users in the cloud. But it suffers from user revocation, attribute revocation, forward secrecy and backward secrecy issues. Communication and computation overhead is more due to the linear variation in the size of ciphertext and the secret key with respect to the number of attributes. In this paper, we investigate an on-demand access control for flexible sharing of secure data among randomly selected users. It is a tunable access control mechanism for the flexible sharing of ciphertext classes in the cloud. It delegates the decryption rights of any set of ciphertext classes among the users only if their attributes are satisfied with the access policy associated with ciphertext and if they should possess a compact key corresponding to the intended set of ciphertext classes. It produces a constant size ciphertext and a compact secret key to efficiently utilize the storage space and reduce the communication cost. The compact key aggregates the power of secret keys used to encrypt the outsourced data. This method flexibly shares the ciphertext classes among the randomly selected users with a specific set of attributes. All other ciphertext classes outside the set remain confidential. It allows dynamic data updates by verifying the data manipulation privilege of users with the help of claim policy. The proposed scheme provides access control of varying granularity, at user-level, at file-level, and attribute-level. Granularity levels can be chosen based on applications and user demands. Hence, it is a multi-level, tunable access control over the shared data. It is very useful for secure data storage. This scheme tackles user revocation and attribute revocation problems so that, it allows the data owner to revoke a specific user or a group of users. It prevents forward and backward secrecy issues.

     

Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network

Precise liver segmentation in abdominal MRI images is one of the most important steps for the computer-aided diagnosis of liver pathology. The first and essential step for diagnosis is automatic liver segmentation, and this process remains challenging. Extensive research has examined liver segmentation; however, it is challenging to distinguish which algorithm produces more precise segmentation results that are applicable to various medical imaging techniques. In this paper, we present a new automatic system for liver segmentation in abdominal MRI images. The system includes several successive steps. Preprocessing is applied to enhance the image (edge-preserved noise reduction) by using mathematical morphology. The proposed algorithm for liver region extraction is a combined algorithm that utilizes MLP neural networks and watershed algorithm. The traditional watershed transformation generally results in oversegmentation when directly applied to medical image segmentation. Therefore, we use trained neural networks to extract features of the liver region. The extracted features are used to monitor the quality of the segmentation using the watershed transform and adjust the required parameters automatically. The process of adjusting parameters is performed sequentially in several iterations. The proposed algorithm extracts liver region in one slice of the MRI images and the boundary tracking algorithm is suggested to extract the liver region in other slices, which is left as our future work. This system was applied to a series of test images to extract the liver region. Experimental results showed positive results for the proposed algorithm.     

Dissipative particle dynamics simulation of the relaxation behaviour of a triblock copolymer supramolecular network

Since block copolymers self-assemble into various nanostructures and these are widely used in soft materials such as cosmetics and paints, these continue to be the subjects of fundamental studies that progress new technologies. ABA triblock copolymers self-organise into supramolecular networks in which crosslinked structures change upon heating and other external stimulation. Supramolecular networks that consist of ABA triblock copolymers have three components: bridges, loops and dangles. These supramolecular networks have structural regions (clusters) in which end blocks of polymer chains are aggregated and connected by the internal blocks of the polymer chains. Despite of the importance of relaxation behaviour during the measurement and control of polymer materials, the molecular-level behaviour of these systems has not been addressed. We observed pull-out phenomenon that the ends of these polymers detach from clusters, and estimated characteristic detachment times by counting the number of detachments and compared it with the time of longest relaxation in that system.     

Structural similarity regularized and sparse coding based super-resolution for medical images

Recently the single image super-resolution reconstruction (SISR) via sparse coding has attracted increasing interests. Considering that there are obviously repetitive image structures in medical images, in this study we propose a regularized SISR method via sparse coding and structural similarity. The pixel based recovery is incorporated as a regularization term to exploit the non-local structural similarities of medical images, which is very helpful in further improving the quality of recovered medical images. An alternative variables optimization algorithm is proposed and some medical images including CT, MRI and ultrasound images are used to investigate the performance of our proposed method. The results show the superiority of our method to its counterparts.     

A theoretical application of selectable markers in bacterial episomes for a DNA cryptosystem

A theoretical model utilizing the principle of selection for the purposes of DNA cryptography is proposed. A method to enhance the security of DNA cryptosystems that utilize polymerase chain reaction primer keys is presented. Double-encryption systems - encryption systems that have two keys - like the cryptosystem advanced in this paper, are generally more secure than systems that have one key. Two problems with this model are also discussed, and a hypothetical solution to the most major problem is proposed. The chief advantage of this DNA cryptosystem over other DNA cryptosystems is that it has, in theory, two equally secure keys an intruder must break through in order to access a secret message.No experimental analysis has been conducted by the author regarding any of the theoretical methods proposed in this paper.     

Discovering Hominins - Application of Medical Computed Tomography (CT) to Fossil-Bearing Rocks from the Site of Malapa,South Africa

In the South African context, computed tomography (CT) has been used applied to individually prepared fossils and small rocks containing fossils, but has not been utilized on large breccia blocks as a means of discovering fossils, and particularly fossil hominins. Previous attempts at CT imaging of rocks from other South African sites for this purpose yielded disappointing results. For this study, 109 fossil- bearing rocks from the site of Malapa, South Africa were scanned with medical CT prior to manual preparation. The resultant images were assessed for accuracy of fossil identification and characterization against the standard of manual preparation. The accurate identification of fossils, including those of early hominins, that were not visible on the surface of individual blocks, is shown to be possible. The discovery of unexpected fossils is reduced, thus lowering the potential that fossils could be damaged through accidental encounter during routine preparation, or even entirely missed. This study should significantly change the way fossil discovery, recovery and preparation is done in the South African context and has potential for application in other palaeontological situations. Medical CT imaging is shown to be reliable, readily available, cost effective and accurate in finding fossils within matrix conglomerates. Improvements in CT equipment and in CT image quality are such that medical CT is now a viable imaging modality for this palaeontological application.     

Analysis of stained objects in histological sections by spectral imaging and differential absorption.

We describe a new light microscopic imaging system and method to perform high through put color image analysis on histological tissue sections. The system features a computer-controlled, random-access liquid crystal tunable filter and high-resolution digital camera on a conventional brightfield microscope. For any combination of stains, the method determines the spectral transmittance of each stain on the slide and selects two or more wavelengths at which the differential absorption between stain and counterstain is greatest and the exposure time is reasonably short. Flatfield corrected digital images at these wavelengths are acquired and divided to produce a gray scale ratio image. The ratio image is calculated such that the stained features of interest are highlighted above a uniform background and the counterstained features are highlighted below background. Image threshold procedures using either visual inspection or a threshold value determined by the image mean intensity and standard deviation are used to segment the stained features of interest for subsequent morphometry. Results are presented for peroxidase-AEC-labeled tumor tissue and trichrome-stained biomaterial implant tissues. In principle, the method should work for any combination of colored stains. (J Histochem Cytochem 47:1307-1313, 1999)     

Deep-etch views of clathrin assemblies

Clathrin assemblies were adsorbed to mica and freeze-dried by a new procedure that yields 3-D images with much topological detail. These permitted renewed inquiry into how clathrin trimers (i.e. "triskelions") assemble into polygonal coats or baskets. Freeze-drying revealed unsuspected differences in the relative shapes and dimensions of individual trimer building blocks, as compared with the completed polygonal networks, which indicate that the assembly scheme first proposed by Crowther and Peare (1) requires modification. Specifically, the freeze-etch images display the following new features: (1) Trimer arms possess terminal scroll-shaped hooks that can open or close and thus determine their lengths. (2) When extended, trimer arms are sufficiently long to pass around three sides of the final polygonal facet. Since current views hold that the arms pass around only two sides, the remaining length, including the terminal hook, must point into the basket interior. (3) Freeze-dried trimers display bends in their arms at specific loci that determine their final distribution in the completed baskets. (4) The completed struts of the final assemblies are uniformed in the calibre, cylindrical in profile, and travel directly between the vertices of each polygon, without any sign of the slew or width-variation that is predicted by the Crowther and Pearse model. Based on this direct comparison of promoter vs product, by a single technique that can image both, we offer a modified scheme for clathrin coat assembly, in which we predict that the individual arms in each clathrin triskelion emanate from its center in a slewed manner, but the final assembled struts of the basket need not be slewed. Attempts were made to capture assembly intermediates on mica to obtain support for the scheme, but these unfortunately yielded ambiguous images of incomplete polygons with blunt projections, rather than the expected "halo" of uncommitted trimer arms. These we interpret to be "dead ends" that failed to polymerize further because they included proteolyzed components. Further assembly experiments, avoiding such hazards, are indicated.     

UCSF tomography: an integrated software suite for real-time electron microscopic tomographic data collection, alignment, and reconstruction

A real-time alignment and reconstruction scheme for electron microscopic tomography (EMT) has been developed and integrated within our UCSF tomography data collection software. This newly integrated software suite provides full automation from data collection to real-time reconstruction by which the three-dimensional (3D) reconstructed volume is immediately made available at the end of each data collection. Real-time reconstruction is achieved by calculating a weighted back-projection on a small Linux cluster (five dual-processor compute nodes) concurrently with the UCSF tomography data collection running on the microscope's computer, and using the fiducial-marker free alignment data generated during the data collection process. The real-time reconstructed 3D volume provides users with immediate feedback to fully asses all aspects of the experiment ranging from sample choice, ice thickness, experimental parameters to the quality of specimen preparation. This information can be used to guide subsequent data collections. Access to the reconstruction is especially useful in low-dose cryo EMT where such information is very difficult to obtain due to extraordinary low signal to noise ratio in each 2D image. In our environment, we generally collect 2048 x 2048 pixel images which are subsequently computationally binned four-fold for the on-line reconstruction. Based upon experiments performed with thick and cryo specimens at various CCD magnifications (50000x-80000x), alignment accuracy is sufficient to support this reduced resolution but should be refined before calculating a full resolution reconstruction. The reduced resolution has proven to be quite adequate to assess sample quality, or to screen for the best data set for full-resolution reconstruction, significantly improving both productivity and efficiency of system resources. The total time from start of data collection to a final reconstructed volume (512 x 512 x 256 pixels) is about 50 min for a +/-70 degrees 2k x 2k pixel tilt series acquired at every 1 degrees.     

常用医学图像“局部显微镜”程序的研究

在医学临床和科学研究中,常常需要将图像的某个感兴趣区域(ROI)进行放大显示,以便清晰地观察图像的细节.为了实现这一目标,采用IDL语言(Interactive Data Language)编写了应用程序,从而实现了医学图像“局部显微镜”的功能.一系列实验表明:对于各种常用的医学图像类型(灰度图像、RGB图像、DICOM图像等),程序均能较好地实现放大显示的功能.此外,该程序还具有人机交互性强、可移植性高等优点.