Medical Volume Data Research Articles

Prediction of the Possibility of Hemorrhagic Syndrome during Combined Antiplatelet Therapy According to the Krasnodar Region Registry

Background. According to the literature data, acute coronary syndrome (ACS) in 2-20 % of cases is combined with atrial fibrillation (AF). According to the current guidelines of the European Society of Cardiology (ESC), patients with coexisting AF and ACS should receive dual antiplatelet therapy for the prevention of recurrent cardiovascular events and anticoagulant therapy for the prevention of thromboembolic complications. However, this combination is fraught with the development of hemorrhagic syndrome.Aim. To develop a model and software module for predicting possible bleeding in patients with ACS combined with AF taking three-component antithrombotic therapy.Materials and Methods. To build prognostic models for the development of hemorrhagic syndrome, a statistical method was used for classification trees and the neural network procedure implemented in the STATISTICA package. To build prognostic models, a sample was used consisting of 201 patients with a combination of ACS and AF with and without fatal outcome, the state of which was described by 42 quantitative and qualitative clinical indicators. The control group included 205 patients with ACS and intact sinus rhythm.Results. To identify predictors of predictive models of the possible development of hemorrhagic syndrome in patients with triple antithrombotic therapy, the Spearman correlation coefficient was used. The study of correlations allowed to reveal clinical indicators – predictors of prognostic models. After analyzing the predictive ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment that allows determining the possibility of hemorrhagic syndrome in patients with a combination of ACS and AF using classification trees and neural networks.Сonclusion. A classification model and a software module were developed to predict possible bleeding in patients taking three-component antithrombotic therapy. Models contain both quantitative and qualitative (categorical) clinical indicators. The current level of development of data analysis technologies opens up broad horizons for medicine in solving problems on real medical data, without translating them into scoring risk scales, including prediction of the diagnosis of the disease, stage of the disease, treatment outcome, possible complications, etc. High reliability of such systems can be provided by large volumes of medical data accumulated on servers.

Graph-Based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data.

Lossless compression of dynamic 2-D+t and 3-D+t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in telemedicine applications. Motion Compensated Temporal Filtering works well for lossless compression of medical volume data and additionally provides temporal, spatial, and quality scalability features. To achieve a high quality lowpass subband, which shall be used as a downscaled representative of the original data, graph-based motion compensation was recently introduced to this framework. However, encoding the motion information, which is stored in adjacency matrices, is not well investigated so far. This work focuses on coding these adjacency matrices to make the graph-based motion compensation feasible for data compression. We propose a novel coding scheme based on constructing so-called motion maps. This allows for the first time to compare the performance of graph-based motion compensation to traditional block-and mesh-based approaches. For high quality lowpass subbands our method is able to outperform the block-and mesh-based approaches by increasing the visual quality in terms of PSNR by 0.53 dB and 0.28 dB for CT data, as well as 1.04 dB and 1.90 dB for MR data, respectively, while the bit rate is reduced at the same time.

Fine grained medical image fusion using type-2 fuzzy logic

<p>In recent years, many fast-growing technologies coupled with wide volume of medical data for the digitalization of that data. Thus, researchers have shown their immense interest on Multi-sensor image fusion technologies which convey image information based on data from various sensor modalities into a single image. The image fusion technique is a widespread technique for the diagnosis of medical instrumentation and measurement. Therefore, in this paper we have introduced a novel multimodal sensor medical image fusion method based on type-2 fuzzy logic is proposed using Sugeno model. Moreover, a Gaussian smoothen filter is introduced to extract the detailed information of an image using sharp feature points.Type-2 fuzzy algorithm is used to achieve highly efficient feature points from both the b images to provide high visually classified resultant image. The experimental results demonstrate that the proposed method can achieve better performance than the state-of-the- art methods in terms of visual quality and objective evaluation.</p>

Comparative Research of Swarm Intelligence Clustering Algorithms for Analyzing Medical Data

As the Internet of medical Things emerge in the field of medicine, the volume of medical data is expanding rapidly and along with its variety. As such, clustering is an important procedure to mine the vast data. Many swarm intelligence clustering algorithms, such as the particle swarm optimization (PSO), firefly, cuckoo, and bat, have been designed, which can be parallelized to the benefit of mass data computation. However, few studies focus on the systematic analysis of the time complexities, the effect of instances (data size), attributes (dimensionality), number of clusters, and agents of these algorithms. In this paper, we performed a comparative research for the PSO, firefly, cuckoo, and bat algorithms based on both synthetic and real medical data sets. Finally, we conclude which algorithms are effective for the medical data mining. In addition, we recommend the more suitable algorithms that have been developed recently for the different medical data to achieve the optimal clustering.

Floor Map Visualizations of Medical Volume Data

Typically, volumetric medical image data is examined by assessing each slice of an image stack individually. However, this enables observers to assess in-plane spatial relationships between anatomical structures only and requires them to keep track of relationships along the third anatomical plane mentally. Therefore, visualization techniques are researched to support this task by depicting spatial information along the third plane, but they can introduce a high degree of abstraction. To overcome this, we present a novel approach that transforms image stacks with labeled anatomical structures into maps with a three-dimensional layout, namely floor maps. Since this approach increases the visual complexity under certain conditions, some clinical application scenarios, e. g. diagnosis and therapy planning, probably will not benefit. Thus, the approach is mainly aimed to support student training and the generation of clinical reports. We also discuss how to enhance the slice-based exploration of medical image stacks via floor maps and present the results of an informal evaluation with three trained anatomists.

Blockchain-Based Data Preservation System for Medical Data.

Medical care has become an indispensable part of people's lives, with a dramatic increase in the volume of medical data (e.g., diagnosis certificates and medical records). Medical data, however, is easily stolen, tampered with, or even completely deleted. If the above occurs, medical data cannot be recorded or retrieved in a reliable manner, resulting in delay treatment progress, even endanger the patient's life. In this paper, we propose a novel blockchain-based data preservation system (DPS) for medical data. To provide a reliable storage solution to ensure the primitiveness and verifiability of stored data while preserving privacy for users, we leverage the blockchain framework. With the proposed DPS, users can preserve important data in perpetuity, and the originality of the data can be verified if tampering is suspected. In addition, we use prudent data storage strategies and a variety of cryptographic algorithms to guarantee user privacy; e.g., an adversary is unable to read the plain text even if the data are stolen. We implement a prototype of the DPS based on the real world blockchain-based platform Ethereum. Performance evaluation results demonstrate the effectiveness and efficiency of the proposed system.

An Empirical Study of Applying Artificial Neural Network for Classification of Dermatology Disease

Background/Objectives: With the growth in complexity and volume of medical data, an extensive set of information currently available in various forms related to diseases and its symptoms. Mechanisms are necessary to extract rules and patterns from these massive set of data. Identification and extraction of hidden patterns and rules in this massive data set certainly help us to understand about diseases progression facts. Methods: Machine learning provides an automatic way to uncover the patterns from data set and it will be helpful to health care professionals in order to provide precision medicine to their patients. Artificial Neural Network is a popular machine learning technique used for classification tasks in medical diagnosis for diseases detection. It is an eminent field of computer science which can be applied to the health care sector quite efficiently. In this study, Multi-Layer Feed Forward Neural Network has been applied to the dermatology dataset downloaded from UCI repository site to classify the dermatology diseases. Findings: Artificial Neural Network with back propagation algorithm produces the optimum results for classification and prediction problems. It also possesses the ability of generalization and applicable to real world problem. Applications: The experiment will be extended by applying on other types of diseases datasets and an automated diagnostic and advisory system with neural network integration definitely helps in diseases prediction problem.

A Robust Watermarking Algorithm of Encrypted Medical Volume Data Based on 3D DWT and 3D DCT

This work proposed a novel scheme for robust watermarking in the encrypted medical volume data, which protects the original medical volume data from the third party embedders. The original medical volume data is encrypted using a selective partial encryption method based on 3D DCT and Logistic Map. In the watermark embedding phase, the watermark is associated with the feature vector of the encrypted medical volume data, generating a key which will be used in watermark extraction. The feature vector of the encrypted medical volume data is extracted in a way combining 3D DWT and 3D DCT. The watermark extraction is performed on the encrypted domain. The proposed watermarking algorithm realizes zero-watermarking technology in the encrypted domain, which has no effects on the encrypted medical volume data. The experimental results show that the proposed watermarking scheme has good robustness to some normal and geometric attacks. Therefore, it has a good practical value.

English

English

Retraction Notice: 3D DWT-DCT and Logistic MAP Based Robust Watermarking for Medical Volume Data.

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

A New Zero-Watermarking Algorithm Resisting Attacks Based on Differences Hashing

Abstract Medical volume data containing patient information is often faced with various attacks in the transmission process. In order to enhance the medical information system security, and effectively solve the problem of medical volume data protection, a new zero-watermarking algorithm is proposed in the paper. The new zero-watermarking algorithm takes advantage of three-dimensional discrete wavelet transform multi-resolution analysis characteristics of space and time, threedimensional discrete cosine transform properties, and differences hashing robust characteristic. In order to enhance watermarking algorithm security, Legendre chaotic neural network is used for scrambling original watermark image. The medical volume data is made by three-dimensional discrete wavelet transform, threedimensional discrete cosine transform and three-dimensional discrete inverse cosine transform r to obtain the medical volume data feature matrix (4×5×4), which is converted to 64-bit binary feature sequence through difference hashing algorithm. The 64-bit binary feature sequence is used to construct the zero-watermarking. The experimental results prove that the new zero-watermarking has favorable security and robustness resisting various attacks. Therefore, the new zero-watermarking algorithm is more applicable to protect medical volume data.

Zero-watermarking Algorithm for Medical Volume Data Based on Difference Hashing

In order to protect the copyright of medical volume data, a new zerowatermarking algorithm for medical volume data is presented based on Legendre chaotic neural network and difference hashing in three-dimensional discrete cosine transform domain. It organically combines the Legendre chaotic neural network, three-dimensional discrete cosine transform and difference hashing, and becomes a kind of robust zero-watermarking algorithm. Firstly, a new kind of Legendre chaotic neural network is used to generate chaotic sequences, which causes the original watermarking image scrambling. Secondly, it uses three-dimensional discrete cosine transform to the original medical volume data, and the perception of the low frequency coefficient invariance in the three-dimensional discrete cosine transform domain is utilized to extract the first 4*5*4 coefficient in order to form characteristic matrix (16*5). Then, the difference hashing algorithm is used to extract a robust perceptual hashing value which is a binary sequence, with the length being 64-bit. Finally, the hashing value serves as the image features to construct the robust zero-watermarking. The results show that the algorithm can resist the attack, with good robustness and high security.

Zero-watermarking Algorithm for Medical Volume Data Based on Legendre Chaotic Neural Network and Perceptual Hashing

Medical information digitization makes the medical information storage and extraction more convenient. Medical image information security and copyright protection is also gradually being taken seriously, and some medical image watermarking has been applied. According to the characteristics of three-dimensional medical images, this paper proposes a robust zero-watermarking algorithm for medical volume data based on legendre chaotic neural network and perceptual hashing. The algorithm is based on three-dimensional discrete wavelet transform frequency analysis features, which uses perceptual hashing technique to extract medical volume data itself feature vector in order to structure robust zero watermarking. And using legendre chaotic neural network to generate chaotic sequence to enhance the security of the watermarking. The algorithm achieves a combination of legendre chaotic neural network encryption and zerowatermarking technology, which can improve the medical volume data watermarking algorithm security and robustness. The simulation results show the effectiveness of the algorithm.

Volume visualization based on the intensity and SUSAN transfer function spaces

Designing transfer functions is a challenging task for medical volume data visualization, especially when an arch of the same boundary disperses seriously and adjacent arches are intersected in the intensity and gradient magnitude (IGM) transfer function space. In this paper, a novel transfer function space is proposed to better highlight and differentiate different materials in realistic volume datasets. The proposed method combines the intensity values and three-dimensional (3D) SUSAN (Smallest Univalue Segment Assimilating Nucleus) edge responses of the original data to define the intensity and SUSAN (IS) transfer function space. The results of various datasets in volume rendering show that boundary of different materials exhibits a trapezoidal shape in the proposed IS space, and boundary information is much better brought out in comparison to the IGM space. Thus the IS space provides much more intuitive clues than the IGM space in order that transfer functions can be more easily designed. Meanwhile, more details of materials of interest are visible in the rendering images.

Supervoxels based Graph Cut for Medical Organ Segmentation

Organ segmentation is one of the most fundamental and challenging tasks in computer aid diagnosis system. Researches successfully working on interactive segmentation for medical image include graph cuts and random walk. However, graph cut-based organ segmentation for 3D medical volume data requires an optimization procedure of cutting the object/background regions on a very large-scale graph, which not only consumes large amount of memory and but also requires expensive computational cost. This paper conquers the drawbacks of graph cut-based organ segmentation via instead of voxel with supervoxel as nodes for constructing graph in interactive 3D organ segmentation that can greatly reduces node number and connected edges; voxels of medical data with similar intensity magnitude and near spatial relation are grouped into the supervoxels and such supervoxels are used as pseudo nodes of graph for cutting object (organ) and background regions, named as supervoxel-based graph cut. To validate the effectiveness and efficiency of the proposed method, we conduct experiments on 10 medical data, which possibly have tumors inside organ, or have abnormal deformed organ shape. The experimental results show that the proposed method is much superior than conventional graph cut-based method in term of accuracy, computational time and memory usage.

3D DWT-DCT and Logistic MAP Based Robust Watermarking for Medical Volume Data.

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.

Performance Analysis of Denoising in MR Images with Double Density Dual Tree Complex Wavelets, Curvelets and Non-subsampled Contourlet Transforms

Digital images are extensively used by the medical doctors during different stages of disease diagnosis and treatment process. In the medical field, noise occurs in an image during two phases: acquisition and transmission. During the acquisition phase, noise is induced into an image, due to manufacturing defects, improper functioning of internal components, minute component failures and manual handling errors of the electronic scanning devices such as PECT/SPECT, MRI/CT scanners. Nowadays, healthcare organizations are beginning to consider cloud computing solutions for managing and sharing huge volume of medical data. This leads to the possibility of transmitting different types of medical data including CT, MR images, patient details and much more information through internet. Due to the presence of noise in the transmission channel, some unwanted signals are added to the transmitted medical data. Image denoising algorithms are employed to reduce the unwanted modifications of the pixels in an image. In this paper, the performance of denoising methods with two dimensional transformations of nonsubsampled contourlets (NSCT), curvelets, double density dual tree complex wavelets (DD-DTCWT) are compared and analysed using the image quality measures such as peak signal to noise ratio, root mean square error, structural similarity index. In

A New Robust Zero-watermarking Algorithm for Medical Volume Data

Aiming at medical image information security problem, this paper proposes a new robust zero-watermarking algorithm for medical volume data based on three-dimensional discrete wavelet transform, three-dimensional Fourier transform and hermite chaotic neural network . Firstly, it employs a novel hermite chaotic neural network to generate the pseudo-random chaotic sequence for scrambling. Secondly, three dimensional medical image is transformed by three-dimensional discrete wavelet transform and three-dimensional discrete Fourier transform. Then, select the transformed low and intermediate frequency coefficients symbol as medical volume data characteristics to structure zero-watermarking. The algorithm integrates hermite chaotic neural network and zero-watermarking technology, which is not confined to artificially selected region of interest. The watermarking extraction does not need the original image. And its security depends on the chaotic sequence complexity and unpredictability, solving the watermark embedding, extraction safety and efficiency. The simulation results show that the algorithm is simple to implement, with good robustness, security and invisibility.

Medical Volume Data Zero-Watermarking Algorithm Using 3D-DCT and Chaotic Neural Network

This paper presented a new watermarking algorithm based on 3D-DCT and chaotic neural network in order to protect three-dimensional medical images. The algorithm adopts a wealth of information grayscale image as a watermark and three-dimensional medical image as the original carrier. It utilizes chaos neural network for scrambling and encryption of watermarking image. The embedded watermark has cryptographic security significance. Experimental results show that the algorithm is simple, which is a blind watermarking algorithm; the watermark extraction process does not require the original image. It is capable of resisting shear and filtering attacks.

Application of Chaotic Neural Network and 3D DWT-DFT in Medical Volume Data Watermarking

This paper proposes a digital watermarking method for the protection of medical volume data based on chaotic neural network and 3D DWT-DFT. In order to improve the security of digital watermarking, it uses chaotic sequence for scrambling .The chaotic sequence is generated by chaotic neural network. It makes full use of multi-level decomposition characteristics of 3D DWT-DFT, embedding watermark in the transform domain. The experimental results show that the method can resist filtering and rotation attacks, which has good robustness.