Design of an Information Security Service for Medical Artificial Intelligence

The medical convergence industry has gradually adopted ICT devices,which has led to legacy security problems related to ICT devices.However,it has been difficult to solve these problems due to data resource issues.Such problems can cause a lack of reliability in medical artificial intelligence services that utilize medical information.Therefore,to provide reliable services focused on security internalization,it is necessary to establish a medical convergence environment-oriented security management system.This study proposes the use of system identification and countermeasures to secure systemreliabilitywhen using medical convergence environment information in medical artificial intelligence.We checked the life cycle of medical information and the flow and location of information,analyzed the security threats that may arise during the life cycle,and proposed technical countermeasures to overcome such threats.We verified the proposed countermeasures through a survey of experts.Security requirements were defined based on the information life cycle in the medical convergence environment.We also designed technical countermeasures for use in the security management systems of hospitals of diverse sizes.

Exploration and Practice of Postgraduate Training Mode Based on Medical Imaging Information Security

In the process of continuous maturity and development of medical imaging diagnosis,it is common to transmit images through public networks.How to ensure the security of transmission,cultivate talents who combine medical imaging and information security,and explore and cultivate new discipline growth points are difficult problems and challenges for schools and educators.In order to cope with industrial changes,a new round of scientific and technological revolution,and the challenges of the further development of artificial intelligence in medicine,this article will analyze the existing problems in the training of postgraduates in medical imaging information security by combining the actual conditions and characteristics of universities,and put forward countermeasures and suggestions to promote the progress of technology in universities.

A method based on mutual information and gradient information for medical image registration

Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.

Fast Mutual Information Registration Method of 3-D Medical Image

Currently the voxel based registration methods have been used widely such as the well known mutual information (MI). Although the accuracy of their results is plausible, the registration procedure is slow. This paper proposed some methods to rigid registration based on mutual information, aiming for an acceleration of the registration process without significantly loss of precision in the final results. The efficiency of these methods is examined by registration of CT MR and PET MR. Experimental results show that the speedup is effective and efficient. By using the fast methods, the registration of 3 D medical image could also be implemented on PC rapidly.

Robust Image Registration Based on Mutual Information Measure

A new implementation of the image registration algorithm based on the mutual information is presented for the case of medical images. The registration is achieved if the maximum of the mutual information is attained. In this maximization process optimal values of five parameters of an affine transformation are searched.

MUTUAL INFORMATION BASED 3D NON-RIGID REGISTRATION OF CT/MR ABDOMEN IMAGES

A mutual information based 3D non-rigid registration approach was proposed for the registration of deformable CT/MR body abdomen images. The Parzen Windows Density Estimation (PWDE) method is adopted to calculate the mutual information between the two modals of CT and MRI abdomen images. By maximizing MI between the CT and MR volume images, the overlapping part of them reaches the biggest, which means that the two body images of CT and MR matches best to each other. Visible Human Project (VHP) Male abdomen CT and MRI Data are used as experimental data sets. The experimental results indicate that this approach of non-rigid 3D registration of CT/MR body abdominal images can be achieved effectively and automatically, without any prior processing procedures such as segmentation and feature extraction, but has a main drawback of very long computation time.

Using mutual information to evaluate performance of medical imaging systems

Information on physical image quality of medical images is important for imaging system assessment in order to promote and stimulate the development of state-of-the-art imaging systems. In this paper, we present a method for evaluating physical performance of medical imaging systems. In this method, mutual information (MI) which is a concept from information theory was used to measure combined properties of image noise and resolution of an imaging system. In our study, the MI was used as a measure to express the amount of information that an output image contains about an input object. The more the MI value provides, the better the image quality is. To validate the proposed method, computer simulations were per- formed to investigate the effects of noise and resolution degradation on the MI, followed by measuring and comparing the performance of two imaging systems. Our simulation and experimental results confirmed that the combined effect of deteriorated blur and noise on the images can be measured and analyzed using the MI metric. The results demonstrate the potential usefulness of the proposed method for evaluating physical quality of medical imaging systems.

Trust and Security on Semantic HIM (Health Information Management)

Information technology have changed information media by networking and internet using technology in health as same as another part improve efficiency and effectiveness. Currently, the medical document is reality-based medicine, so that is the most important, richest and the most realistic source of medical and health information. Health information management systems that require systems to the storage, retrieval, storage and elimination of health records (by law), and adjust to the rules of professional. These processes are difficult and time consuming for human. In the meantime semantic HIM seem best solution.

Study of three-dimensional PET and MR image registration based on higher-order mutual information

Mutual information has currently been one of the most intensivelyresearched measures. It has been proven to be accurate and effective registrationmeasure. Despite the general promising results, mutual information sometimes mightlead to misregistration because of neglecting spatial information and treating intensityvariations with undue sensitivity. In this paper, an extension of mutual informationframework was proposed in which higher-order spatial information regarding imagestructures was incorporated into the registration processing of PET and MR. Thesecond-order estimate of mutual information algorithm was applied to the registrationof seven patients. Evaluation from Vanderbilt University and our visual inspectionshowed that sub-voxel accuracy and robust results were achieved in all cases withsecond-order mutual information as the similarity measure and with Powell's multi-dimensional direction set method as optimization strategy.

LGNet:Local and global representation learning for fast biomedical image segmentation

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many challenges.Building on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous progress.However,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global context.To address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper.Specifically,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features.LGNet has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from convolutions.Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and MOST.Specifically,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on MOST.Meanwhile,the inference speed attains at 172 frames per second with 224-224 input resolution.The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.

MEDICAL IMAGE SEGMENTATION BASED ON A MODIFIED LEVEL SET ALGORITHM

Objective To present a novel modified level set algorithm for medical image segmentation. Methods The algorithm is developed by substituting the speed function of level set algorithm with the region and gradient information of the image instead of the conventional gradient information. This new algorithm has been tested by a series of different modality medical images. Results We present various examples and also evaluate and compare the performance of our method with the classical level set method on weak boundaries and noisy images. Conclusion Experimental results show the proposed algorithm is effective and robust.

On Hiding Secret Information in Medium Frequency DCT Components Using Least Significant Bits Steganography

This work presents a new method of data hiding in digital images,in discrete cosine transform domain.The proposed method uses the least significant bits of the medium frequency components of the cover image for hiding the secret information,while the low and high frequency coefficients are kept unaltered.The unaltered low frequency DCT coefficients preserves the quality of the smooth region of the cover image,while no changes in the high DCT coefficient preserve the quality of the edges.As the medium frequency components have less contribution towards energy and image details,so the modification of these coefficients for data hiding results in high quality stego images.The distortion due to the changes in the medium frequency coefficients is insignificant to be detected by the human visual system.The proposed methods demonstrated a hiding capacity of 43:11%with the stego image quality of a peak signal to the noise ration of 36:3 dB,which is significantly higher than the threshold of 30 dB for a stego image quality.The proposed technique is immune to steganalysis and has proved to be highly secured against both spatial and DCT domain steganalysis techniques.

Content-based retrieval based on binary vectors for 2-D medical images

In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...

基于OCR和Pydicom的PACS数据库数据丢失后的应急与恢复研究

目的在影像归档和通信系统(Picture Archiving and Communication System,PACS)数据库文件丢失或损坏后,实现影像资料和PDF报告关键信息的快速识别和重组,供患者回诊使用。方法利用基于深度学习的光学字符识别技术和Pydicom技术分别读取PDF和DCOM文件中的基本信息,重新建立起患者、影像、报告三者之间的联系,并将关联数据写入数据库。结果经抽样验证,该方法识别同类图像精度的准确度、精准度及召回率均为100%,综合指标F1值为1,在不同组别独立样本间的识别精度表现出一致性。平均每份报告识别时间约为0.14 s(t=-1.005,P=0.315),说明不同组别独立样本间的识别时间表现出一致性。结论该方法的使用能有效缩短数据库故障后患者等待时长,能够在短时间内恢复医疗秩序,可用于PACS数据库数据丢失后的应急处置,也为PACS的数据整合提供依据,为医学影像数据恢复和数据整合提供一种新思路。

Medical Image Retrieval Based on Multi-Layer Resampling Template

Medical image application in clinical diagnosis and treatment is becoming more and more widely, How to use a large number of images in the image management system and it is a very important issue how to assist doctors to analyze and diagnose. This paper studies the medical image retrieval based on multi-layer resampling template under the thought of the wavelet decomposition, the image retrieval method consists of two retrieval process which is coarse and fine retrieval. Coarse retrieval process is the medical image retrieval process based on the image contour features. Fine retrieval process is the medical image retrieval process based on multi-layer resampling template, a multi-layer sampling operator is employed to extract image resampling images each layer, then these resampling images are retrieved step by step to finish the process from coarse to fine retrieval.

基于PACS/RIS系统影像科信息化质量控制与管理的实践研究

目的:探索基于影像归档和通信系统(PACS)/放射信息管理系统(RIS)构建的影像科医疗质量信息化管理模式在影像科医疗质量控制与管理中的效果。方法:选取暨南大学附属顺德医院2022年1月—12月收治的1800例患者作为实施前的对照组,2023年1月—12月收治的1800例患者作为实施后的观察组。而后根据检查类型将两组患者均匀划分为数字化放射成像(DR)组、CT组和MRI组。同时,患者根据就诊的紧急程度及方式进一步分为急诊、门诊和住院3类(DR检查急诊400例、门诊400例、住院400例,CT检查急诊400例、门诊400例、住院400例,MRI检查急诊400例、门诊400例、住院400例)。对照组使用传统质量控制模式,观察组实施基于PACS/RIS系统的信息化质量控制管理模式。观察患者从登记到检查的时间、检查到报告审核的时间、图像检查质量及报告诊断质量。结果:实施PACS/RIS系统信息化质量控制管理模式的观察组患者从前台登记到影像检查时间、检查完成到报告审核的时间均短于实施传统质量控制评价模式的对照组,差异有统计学意义(P<0.05);但两组MRI住院患者检查到报告审核时间比较,差异无统计学意义(P>0.05)。实施PACS/RIS系统信息化质量评价体系的观察组的图像质量、报告质量均优于实施传统质量控制评价模式的对照组,差异有统计学意义(P<0.05)。结论:应用基于PACS/RIS系统的影像科医疗质量信息化管理模式进行质量控制,能够有效缩短影像科检查时间和报告时间,提高图像和报告的质量,有助于构建结构合理、高效的影像医疗质量管理体系,为患者提供更加优质高效的医疗服务。

PACS与HIS/RIS集成方法的研究

介绍了PACS和HIS/RIS所管理的信息对象及遵循的标准 ,详细阐述了PACS与HIS/RIS之间的集成方法 ,并结合实例说明了如何用DICOM标准实现它们之间的无缝连接。

PACS的应用现状与未来发展分析

目的:分析国内影像归档和通信系统(picture archiving and communication systems,PACS)的应用现状及存在问题,为PACS未来的设计和发展提供技术支持和依据。方法:回顾2015年6月至2016年5月某院介入诊疗科PACS运行情况,结合全院实际情况系统分析国内PACS的应用现状及问题。结果:介入诊疗科PACS运行存在问题较多,影像采集设备使用时间跨度长达13 a,6.8%的工作站、14.2%的影像采集设备未接入PACS,4.1%的患者、2.9%的影像未传至PACS。结论:国内PACS设计要适合未来发展预期,运行要稳定、高效、安全、可靠,应用要满足未来医疗、科研、教学需求,为PACS在互联网医疗时代奠定坚实基础。

DICOM技术在PACS系统中的应用综述

PACS是当代医院信息化高速发展的重要环节,而DICOM又是PACS中医学图像格式和传输医学图像协议的标准。阐述基于DICOM标准的PACS系统的国内外发展现状,并对PACS系统按其应用分类进行了详细阐述,并分类研究了DICOM在PACS中的应用,同时论述了DICOM在PACS中的长远展望。

PACS系统在医学影像中的应用

PACS系统指图像存档及通信系统,解决了影像科传统的保存胶片、保存病人信息的弊端,提供了一个信息化管理平台,方便了临床医生阅片、比较病人病情,提高了诊断效率及诊断水平,促进了医院医疗水平的发展。