基于Snake模型的医学图像分割

基于Snake模型的图像分割技术是近年来图像处理领域的研究热点之一。Snake模型承载上层先验知识并融合了图像的底层特征,针对医学图像的特殊性,能有效地应用于医学图像的分割中。本文对各种基于Snake模型的改进算法和进化模型进行了研究,并重点梳理了最新的研究成果,以利于把握基于Snake模型的医学图像分割方法的脉络和发展方向。     

医学图像融合配准技术

图像融合技术在现代医学中扮演着极其重要的角色,是现代医学图像技术研究的重点。图像融合技术中,图像的配准又是其中的重点、难点和热点。本文按照图像变换特性对图像配准进行了分类,对每个类别的不同配准方法(特征点的获取、图像配准的变换等)进行介绍。但是,图像配准是一个尚处在发展阶段的学科,实现配准的精确化、快速化、自动化仍需要进一步的努力。     

基于VTK的医学图像三维可视化系统

医学图像的三维可视化可以通过可视化工具包（VTK）提供的API实现。VTK是医学图像可视化的开法工具包,它把可视化的算法封装起来,利用简单的代码生成所需图形。基于VTK的医学图像三维可视化系统阐述了如何借助VTKAPI读入二维医学图像序列、操作二维图像、重建三维图像以及进行三维图像可视化的全套方案,为临床医生的诊断、治疗提供了有益的途径。     

医用PACS系统及其主要技术问题

医用图像归档与通信系统(简称PACS)是近年来医学图像处理的一个研究热点。本文介绍了这一系统的基本功能、系统构成及其几个主要技术问题。     

人工智能大模型医学应用研究

近年来,以自然语言处理和视频图像分析为主的人工智能大模型技术得到快速发展,其基本特征是聚焦相关应用领域的共性需求,通过大数据、强算力和复杂算法的高效协同与深度融合,构建通用预训练模型,广泛适配下游任务,有力提高模型的处理性能与研发效率.因此,大模型技术为医学人工智能高质量发展提供了难得契机.本文通过全面梳理国内外大模型的研究进展、关键技术与核心算法,分析总结生物医学领域一系列标准数据集和预训练模型的发展特点,结合医学人工智能的研发实践,深入剖析医学领域大模型构建的应用需求、解决思路与研发经验,助力推动医学大模型创新发展.     

基于小波分析的医学图像的处理

医学图像的好坏直接影响着医生对病情的诊断和治疗,因此利用数字图像处理等技术对医学图像进行有效的处理,已成为医学图像处理研究和开发的一大热点.小波分析是对傅立叶变换的继承和发展,在医学影像领域有着广阔的应用前景.介绍了二维离散小波变换的一般形式,在图像分解的基础上,利用小波分析对医学图像进行去噪和增强处理,能够有效的改善图像质量,有利于医生对病情的诊断和治疗.     

基于PET医学图像边缘检测算法的研究

目的:边缘检测在图像处理中至关重要,可被广泛应用于目标区域识别、区域形状检测、图像分割等图像分析领域。边缘是图像中不平稳现象和不规则结构的重要表现,往往携带着图像中的大量信息,并给出图像轮廓。在医学图像三维显示技术中,为了更精确的临床判别需要得到单像素的清晰轮廓,因此我们提出一种新的边缘检测算法。方法:在传统的小波边缘检测的基础上,提出了一种新的边缘算法,即基于小波极大值边缘检测算法,应用模糊算法构造相应的隶属函数,再对得到的极大值进一步筛选。结果:将该算法应用到医学图像中,最终可以得到较清楚的单像素边缘轮廓,实验结果证明了该算法的可行性。结论:运用这种算法处理过的医学图像边缘锐化更好,更清晰,能够为肿瘤的早期识别提供依据,满足医学影像识别的需要。     

基于小波变换的医学图像编码方法

高分辨率的医学图像具有很大的信息量,影响了整个数字化的远程医疗系统的实时性,因此必须在保证不丢失关键诊断信息的前提下,对医学图像进行必要的压缩。本文提出了在给定小波基下,基于二维小波分解和重构的快速压缩方法。该方法使用了向量量化技术并采用LBG算法设计码本。实验结果证明,采用该方法可获得较高的压缩比和符合诊断要求的压缩图像。     

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.     

探讨医学图像库的新进展

随着数字化医疗设备如CT、MR、DSA、DR在临床医学诊断中的大量采用,以及计算机技术在医院的广泛应用,医学影像数据正在里海量增长,现有的存储、图像处理及管理方式面临着巨大的挑战。网络医学图像库的出现,给医学图像库发展提供了条件和基础。而计算机网络的发展趋势是：开放、集成、高性能和智能化。它们渗透到网络自身、网络服务和网络应用的各个层次。基于医院现有影像数据系统,建立网络应用环境,来满足医学图像库发展的需要,为解决数字医学图像库所面临的问题提供一条很好的技术思路。     

小波变换及其在医学图像处理中的应用

医学图像的好坏直接影响着医生对病情的诊断和治疗,因此利用数字图像处理等技术对医学图像进行有效的处理,已成为医学图像处理研究和开发的一大热点。小波变换是对傅里叶变换的继承和发展,在医学影像领域有着广泛的应用前景。本文介绍了二维离散小渡变换的一般形式,在图像分解与重构的基础上．系统地阐述了利用小小组变换的时频域特性与多分辨分析对医学图像进行去噪、增强以及边缘提取等深层次的处理,有效的改善图像质量。     

An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion

Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation.By two-scale image decomposition,the input image is decomposed into base layer and detail layer.For the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge information.Besides,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization problems.For the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain.The sum of the above base and detail layers is as the final fused image.Experimental results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.     

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

The vast amount of data produced by today’s medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field.     

Registration methods in radiotherapy

PurposeThe aim of this study is to present a short and comprehensive review of the methods of medical image registration, their conditions and applications in radiotherapy. A particular focus was placed on the methods of deformable image registration.MethodsTo structure and deepen the knowledge on medical image registration in radiotherapy, a medical literature analysis was made using the Google Scholar browser and the medical database of the PubMed library.ResultsChronological review of image registration methods in radiotherapy based on 34 selected articles. A particular attention was given to show: (i) potential regions of the application of different methods of registration, (ii) mathematical basis of the deformable methods and (iii) the methods of quality control for the registration process.ConclusionsThe primary aim of the medical image registration process is to connect the contents of images. What we want to achieve is a complementary or extended knowledge that can be used for more precise localisation of pathogenic lesions and continuous improvement of patient treatment. Therefore, the choice of imaging mode is dependent on the type of clinical study. It is impossible to visualise all anatomical details or functional changes using a single modality machine. Therefore, fusion of various modality images is of great clinical relevance. A natural problem in analysing the fusion of medical images is geographical errors related to displacement. The registered images are performed not at the same time and, very often, at different respiratory phases.     

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.     

Artificial intelligence and machine learning for medical imaging: A technology review

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.