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Medical data on demand with WebMIA
Abstract
This article focuses on the creation of a system for managing and archiving large-scale medical data. Specifically, this case study demonstrates the new paradigm through the prototype system WebMIA. The system enables storage, maintenance, sharing, updating, and retrieval of medical files based on the existing Internet facilities and the state-of-the-art literature in medical image indexing and multimedia information presentation. It also incorporates our current research in image retrieval and multimodal medical image fusion. It surpasses the existing systems in the commercial and research sectors as it provides convenient, efficient, effective, and flexible online processing, annotation, and multimodal querying capabilities in one forum. The patient confidentiality issue is well addressed when the sharing of the medical information is provided. This technology will assist medical practitioners/researchers by enabling efficient management and sharing of medical files within or across a community, without being subject to geographical restrictions an d without creating the problems of inconsistent and fragmented medical data.
... As the population of the world is growing quickly, and as new medical diagnostic technologies are developed, the traditional archival system produces several major problems in accommodating the increased demand. Consequently, more and more physical space will become necessary to store medical files, and it will become increasingly less efficient and more difficult to access and retrieve a particular medical file (Zhang 2005). In recent years, there has been an evolution of technology access and storage, and especially with increased legal obligations. ...
... With the rapid development of computer and network technologies, it is possible to share and access to data on grid storage devices. In (Zhang 2005), a web-based medical information archive system enables storage, maintenance, sharing, updating, and retrieval of medical data based on the existing Internet facilities. ...
Telemedicine is the process by which electronic, visual and audio communications are used to support practitioners at remote sites with diagnosis and consultation procedures, such as remote clinical ex-aminations and medical image transfers. Telemedicine is legally regu-lated by laws and constraints regarding the access of data contained in Personal Medical Files. These requirements are given by international entities such as IHE. They often specify the required functionalities to meet requirements of information exchange in the medical field. These functionalities are presented in this paper and outline some technical examples for their implementation in each level: authentication, commu-nication, data storage, tracking and patient identification.
... Information Medicine is one of the medical applications that have been studied for decades to utilize our great efforts of mankind on the Internet to provide every life an opportunity to live longer and healthier. Medical researchers have applied Internet Technology into various design aspects starting from [12] originated from the [13] project, to the designs of [11] and [21] equipping medical devices and their results to illustrate them on the Internet while WebMIA in [22] and grid servers of DM2 using DICOM in [5] provided medical image processing on the Internet. With many design aspects, the telemedicine has been applied with mobile technology in [4] to support three cores of Naval Telemedicine – hospitals, Fleet Naval Consultation and Diagnostic Centers and hospital ships, [15] using PDA to support online medical information management, [16] to support many sport events located in rural area in Japan and [14] and [9] for medical education. ...
Telemedicine has been researched to provide better care to people in the remote area via Internet. Enhancing the Telemedicine over Internet can be applied to multiple health-care agencies containing medical knowledge in distributed databases. We are currently developing an internet-based medical diagnosis system, called Medical Knowledge Technology (MKT) system with confidence levels during coordinately diagnosis by multiple medical experts to support both medical experts and patients for early diagnosis and in researches. In this paper, methodologies of medical diagnosis among locally, regionally, nationally, and globally distributed nodes of MKT system are explained with their medical knowledge structures and experimental results on real-time simulation. In addition, the formulae for finding confidence levels for medical diagnosis are presented step-by-step according to the way that a physician diagnoses a patient.
In this chapter, we propose a new indexing approach on medical "image scanner" databases combining the analysis process of the texture characteristics with the descriptive information. The proposed model is based on the digital image components using the characteristics vector. This vector represents the morphological processing result on image texture. It is linked to image semantic attributes using the annotations of medical professionals. Our application context is based on "Mammographic Image Analysis" (MIAS) in databases. The first aspect concerning the morphology processing on images called the "numerical signature" vector. In this approach, the texture analysis of the image is based on the Gabor Wavelets (or Filters) Theory. In offline processing for each image in MIAS databases, the Gabor Wavelets determine all numerical signatures: image characteristics as multi-index vectors. In online, the query processing by image in real-time defines the query signature (or image-query vectors) and determines all similarities by multi-index matching with images in databases. The similarities are built between the image-query and images in MIAS databases using the same Gabors' algorithms implementation. In order to evaluate the robustness of our system (based on multi-index, semantic attributes, query and information retrieval by image), we experiment with a controlled database of 320 mammographies. The efficient results show a set of successful criteria in image representations based on the Gabor's Wavelets, semantic attributes and significant ratios in the system recall and precision. The objective is to design an intelligent application to assist medical professionals in the decision-making on tumor dignosis based on mammography scanner.
The Internet has been widely used for various purposes while the telemedicine has been researched to provide better care to people in the remote area. The combination of two concepts can be used with multiple health-care agencies containing medical knowledge in distributed databases. We are currently developing an Internet-based medical diagnosis system, called Medical Knowledge Technology (MKT) system to support both medical experts and patients in the way that multiple medical experts coordinately diagnose the illnesses for a patient with confidence levels. The goal of MKT system is to support world wide, especially in the rural areas that lack of medical teams but have an Internet access
This work is about Content Based Image Retrieval (CBIR), focusing on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region based approach to improving the retrieval effectiveness and robustness (2) development of a new hierarchical indexing structure and the corresponding Hierarchical, Elimination-based A* Retrieval algorithm (HEAR) to significantly improve the retrieval efficiency without sacrificing the retrieval effectiveness; it is shown that HEAR is guaranteed to deliver a logarithm search in the average case (3) employment of user relevance feedbacks to tailor the semantic retrieval to each user's individualized query preference through the novel Indexing Tree Pruning (ITP) and Adaptive Region Weight Updating (ARWU) algorithms. Theoretical analysis and experimental evaluations show that FAST methodology holds a great promise in delivering fast and semantics-tailored image retrieval in CBIR.
CoMed is a prototype of a real-time collaborative medicine system that allows medical specialists to share patient records and to communicate with each other on the Internet. CoMed consists of a multimedia medical database containing relevant information about laryngeal diseases and a real-time collaboration system including a teleconferencing system, a whiteboard and a chatting system. CoMed is web-based. We adopted the object database O2 and CORBA technologies for the multimedia medical database. Therefore, our system can provide the flexibility, extensibility and location transparency of patient databases. We developed a SeeYou Active X control for the teleconferencing system and a Java applet for the whiteboard and chatting system. CoMed improves the efficiency of the overall system by separating the servers on a UNIX machine and a Windows NT machine. CoMed can be utilized for stand-alone research, for collaborative consultations among medical specialists and for a telemedicine in participation with the patients and medical specialists. Our system can be extended easily into other types of the collaborative systems, such as collaborative distance learning, collaborative science system, etc.
The motivation for our work is to develop a truly semantic-based image retrieval system that can discriminate between images differing only through subtle, domain-specific cues, which is a characteristic feature of many medical images. We propose a novel image retrieval framework centered around classification driven search for a good similarity metric (image index features) based on the image semantics rather than on appearance. Given a semantically well-defined image set, we argue that image classification and image retrieval share fundamentally the same goal. Thus, the distance metric defining a classifier which performs well on the data should be expected to behave well when used as the similarity metric for semantic image retrieval. In this paper we shall report our methodology and results on 3D grey-level medical image retrieval. 1 1 Introduction 1.1 Motivation On-line image data is expanding rapidly in quantity, content and dimension. Many medical Picture Archiving and Comm...
We present both a high-level symbolic model of the human brain, and a method of using this model to aid in the recognition of objects from medical images. The model is stored as a frame based semantic network consisting of three coexisting graphs (a spatial adjacency graph; a part hierarchy; and an inheritance graph). We propose a method similar to assumption-based truth maintenance systems for the collating and reasoning processes required in the labelling of input images.
We present both a high-level symbolic model of the human brain, and a method of using this model to aid in the recognition of objects from medical images. The model is stored as a frame-based semantic network consisting of three coexisting graphs (a spatial adjacency graph, a part hierarchy and an inheritance graph). We propose a method similar to assumption-based truth maintenance systems for the collating and reasoning processes required in the labelling of input images.
The use of color information for image retrievalhas been used widely in many content-basedretrievalsystem with some success. However, histogram-basedcolor retrieval techniques su#er from a lack of importantspatial knowledge. We discuss a technique ofintegrating color information with spatial knowledgeto obtain an overall impression of the image. Thetechnique involves three steps: the selection of a setof representative colors, the analysis of spatial informationof the selectedcolors,...
Despite generally good performance, mutual information has also been shown by several researchers to lack robustness for certain registration problems. A possible cause may be the absence of spatial information in the measure. The present paper proposes to include spatial information by combining mutual information with a term based on the image gradient of the images to be registered. The gradient term not only seeks to align locations of high gradient magnitude, but also aims for a similar orientation of the gradients at these locations.
Results of combining both standard mutual information as well a normalized measure are presented for rigid registration of three-dimensional clinical images (MR, CT and PET). The results indicate that the combined measures yield a better registration function than mutual information or normalized mutual information per se. The accuracy of the combined measures is compared against a screw marker based gold standard, revealing a similar accuracy for the combined measures to that of the standard measures. Experiments into the robustness of the measures with respect to starting position imply a clear improvement in robustness for the measures including spatial information.
A comparison of six similarity measures for use in intensity based 2D-3D image registration is presented. The accuracy of
the similarity measures are compared to a “gold-standard” registration which has been accurately calculated using fiducial
markers. The similarity measures are used to register a CT scan to a fluoroscopy image of a spine phantom. The registration
is carried out within a region of interest in the fluoroscopy image which is user defined to contain a single vertebra. Many
of the problems involved in this type of registration are caused by features which were not modelled by a phantom image alone.
More realistic “gold standard” data sets were simulated using the phantom image with clinical image features overlaid. Results
show that the introduction of soft tissue structures and interventional instruments into the phantom image can have a large
effect on the performance of some similarity measures previously applied to 2D-3D image registration. Two measures were able
to register accurately and robustly even when soft tissue structures and interventional instruments were present as differences
between the images. These measures are called pattern intensity and gradient difference.
Though numerous multimedia systems exist in the commercial market today, relatively little work has been done on developing the mathematical foundation of multimedia technology. We attempt to take some initial steps towards the development of a theoretical basis for a multimedia information system. To do so, we develop the motion of a structured multimedia database system. We begin by defining a mathematical model of a media-instance. A media-instance may be thought of as “glue” residing on top of a specific physical media-representation (such as video, audio, documents, etc). Using this “glue”, it is possible to define a general purpose logical query language to query multimedia data. This glue consists of a set of “states” (e.g., video frames, audio tracks, etc.) and “features”, together with relationships between states and/or features. A structured multimedia database system imposes a certain mathematical structures on the set of features/states. Using this notion of a structure, we
A comparison of six similarity measures for use in intensity-based two-dimensional-three-dimensional (2-D-3-D) image registration is presented. The accuracy of the similarity measures are compared to a "gold-standard" registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a computed tomography (CT) scan of a spine phantom to a fluoroscopy image of the phantom. The registration is carried out within a region-of-interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modeled by a phantom image alone. More realistic "gold-standard" data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft-tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2-D-3-D image registration. Two measures were able to register accurately and robustly even when soft-tissue structures and interventional instruments were present as differences between the images. These measures were pattern intensity and gradient difference. Their registration accuracy, for all the rigid-body parameters except for the source to film translation, was within a root-mean-square (rms) error of 0.54 mm or degrees to the "gold-standard" values. No failures occurred while registering using these measures.
This paper applies and evaluates an automatic mutual information-based registration algorithm across a broad spectrum of multimodal volume data sets. The algorithm requires little or no pre-processing, minimal user input and easily implements either affine, i.e. linear or thin-plate spline (TPS) warped registrations. We have evaluated the algorithm in phantom studies as well as in selected cases where few other algorithms could perform as well, if at all, to demonstrate the value of this new method. Pairs of multimodal gray-scale volume data sets were registered by iteratively changing registration parameters to maximize mutual information. Quantitative registration errors were assessed in registrations of a thorax phantom using PET/CT and in the National Library of Medicine's Visible Male using MRI T2-/T1-weighted acquisitions. Registrations of diverse clinical data sets were demonstrated including rotate-translate mapping of PET/MRI brain scans with significant missing data, full affine mapping of thoracic PET/CT and rotate-translate mapping of abdominal SPECT/CT. A five-point thin-plate spline (TPS) warped registration of thoracic PET/CT is also demonstrated. The registration algorithm converged in times ranging between 3.5 and 31 min for affine clinical registrations and 57 min for TPS warping. Mean error vector lengths for rotate-translate registrations were measured to be subvoxel in phantoms. More importantly the rotate-translate algorithm performs well even with missing data. The demonstrated clinical fusions are qualitatively excellent at all levels. We conclude that such automatic, rapid, robust algorithms significantly increase the likelihood that multimodality registrations will be routinely used to aid clinical diagnoses and post-therapeutic assessment in the near future.
Mutual information has developed into an accurate measure for rigid and affine monomodality and multimodality image registration. The robustness of the measure is questionable, however. A possible reason for this is the absence of spatial information in the measure. The present paper proposes to include spatial information by combining mutual information with a term based on the image gradient of the images to be registered. The gradient term not only seeks to align locations of high gradient magnitude, but also aims for a similar orientation of the gradients at these locations. Results of combining both standard mutual information as well as a normalized measure are presented for rigid registration of three-dimensional clinical images [magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET)]. The results indicate that the combined measures yield a better registration function does mutual information or normalized mutual information per se. The registration functions are less sensitive to low sampling resolution, do not contain incorrect global maxima that are sometimes found in the mutual information function, and interpolation-induced local minima can be reduced. These characteristics yield the promise of more robust registration measures. The accuracy of the combined measures is similar to that of mutual information-based methods.
This paper addresses the multimodal medical image fusion problem. To deliver the expected fusion accuracy, most of the state-of-the-art fusion algorithms have the typical requirement - a set of fiducial points between the two modalities of the images to "guide" the fusion. This paper aims at removing this requirement, resulting in an "automatic" multimodal medical image fusion methodology based on an innovative model using ElectroStatic Equilibrium theory called ESE that can be used in clinical diagnoses and evaluations with accepted fusion accuracy. By "automatic", it is meant that the fusion algorithm per se does not require fiducial points; it does require a certain form of human interactions in terms of providing users a list of parameter settings at the beginning of the fusion, that are case-based, anatomy-based, and image-modality-based, and it does even allow users to have an option to change the specific values of the parameter settings to accommodate specific clinical needs. This "automatic" approach allows radiologists to save their time/effort in identifying and marking the fiducial points in the images, allows physicians and radiologists to apply their domain expertise more intelligently in "playing with" different parameter settings in a higher level when running this algorithm in diagnoses, and also allows patients to avoid the inconvenience to be placed under the fiducial markings. Preliminary evaluations against one of the existing fusion methods have shown that ESE holds a great promise in future medical applications.
Multimedia information management and communications systems for medical applications have been undergoing design and development. The aim of these activities has been the development of distributed computer systems providing storage, processing, and communication services required by the medical community. One of the main critical issues of such systems is the handling of multimedia information (i.e., text, images, graphics, and voice) in a uniform way and the fast access to images through the network. We present the design architecture and concepts used in our multimedia database system, along with its performance evaluation. The evaluation focuses on the response times of a multimedia medical database server, as seen by remote workstations for various retrieval and image transfer requests. For this purpose, a queueing network model was developed and simulated. Communications between the workstations and the database server are governed by the client-server model using TCP/IP and appropriate application protocols on a 10 Mb/s Ethernet LAN.<
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Demonstration of accuracy and clinical ver-satility of mutual information warped geometric deformations
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Multimedia patient database: Integrating endoscopic data and images
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multimedia patient database: integrating endoscopic data and images
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