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Medical Image Intelligent Access Integrated with Electronic Medical Records System for Brain Degenerative Disease

In book: Data Storage
Source: InTech

ABSTRACT In this study, the intelligent information access framework for medical image databases was designed to integrate radiological reports and clinical information. The most important concern in this approach is the interdisciplinary collaboration among neurology, medical informatics and radiology experts. The second important concern would be the implementation with the critical and service-oriented hospital information system. Therefore, we will test the system by the physician-in-the-loop approach to enhance diagnosis practically and revise the system. Moreover, we focused on decision support for dementia diagnosis, teaching and research. Therefore, we would retrieve images of similar patients via a medical grid by querying keywords of the base information, clinical History, clinical diagnose, Lab. we get the new retrieved image database for the patients. Therefore, we will analysis their medical image correlation by image processing and the relevance of the clinical data and images in order to assist diagnosis, research and teaching. Security and privacy is also a very important issue in this field of research. First, we build trusted electronic relationships between healthcare customers, employees, businesses, trading partners and stakeholders. Therefore, when we use the patient's anamnesis, examination data, or medical images, whether we have the patient's permission or not, there must be a set of procedures to follow accordingly. We plan to consider the security and privacy function in the system, in the future. The implementation of this prototyping system must be well organized and the initial testing done on an offline system. The clinical data could be backed-up and copied to a

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    ABSTRACT: This paper presents the SRIS-HC-an Patient Electronic Medical Record System with support for Content-based Image Retrieval, developed aiming at demonstrating the benefits of having the ability of similarity retrieval over image datasets based on their contents, at the Clinical Hospital of the Medical School of Ribeirao Preto of the University of Sao Paulo at Ribeirao Preto-Brazil (the HCFMRP/USP). This ability is an additional resource developed over a PACS system, intending to enable the improvement of medical diagnosis by images, as well as to provide a basis to perform analysis of similar medical cases and bibliographic research over them. The SRIS-HC was developed on top of the Radiology Information System (RIS) of the Radio-diagnosis Laboratory of the HCFMRP/USP, which is in turn at the core of the Electronic Patient Record System of the hospital.
    16th IEEE Symposium on Computer-Based Medical Systems (CBMS 2003), 26-27 June 2003, New York, NY, USA; 01/2003
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    ABSTRACT: In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.
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    ABSTRACT: This paper presents a new picture archiving and communication system (PACS), called cbPACS (content-based PACS), which has content-based image retrieval resources. cbPACS answers similarity (range and nearest-neighbor) queries, taking advantage of a metric access method embedded into the image database manager. The images are compared via their features, which are extracted by an image processing system module. The system works on features based on the color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant with regard to scale, translation and rotation of images and also to brightness transformations. cbPACS is prepared to integrate new image features, based on the texture and shape of the main objects in the image.
    15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002), 4-7 June 2002, Maribor, Slovenia; 01/2002

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May 20, 2014