PERANCANGAN ARSITEKTUR XML DBMS UNTUK CITRA MEDIS FORMAT SCALABLE VECTOR GRAPHICS HASIL RONTGEN

SVG medical Image formats, conducive for image merge with the image information into one file. SVG is image type with XML format, which text document format, so that using text editor in general can do change or expurgation. A document has to be arranged so that seeking and depository data return easily. Problems, information can be stored in a file so that easy to change and retrieve data. This article study concerning how to develop; build a architecture of XML DBMS for the sake of record-keeping of processing and denominating of data return medical image result of Roentgen

Web-based manipulation of multiresolution micro-CT images

Micro Computed-Tomography (mu-CT) scanning is opening a new world for medical researchers. Scientific data of several tens of gigabytes per image is created and usually requires storage on a common server such as Picture Archiving and Communication Systems (PACS). Previewing this data online in a meaningful way is an essential part of these systems. Radiologists who have been working with CT data for a long time are commonly looking at two-dimensional slices of 3D image stacks. Conventional web-viewers such as Google Maps and Deep Zoom use tiled multiresolution-images for faster display of large 2D data. In the medical area this approach is being adapted for high resolution 2D images. Solutions that include basic image processing still rely on browser external solutions and high-performance client-machines. In this paper we optimized and modified Brain Maps API to create an interactive orthogonal-sectioning image viewer for medical mu-CT scans, based on JavaScript and HTML5. We show that tiling of images reduces the processing time by a factor of two. Different file formats are compared regarding their quality and time to display. As well a sample end-to-end application demonstrates the feasibility of this solution for custom made image acquisition systems

Dynamic Selection of Symmetric Key Cryptographic Algorithms for Securing Data Based on Various Parameters

Most of the information is in the form of electronic data. A lot of electronic data exchanged takes place through computer applications. Therefore information exchange through these applications needs to be secure. Different cryptographic algorithms are usually used to address these security concerns. However, along with security there are other factors that need to be considered for practical implementation of different cryptographic algorithms like implementation cost and performance. This paper provides comparative analysis of time taken for encryption by seven symmetric key cryptographic algorithms (AES, DES, Triple DES, RC2, Skipjack, Blowfish and RC4) with variation of parameters like different data types, data density, data size and key sizes.Comment: 8 pages, 4 figures, Fifth International Conference on Communications Security & Information Assurance (CSIA 2014) May 24~25, 2014, Delhi, Indi

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

Community standards for open cell migration data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration

Grid Databases for Shared Image Analysis in the MammoGrid Project

The MammoGrid project aims to prove that Grid infrastructures can be used for collaborative clinical analysis of database-resident but geographically distributed medical images. This requires: a) the provision of a clinician-facing front-end workstation and b) the ability to service real-world clinician queries across a distributed and federated database. The MammoGrid project will prove the viability of the Grid by harnessing its power to enable radiologists from geographically dispersed hospitals to share standardized mammograms, to compare diagnoses (with and without computer aided detection of tumours) and to perform sophisticated epidemiological studies across national boundaries. This paper outlines the approach taken in MammoGrid to seamlessly connect radiologist workstations across a Grid using an "information infrastructure" and a DICOM-compliant object model residing in multiple distributed data stores in Italy and the UKComment: 10 pages, 5 figure

Cross-Platform Presentation of Interactive Volumetric Imagery

Volume data is useful across many disciplines, not just medicine. Thus, it is very important that researchers have a simple and lightweight method of sharing and reproducing such volumetric data. In this paper, we explore some of the challenges associated with volume rendering, both from a classical sense and from the context of Web3D technologies. We describe and evaluate the pro- posed X3D Volume Rendering Component and its associated styles for their suitability in the visualization of several types of image data. Additionally, we examine the ability for a minimal X3D node set to capture provenance and semantic information from outside ontologies in metadata and integrate it with the scene graph

Video codecs and decompressors

Digital video and audio produce very large and unwieldy files. Codecs are used to shrink files and them play back in reduced file size format. This article disusses the advantages, disadvantages and tradeoffs in using codes, and briefly reviews the most commonly used codecspeer-reviewe

The Development of Image Processing System for Medical Robot Remote Application

In this paper, web-base image processing system has been implemented for remote-controlled medical robot applications. The developed software system was hierarchically composed of diverse image processing and remote operation modules, and the hierarchical composition was satisfied the expandability to higher level application and the accessibility over the web. It can also support diverse file formats including DICOM, VRML, and CAD(STL) to display, transmit, store and share the processed images depending on application environment. Message-based data exchange, object-oriented module and open-source based software configuration will enable the dynamic combination associated with diverse remote medical application requirements.ope