Interpretation of Medical Imaging Data with a Mobile Application: A Mobile Digital Imaging Processing Environment

Digital Imaging Processing (DIP) requires data extraction and output from a visualization tool to be consistent. Data handling and transmission between the server and a user is a systematic process in service interpretation. The use of integrated medical services for management and viewing of imaging data in combination with a mobile visualization tool can be greatly facilitated by data analysis and interpretation. This paper presents an integrated mobile application and DIP service, called M-DIP. The objective of the system is to (1) automate the direct data tiling, conversion, pre-tiling of brain images from Medical Imaging NetCDF (MINC), Neuroimaging Informatics Technology Initiative (NIFTI) to RAW formats; (2) speed up querying of imaging measurement; and (3) display high-level of images with three dimensions in real world coordinates. In addition, M-DIP provides the ability to work on a mobile or tablet device without any software installation using web-based protocols. M-DIP implements three levels of architecture with a relational middle-layer database, a stand-alone DIP server, and a mobile application logic middle level realizing user interpretation for direct querying and communication. This imaging software has the ability to display biological imaging data at multiple zoom levels and to increase its quality to meet users’ expectations. Interpretation of bioimaging data is facilitated by an interface analogous to online mapping services using real world coordinate browsing. This allows mobile devices to display multiple datasets simultaneously from a remote site. M-DIP can be used as a measurement repository that can be accessed by any network environment, such as a portable mobile or tablet device. In addition, this system and combination with mobile applications are establishing a virtualization tool in the neuroinformatics field to speed interpretation services.

[1]  V. Horner.  Developing a consumer health informatics decision support system using formal concept analysis , 2008 .

[2]  Steve Evans,et al.  The DEDUCE Guided Query tool: Providing simplified access to clinical data for research and quality improvement , 2011, J. Biomed. Informatics.

[3]  Carole Alcock,et al.  Enhancing User Acceptance of Mandated Technology Implementation in a Mobile Healthcare Setting: A Case Study , 2005 .

[4]  Jesus J. Caban,et al.  Rapid Development of Medical Imaging Tools with Open-Source Libraries , 2007, Journal of Digital Imaging.

[5]  Torsten Seemann,et al.  Digital Image Processing using Local Segmentation , 2002 .

[6]  Kevin W. Eliceiri,et al.  Open source bioimage informatics for cell biology , 2009, Trends in cell biology.

[7]  Alan C. Evans,et al.  LORIS: a web-based data management system for multi-center studies , 2012, Front. Neuroinform..

[8]  A HarrisPaul,et al.  Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support , 2009 .

[9]  Mykola Pechenizkiy,et al.  Towards the framework of adaptive user interfaces for eHealth , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[10]  Ephraim R. McLean,et al.  Information Systems Success: The Quest for the Dependent Variable , 1992, Inf. Syst. Res..

[11]  Ram B. Misra,et al.  Global IT Outsourcing: Metrics for Success of All Parties , 2004 .

[12]  Eugenia G. Giannopoulou.  Data Mining in Medical and Biological Research , 2008 .

[13]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[14]  B. Demaerschalk,et al.  Systematic Review of Teleneurology: Methodology , 2012, Front. Neur..

[15]  Jessica A. Turner,et al.  Electronic Data Capture, Representation, and Applications for Neuroimaging , 2012, Front. Neuroinform..

[16]  Larry E. Davis,et al.  Teleneurology applications , 2013, Neurology.

[17]  B. S. Manjunath,et al.  Biological imaging software tools , 2012, Nature Methods.

[18]  Oded Berman,et al.  A decision model and support system for the optimal design of health information networks , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[19]  Xiaobo Zhou,et al.  Towards Automated Cellular Image Segmentation for RNAi Genome-Wide Screening , 2005, MICCAI.

[20]  Raj Gururajan,et al.  Development Of A Prototype Multi-Touch ECG Diagnostic Decision Support System Using Mobile Technology For Monitoring Cardiac Patients At A Distance , 2011, PACIS.

[21]  Hanchuan Peng,et al.  Bioimage informatics: a new area of engineering biology , 2008, Bioinform..

[22]  Markus Hadwiger,et al.  Ssecrett and NeuroTrace: Interactive Visualization and Analysis Tools for Large-Scale Neuroscience Data Sets , 2010, IEEE Computer Graphics and Applications.

[23]  I. Cosic,et al.  A Mobile Device Based ECG Analysis System , 2008 .

[24]  Stephen T. C. Wong,et al.  ZFIQ: a software package for zebrafish biology , 2008, Bioinform..

[25]  Timothy R. Olsen,et al.  The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. , 2007, Neuroinformatics.

[26]  David B. Keator,et al.  A National Human Neuroimaging Collaboratory Enabled by the Biomedical Informatics Research Network (BIRN) , 2008, IEEE Transactions on Information Technology in Biomedicine.

[27]  Arthur W. Toga,et al.  Is it time to re-prioritize neuroimaging databases and digital repositories? , 2009, NeuroImage.

[28]  Dimiter Prodanov.  Data Ontology and an Information System Realization for Web-Based Management of Image Measurements , 2011, Front. Neuroinform..