Literature DB >> 17386997

CAD-PACS integration tool kit based on DICOM secondary capture, structured report and IHE workflow profiles.

Zheng Zhou1, Brent J Liu, Anh H Le.   

Abstract

Computer aided diagnosis/detection (CAD) goes beyond subjective visual assessment of clinical images providing quantitative computer analysis of the image content, and can greatly improve clinical diagnostic outcome. Many CAD applications, including commercial and research CAD, have been developed with no ability to integrate the CAD results with a clinical picture archiving and communication system (PACS). This has hindered the extensive use of CAD for maximum benefit within a clinical environment. In this paper, we present a CAD-PACS integration toolkit that integrates CAD results with a clinical PACS. The toolkit is a software package with two versions: DICOM (digital imaging and communications in medicine)-SC (secondary capture) and DICOM-IHE (Integrating the Healthcare Enterprise). The former uses the DICOM secondary capture object model to convert the screen shot of the CAD results to a DICOM image file for PACS workstations to display, while the latter converts the CAD results to a DICOM structured report (SR) based on IHE Workflow Profiles. The DICOM-SC method is simple and easy to be implemented without ability for further data mining of CAD results, while the DICOM-IHE can be used for data mining of CAD results in the future but more complicated to implement than the DICOM-SC method.

Mesh:

Year:  2007        PMID: 17386997     DOI: 10.1016/j.compmedimag.2007.02.015

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  6 in total

Review 1.  Integration of computer-aided diagnosis/detection (CAD) results in a PACS environment using CAD-PACS toolkit and DICOM SR.

Authors:  Anh H T Le; Brent Liu; H K Huang
Journal:  Int J Comput Assist Radiol Surg       Date:  2009-04-15       Impact factor: 2.924

2.  Black box integration of computer-aided diagnosis into PACS deserves a second chance: results of a usability study concerning bone age assessment.

Authors:  Ina Geldermann; Christoph Grouls; Christiane Kuhl; Thomas M Deserno; Cord Spreckelsen
Journal:  J Digit Imaging       Date:  2013-08       Impact factor: 4.056

3.  Bone age assessment of children using a digital hand atlas.

Authors:  Arkadiusz Gertych; Aifeng Zhang; James Sayre; Sylwia Pospiech-Kurkowska; H K Huang
Journal:  Comput Med Imaging Graph       Date:  2007-03-26       Impact factor: 4.790

4.  Racial differences in growth patterns of children assessed on the basis of bone age.

Authors:  Aifeng Zhang; James W Sayre; Linda Vachon; Brent J Liu; H K Huang
Journal:  Radiology       Date:  2008-10-27       Impact factor: 11.105

5.  Design and development of an ethnically-diverse imaging informatics-based eFolder system for multiple sclerosis patients.

Authors:  Kevin C Ma; James R Fernandez; Lilyana Amezcua; Alex Lerner; Mark S Shiroishi; Brent J Liu
Journal:  Comput Med Imaging Graph       Date:  2015-10-23       Impact factor: 4.790

Review 6.  An engineering view on megatrends in radiology: digitization to quantitative tools of medicine.

Authors:  Namkug Kim; Jaesoon Choi; Jaeyoun Yi; Seungwook Choi; Seyoun Park; Yongjun Chang; Joon Beom Seo
Journal:  Korean J Radiol       Date:  2013-02-22       Impact factor: 3.500

  6 in total

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