Methods Inf Med 2009; 48(04): 336-339
DOI: 10.3414/ME9232
Original Articles
Schattauer GmbH

A Quality-refinement Process for Medical Imaging Applications

J. Neuhaus*
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
D. Maleike*
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
M. Nolden
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
H.-G. Kenngott
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
H.-P. Meinzer
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
I. Wolf
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
› Institutsangaben
Weitere Informationen

Publikationsverlauf

05. Juni 2009

Publikationsdatum:
17. Januar 2018 (online)

Summary

Objectives: To introduce and evaluate a process for refinement of software quality that is suitable to research groups. In order to avoid constraining researchers too much, the quality improvement process has to be designed carefully. The scope of this paper is to present and evaluate a process to advance quality aspects of existing research prototypes in order to make them ready for initial clinical studies. The proposed process is tailored for research environments and therefore more lightweight than traditional quality management processes.

Methods: Focus on quality criteria that are important at the given stage of the software life cycle. Usage of tools that automate aspects of the process is emphasized. To evaluate the additional effort that comes along with the process, it was exemplarily applied for eight prototypical software modules for medical image processing.

Results: The introduced process has been applied to improve the quality of all prototypes so that they could be successfully used in clinical studies. The quality refinement yielded an average of 13 person days of additional effort per project. Overall, 107 bugs were found and resolved by applying the process.

Conclusions: Careful selection of quality criteria and the usage of automated process tools lead to a lightweight quality refinement process suitable for scientific research groups that can be applied to ensure a successful transfer of technical software prototypes into clinical research workflows.

* First two authors contributed equally to this work.


 
  • References

  • 1 Lehmann TM, Meinzer HP, Tolxdorff T. Advances in Biomedical Image Analysis – Past, Present and Future Challenges. Methods Inf Med 2004; 43: 308-314.
  • 2 Müller H, Gao X, Luo S. From medical imaging to medical informatics. Computer Methods and Programs in Biomedicine 2008; 92: 225-226.
  • 3 Handels H, Ehrhardt J. Medical Image Computing for Computer-supported Diagnostics and Therapy – Advances and Perspectives. Methods Inf Med 2009; 48: 11-17.
  • 4 Duncan JS, Ayache N. Medical image analysis: progress over two decades and the challenges ahead. IEEE Trans Pattern Anal Mach Intell 2000; 22 (Suppl. 01) 85-106.
  • 5 McCall JA, Richards PK, Walters GF. Factors in Software Quality. National Technology Information Service 1977. Springfield; USA: AD/A-049-014/15/055.
  • 6 Dromey RG. A Model for Software Product Quality. IEEE Transactions on Software Engineering 1995 pp 146-162.
  • 7 ISO/IEC 9126-1:2001.. Software engineering – Product quality – Part 1: Quality model. IEC.2001.
  • 8 Garvin D. What does “product quality” really mean?. Sloan Management Review 1984 pp 25-45.
  • 9 ISO 9001:2008.. Quality management systems – Requirements. IEC; 2001
  • 10 CMMI Product Team.. CMMI for Development, Version 1.2, CMMI-DEV v1.2, CMU/SEI-2006– TR-008, Technical Report. Software Engineering Institute 2006
  • 11 ISO 13485:2003.. Medical devices – Quality management systems – Requirements for regulatory purposes. IEC; 2003
  • 12 Kitchenham B, Pfleeger SL. Software Quality: The elusive target. IEEE Software 1996 pp 12-21.
  • 13 Schroeder WJ, Ibanez L, Martin KM. Software process: the key to developing robust, reusable and maintainable open-source software. IEEE International Symposium on Biomedical Imaging 2004: Nano to Macro. 2004 pp 648-651.
  • 14 Ißler L, Spreckelsen C, Weßel C. Implementing Software Development Guidelines in a Medical Informatics Research Project. Methods Inf Med 2007; 46: 641-645.