A. Johannes Pretorius
School of Computing, University of Leeds, UK
Derek Magee
School of Computing, University of Leeds, UK
Darren Treanor
Leeds Teaching Hospitals Trust/Leeds Institute of Molecular Medicine, University of Leeds, UK
Roy A. Ruddle
School of Computing, University of Leeds, UK
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Published in: Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden
Linköping Electronic Conference Proceedings 81:9, p. 67-75
Published: 2012-11-20
ISBN: 978-91-7519-723-4
ISSN: 1650-3686 (print), 1650-3740 (online)
The conventional approach for parameter optimization of biomedical image analysis algorithms is to tweak parameters by trial-and-error. This presents a challenge: parameter space is often inadequately explored and; consequently; output quality suffers. Interactive visualization can alleviate this problem but has not been widely adopted. Moreover; few examples of the successful application of visualization for parameter optimization of image analysis algorithms have been published. To address this and to illustrate the potential usefulness of interactive visualization; we present a case study. A multidisciplinary team developing novel image segmentation software for histopathology was observed. Within the context of our study; our hypotheses were confirmed: (1) using interactive visualisation; participants considered larger parts of parameter space than they had previously by trial-and-error; (2) participants gained a better understanding of their algorithm (an unknown logic error and errors in its implementation were discovered); and (3) participants achieved higher quality output. Our work is also an example of the value of case studies in iterative design. We describe how a valuable additional requirement was revealed (the importance of derived measures) and how our visualization method was extended to cater for this.
H.5.2 [Information Interfaces and Presentation]: User Interfaces—Visualization; I.3.8 [Computer Graphics]: Applications—Biomedicine
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