Conference article

Visual Parameter Optimization for Biomedical Image Analysis: A Case Study

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

Download article

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

Show more +

Published: 2012-11-20

ISBN: 978-91-7519-723-4

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

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.

Keywords

H.5.2 [Information Interfaces and Presentation]: User Interfaces—Visualization; I.3.8 [Computer Graphics]: Applications—Biomedicine

References

[AH97] ANSARI N.; HOU E.: Computational Intelligence for Optimization. Springer; 1997. 68

[BM10] BRUCKNER S.; MÖLLER T.: Result-driven exploration of simulation parameter spaces for visual effects design. IEEE Transactions on Visualization and Computer Graphics 16; 6 (2010); 1468–1476. 69

[BPFG11] BERGER W.; PIRINGER H.; FILZMOSER P.; GRÖLLER E.: Uncertainty-aware exploration of continuous parameter spaces using multivariate prediction. Computer Graphics Forum 30; 3 (2011); 911–920. 68

[Bro] BROAD INSTITUTE OF MIT AND HARVARD: Broad Bioimage Benchmark Collection; SBS Bioimage CNT. http://www.broadinstitute.org/bbbc/sbs_bioimage_cnt.html. Last visited 31 March 2012. 73

[BSN11] BERGNER S.; SEDLMAIR M.; NABI S.; SAAD A.; MÖLLER T.: Paraglide: interactive parameter space partitioning for computer simulations. Computing Research Repository abs/1110.5181 (2011). 68

[CFS06] CALLAHAN S. P.; FREIRE J.; SANTOS E.; SCHEIDEGGER C. E.; SILVA C. T.; VO H. T.: Managing the evolution of dataflows with VisTrails. In Proceedings of the International Conference on Data Engineering Workshops (2006); pp. 71–75. 69

[CJL06] CARPENTER A. E.; JONES T. R.; LAMPBRECHT M. R.; CLARKE C.; KANG I. H.; FRIMAN O.; GUERTIN D. A.; CHANG J. H.; LINDQUIST R. A.; MOFFAT J.; GOLLAND P.; SABATINI D. M.: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biology 7; R100 (2006). 68; 70

[Har75] HARTIGAN J. A.: Printer graphics for clustering. Journal of Statistical Computation and Simulation 4; 3 (1975); 187–213. 68

[Ins85] INSELBERG A.: The plane with parallel coordinates. The Visual Computer 1 (1985); 69–91. 68

[JKM00] JANKUN-KELLY T. J.; MA K.-L.: A spreadsheet interface for visualization exploration. In Proceedings of the IEEE Conference on Visualization (2000); pp. 69–76. 69

[Ma99] MA K.-L.: Image graphs - a novel approach to visual data exploration. In Proceedings of the IEEE Conference on Visualization (1999); pp. 81–88. 69

[MAB97] MARKS J.; ANDALMAN B.; BEARDSLEY P. A.; FREEMAN W.; GIBSON S.; HODGINS J.; KANG T.; MIRTICH B.; PFISTER H.; RUML W.; RYALL K.; SEIMS J.; SHIEBER S.: Design Galleries: a general approach to setting parameters for computer graphics and animation. In Proceedings of the Conference on Computer Graphics and Interactive Techniques (1997); pp. 389–400. 69

[Onw00] ONWUBIKO C.: Introduction to Engineering Design Optimization. Prentice-Hall; 2000. 68 [PBCR11] PRETORIUS A. J.; BRAY M. A. P.; CARPENTER A. E.; RUDDLE R. A.: Visualization of parameter space for image analysis. IEEE Transactions on Computer Graphics and Applications 17; 2 (2011); 2402–4211. 68; 70; 73

[PBK10] PIRINGER H.; BERGER W.; KRASSER J.: Hyper- MoVal: interactive visual validation of regression models for real-time simulation. Computer Graphics Forum 29; 3 (2010); 989–992. 68

[Pla04] PLAISANT C.: The challenge of information visualizations evaluation. In Proceedings of the Working Conference on Advanced Visual Interfaces (2004); pp. 109–116. 74

[Pre] PRETORIUS A. J.: Paramorama website. http://www.comp.leeds.ac.uk/scsajp/applications/paramorama/. Last visited 1 September 2012. 74

[SP06] SHNEIDERMAN B.; PLAISANT C.: Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In Proceedings of the AVI Workshop on Beyond Time and Errors (2006); pp. 1–7. 74

[TJOH09] TREANOR D.; JORDAN-OWERS N.; HODRIEN J.; QUIRKE P.; RUDDLE R. A.: Virtual reality powerwall versus conventional microscope for viewing pathology slides: an experimental comparison. Histopathology 55; 3 (2009); 294–300. 69

[TS98] TWEEDIE L.; SPENCE R.: The prosection matrix: a tool to support the interactive exploration of statistical models and data. Computational Statistics 13; 1 (1998); 65–76. 68

[TSDS95] TWEEDIE L.; SPENCE B.; DAWKES H.; SU H.: The influence explorer. In Proceedings of the Conference on Human Factors in Computing Systems (1995); pp. 129–130. 68; 73

[Tun09] TUNKELANG D.: Faceted search. Synthesis Lectures on Information Concepts; Retrieval; and Services 1; 1 (2009); 1–80. 73

[TWSM11] TORSNEY-WEIR T.; SAAD A.; MÖLLER T.; WEBER B.; HEGE H. C.; VER-BAVATZ J. M.; BERGNER S.: Tuner: principled parameter finding for image segmentation algorithms using visual response surface exploration. IEEE Transactions on Computer Graphics and Applications 17; 2 (2011); 1892–1901. 68

[WB97] WONG P. C.; BERGERON R. D.: 30 years of multidimensional multivariate visualization. Scientific Visualization: Overview; Methodologies; and Techniques (1997); 3–33. 68

[WHA07] WILLETT W.; HEER J.; AGRAWALA M.: Scented widgets: improving navigation cues with embedded visualizations. IEEE Transactions on Visualization and Computer Graphics 13; 6 (2007); 1129–1136. 73

Citations in Crossref