LOCAL SEGMENTATION BY LARGE SCALE HYPOTHESIS TESTING - Segmentation as Outlier Detection

Sune Darkner, Anders B. Dahl, Rasmus Larsen, Arnold Skimminge, Ellen Garde, Gunhild Waldemar

2010

Abstract

We propose a novel and efficient way of performing local image segmentation. For many applications a threshold of pixel intensities is sufficient. However, determining the appropriate threshold value poses a challenge. In cases with large global intensity variation the threshold value has to be adapted locally. We propose a method based on large scale hypothesis testing with a consistent method for selecting an appropriate threshold for the given data. By estimating the prominent distribution we characterize the segment of interest as a set of outliers or the distribution it self. Thus, we can calculate a probability based on the estimated densities of outliers actually being outliers using the false discovery rate (FDR). Because the method relies on local information it is very robust to changes in lighting conditions and shadowing effects. The method is applied to endoscopic images of small particles submerged in fluid captured through a microscope and we show how the method can handle transparent particles with significant glare point. The method generalizes to other problems. This is illustrated by applying the method to camera calibration images and MRI of the midsagittal plane for gray and white matter separation and segmentation of the corpus callosum. Comparing this segmentation method with manual corpus callosum segmentation an average dice score of 0.88 is obtained across 40 images.

References

  1. Bretzner, L. and Lindeberg, T. (1998). Feature tracking with automatic selection of spatial scales. Computer Vision and Image Understanding, 71(3):385-392.
  2. Darkner, S., Paulsen, R. R., and Larsen, R. (2007). Analysis of deformation of the human ear and canal caused by mandibular movement. In Medical Image Computing and Computer Assisted Intervention MICCAI 2007, pages 801-8, B. Brisbane, Australia, Springer Lecture Notes.
  3. Efron, B. (2004). Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. Journal of the American Statistical Association, 99(465):96-104.
  4. Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag.
  5. Ng, H. (2006). Automatic thresholding for defect detection. Pattern Recognition Letters, 27(14):1644-1649.
  6. Otsu, N. (1975). A threshold selection method from graylevel histograms. Automatica, 11:285-296.
  7. Ryberg, C., Stegmann, M. B., Sj östrand, K., Rostrup, E., Barkhof, F., Fazekas, F., and Waldemar, G. (2006). Corpus callosum partitioning schemes and their effect on callosal morphometry.
  8. Sezan, M. (1990). A peak detection algorithm and its application to histogram-based image data reduction. Computer Vision, Graphics, and Image Processing, 49(1):51.
  9. Sezgin, M. and Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1):146- 168.
  10. Sørensen, T. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske Skrifter, (5):1-34.
  11. Stockman, G. and Shapiro, L. (2001). Computer Vision. Prentice Hall PTR, Upper Saddle River, NJ, USA.
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Paper Citation


in Harvard Style

Darkner S., B. Dahl A., Larsen R., Skimminge A., Garde E. and Waldemar G. (2010). LOCAL SEGMENTATION BY LARGE SCALE HYPOTHESIS TESTING - Segmentation as Outlier Detection . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 215-220. DOI: 10.5220/0002845402150220


in Bibtex Style

@conference{visapp10,
author={Sune Darkner and Anders B. Dahl and Rasmus Larsen and Arnold Skimminge and Ellen Garde and Gunhild Waldemar},
title={LOCAL SEGMENTATION BY LARGE SCALE HYPOTHESIS TESTING - Segmentation as Outlier Detection},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={215-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002845402150220},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - LOCAL SEGMENTATION BY LARGE SCALE HYPOTHESIS TESTING - Segmentation as Outlier Detection
SN - 978-989-674-029-0
AU - Darkner S.
AU - B. Dahl A.
AU - Larsen R.
AU - Skimminge A.
AU - Garde E.
AU - Waldemar G.
PY - 2010
SP - 215
EP - 220
DO - 10.5220/0002845402150220