MDL Based Structure Selection of Union of Ellipse Models for Scaled and Smoothed Histological Images

  • Jenni Hukkanen
  • Edmond Sabo
  • Ioan Tabus
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 187)


In this chapter, we investigate refinements to the structure selection method used in our recently developed minimum description length (MDL) method for interpreting clumps of nuclei in histological images. We start from the SNEF method, which fits elliptical shapes to the clump image based on the extracted contours and on the image gradient information. Introducing some variability in the parameters of the algorithm, we obtain a number of competing interpretations and we select the least redundant interpretation based on the MDL principle, where the description codelengths are evaluated by a simple implementable coding scheme. We investigate in this paper two ways for allowing additional variability in the basic SNEF method: first by utilizing a pre-processing stage of smoothing the original image using various degrees of smoothing and second by using re-scaling of the original image at various downsizing scales. Both transformations have the potential to hide artifacts and features of the original image that prevented the proper interpretation of the nuclei shapes, and we show experimentally that the set of candidate segmentations obtained will contain variants with better MDL values than the MDL of the initial SNEF segmentations. We compare the results of the automatic interpretation algorithm against the ground truth defined by annotations of human subjects.


Ground Truth Original Image Similarity Index Minimum Description Length Histological Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jenni Hukkanen
    • 1
  • Edmond Sabo
    • 2
  • Ioan Tabus
    • 1
  1. 1.Department of Signal ProcessingTampere University of TechnologyTampereFinland
  2. 2.Department of Pathology, Rappaport Faculty of MedicineTechnionHaifaIsrael

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