Time-Dependent Joint Probability Speed Function for Level-Set Segmentation of Rat-Brain Slices

  • The segmentation of rat brain slices suffers from illumination inhomogeneities and staining effects. State-of-the-art level-set methods model slice and background with intensity mixture densities defining the speed function as difference between the respective probabilites. Nevertheless, the overlap of these distributions causes an inaccurate stopping at the slice border. In this work, we propose the characterisation of the border area with intensity pairs for inside and outside estimating joint intensity probabilities. Method - In contrast to global object and background models, we focus on the object border characterised by a joint mixture density. This specifies the probability of the occurance of an inside and an outside value in direct adjacency. These values are not known beforehand, because inside and outside depend on the level-set evolution and change during time. Therefore, the speed function is computed time-dependently at the position of the current zero level-set. Along this zero level-set curve, the inside and outside values are derived as mean along the curvature normal directing inside and outside the object. Advantage of the joint probability distribution is to resolve the distribution overlaps, because these are assumed to be not located at the same border position. Results - The novel time-dependent joint probability based speed function is compared expermimentally with single probability based speed functions. Two rat brains with about 40 slices are segmented and the results analysed using manual segmentations and the Tanimoto overlap measure. Improved results are recognised for both data sets.

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Metadaten
Author:Christoph PalmORCiDGND, Uwe Pietrzyk
DOI:https://doi.org/10.1117/12.770673
Parent Title (English):Proceedings of the SPIE Medical Imaging 6914: Image Processing 69143U
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2008
Release Date:2020/01/22
Tag:Beryllium; Brain; Image processing; Image segmentation; Medical imaging; Neuroimaging; Visualization
GND Keyword:Kernspintomografie; Histologie; Schnittdarstellung; Bildsegmentierung; Gehirn
Issue:6914
First Page:69143U-1
Last Page:69143U-8
Institutes:Fakult├Ąt Informatik und Mathematik
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
Publication:Externe Publikationen