A review of thresholding strategies applied to human chromosome segmentation

  • Authors:

  • Enea Poletti;Francesca Zappelli;Alfredo Ruggeri;Enrico Grisan

  • Affiliations:

  • Department of Information Engineering, University of Padova, Via Gradenigo 6/a, 35131 Padova, Italy;Department of Information Engineering, University of Padova, Via Gradenigo 6/a, 35131 Padova, Italy;Department of Information Engineering, University of Padova, Via Gradenigo 6/a, 35131 Padova, Italy;Department of Information Engineering, University of Padova, Via Gradenigo 6/a, 35131 Padova, Italy

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2012

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Abstract

Karyotype analysis is a widespread procedure in cytogenetics to assess the presence of genetic defects by the visualization of the structure of chromosomes. The procedure is lengthy and repetitive and an effective automatic analysis would greatly help the cytogeneticist routine work. Still, automatic segmentation and the full disentangling of chromosomes are open issues. The first step in every automatic procedure is the thresholding step, which detect blobs that represent either single chromosomes or clusters of chromosomes. The better the thresholding step, the easier is the subsequent disentanglement of chromosome clusters into single entities. We implemented eleven thresholding methods, i.e. the ones that appear in the literature as the best performers, and compared their performance in segmenting chromosomes and chromosome clusters in cytogenetic Q-band images. The images are affected by the presence of hyper- or hypo-fluorescent regions and by a contrast variability between the stained chromosomes and the background. A thorough analysis of the results highlights that, although every single algorithm shows peculiar strong/weak points, Adaptive Threshold and Region Based Level Set have the overall best performance. In order to provide the scientific community with a public dataset, the data and manual segmentation used in this paper are available for public download at http://bioimlab.dei.unipd.it