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Non-parametric and integrated framework for segmenting and counting neuroblastic cells within neuroblastoma tumor images

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Abstract

Neuroblastoma is a malignant tumor and a cancer in childhood that derives from the neural crest. The number of neuroblastic cells within the tumor provides significant prognostic information for pathologists. An enormous number of neuroblastic cells makes the process of counting tedious and error-prone. We propose a user interaction-independent framework that segments cellular regions, splits the overlapping cells and counts the total number of single neuroblastic cells. Our novel segmentation algorithm regards an image as a feature space constructed by joint spatial-intensity features of color pixels. It clusters the pixels within the feature space using mean-shift and then partitions the image into multiple tiles. We propose a novel color analysis approach to select the tiles with similar intensity to the cellular regions. The selected tiles contain a mixture of single and overlapping cells. We therefore also propose a cell counting method to analyse morphology of the cells and discriminate between overlapping and single cells. Ultimately, we apply watershed to split overlapping cells. The results have been evaluated by a pathologist. Our segmentation algorithm was compared against adaptive thresholding. Our cell counting algorithm was compared with two state of the art algorithms. The overall cell counting accuracy of the system is 87.65 %.

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Authors and Affiliations

  1. Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, PO Box 123, Broadway, Sydney, NSW, 2007, Australia

    Siamak Tafavogh, Karla Felix Navarro & Paul J. Kennedy

  2. Biospecimens Research and Tumor Bank, Children’s Cancer Research Unit, The Kids Research Institute, The Children’s Hospital at Westmead, Locked Bag 4001, Westmead, NSW, 2145, Australia

    Daniel R. Catchpoole

Authors
  1. Siamak Tafavogh
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  2. Karla Felix Navarro
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  3. Daniel R. Catchpoole
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  4. Paul J. Kennedy
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Correspondence to Siamak Tafavogh.

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Tafavogh, S., Navarro, K.F., Catchpoole, D.R. et al. Non-parametric and integrated framework for segmenting and counting neuroblastic cells within neuroblastoma tumor images. Med Biol Eng Comput 51, 645–655 (2013). https://doi.org/10.1007/s11517-013-1034-9

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  • DOI: https://doi.org/10.1007/s11517-013-1034-9

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