Unsupervised superpixel-based segmentation of histopathological images with consensus clustering

Shereen Fouad*, David Randell, Antony Galton, Hisham Mehanna, Gabriel Landini

*Corresponding author for this work

    Research output: Chapter in Book/Published conference outputConference publication


    We present a framework for adapting consensus clustering methods with superpixels to segment oropharyngeal cancer images into tissue types (epithelium, stroma and background). The simple linear iterative clustering algorithm is initially used to split-up the image into binary superpixels which are then used as clustering elements. Colour features of the superpixels are extracted and fed into several base clustering approaches with various parameter initializations. Two consensus clustering formulations are then used, the Evidence Accumulation Clustering (EAC) and the voting-based function. They both combine the base clustering outcomes to obtain a single more robust consensus result. Unlike most unsupervised tissue image segmentation approaches that depend on individual clustering methods, the proposed approach allows for a robust detection of tissue compartments. For the voting-based consensus function, we introduce a technique based on image processing to generate a consistent labelling scheme among the base clustering outcomes. Experiments conducted on forty five hand-annotated images of oropharyngeal cancer tissue microarray cores show that the ensemble algorithm generates more accurate and stable results than individual clustering algorithms. The clustering performance of the voting-based consensus function using our re-labelling technique also outperforms the existing EAC.

    Original languageEnglish
    Title of host publicationMedical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings
    EditorsMaria Valdes Hernandez, Victor Gonzalez-Castro
    Number of pages13
    ISBN (Print)9783319609638
    Publication statusPublished - 22 Jun 2017
    Event21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom
    Duration: 11 Jul 201713 Jul 2017

    Publication series

    NameCommunications in Computer and Information Science
    ISSN (Print)1865-0929
    ISSN (Electronic)1865-0937


    Conference21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017
    Country/TerritoryUnited Kingdom

    Bibliographical note

    Funding Information:
    This work was supported by the EPSRC through funding under grant EP/M023869/1 'Novel context-based segmentation algorithms for intelligent microscopy'.


    • Consensus clustering
    • Histology
    • Histopathology
    • Image analysis
    • Superpixel segmentation


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