Unsupervised Superpixel-based Segmentation of Histopathological Images with Consensus Clustering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

External organisations

  • University of Exeter


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
Subtitle of host publication21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11-13, 2017, Proceedings
Publication statusPublished - 2017
EventMedical 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


ConferenceMedical Image Understanding and Analysis (MIUA) 2017
CountryUnited Kingdom
Internet address


  • superpixel segmentation , consensus clustering , histology , histopathology , image analysis