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.
|Title of host publication||Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings|
|Editors||Maria Valdes Hernandez, Victor Gonzalez-Castro|
|Number of pages||13|
|Publication status||Published - 22 Jun 2017|
|Event||21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom|
Duration: 11 Jul 2017 → 13 Jul 2017
|Name||Communications in Computer and Information Science|
|Conference||21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017|
|Period||11/07/17 → 13/07/17|
Bibliographical noteFunding 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
- Image analysis
- Superpixel segmentation