Multi-scale Colorectal Tumour Segmentation Using a Novel Coarse to Fine Strategy

Kun Zhang, Danny Crookes, Jim Diamond, Minrui Fei, Jianguo Wu, Peijian Zhang and Huiyu Zhou

Abstract

This paper addresses the problem of colorectal tumour segmentation in complex real world imagery. For efficient segmentation, a multi-scale strategy is developed for extracting the potentially cancerous region of interest (ROI) based on colour histograms while searching for the best texture resolution. To achieve better segmentation accuracy, we apply a novel bag-of-visual-words method based on rotation invariant raw statistical features and random projection based l2-norm sparse representation to classify tumour areas in histopathology images. Experimental results on 20 real world digital slides demonstrate that the proposed algorithm results in better recognition accuracy than several state of the art segmentation techniques.

Session

Posters 2

Files

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Paper (PDF, 3M)

DOI

10.5244/C.30.97
https://dx.doi.org/10.5244/C.30.97

Citation

Bibtex

        @inproceedings{BMVC2016_97,
        	title={Multi-scale Colorectal Tumour Segmentation Using a Novel Coarse to Fine Strategy},
        	author={Kun Zhang, Danny Crookes, Jim Diamond, Minrui Fei, Jianguo Wu, Peijian Zhang and Huiyu Zhou},
        	year={2016},
        	month={September},
        	pages={97.1-97.12},
        	articleno={97},
        	numpages={12},
        	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
        	publisher={BMVA Press},
        	editor={Richard C. Wilson, Edwin R. Hancock and William A. P. Smith},
        	doi={10.5244/C.30.97},
        	isbn={1-901725-59-6},
        	url={https://dx.doi.org/10.5244/C.30.97}
        }