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.
|Number of pages||12|
|Publication status||Published - 19 Sep 2016|
|Event|| British Machine Vision Conference 2016 - University of York, York, United Kingdom|
Duration: 19 Sep 2016 → 19 Sep 2016
|Conference||British Machine Vision Conference 2016|
|Abbreviated title||BMVC 2016|
|Period||19/09/2016 → 19/09/2016|