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Segmentation of gastroenterology images: A comparison between clustering and fitting models approaches

Riaz, F. ; Dinis-Ribeiro, M. ; Nunes, P. Pimentel Nunes ; Coimbra, M.

Segmentation of gastroenterology images: A comparison between clustering and fitting models approaches, Proc IEEE International Symp. on Computer-Based Medical Systems - CBMS, Porto, Portugal, Vol. ., pp. . - ., June, 2013.

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Abstract
Segmentation is a vital step for pattern recognition systems used in in-body imaging scenarios. In this paper we compare the performance of three popular segmentation algorithms (mean shift, normalized cuts, level-sets) when applied
to two distinct in-body imaging scenarios: chromoendoscopy and narrow-band imaging. Observation shows that the modelbased algorithm did not perform well, when compared to its segmentation by clustering alternatives. Normalized cuts obtained the best performance although future work hints that texture similarity should be further explored in order to increase segmentation performance in this type of scenarios.