Segmentation of gastroenterology images: A comparison between clustering and fitting models approaches | IEEE Conference Publication | IEEE Xplore

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


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 alg...Show More

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: chromoen-doscopy and narrow-band imaging. Observation shows that the model-based 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.
Date of Conference: 20-22 June 2013
Date Added to IEEE Xplore: 10 October 2013
Electronic ISBN:978-1-4799-1053-3
Print ISSN: 1063-7125
Conference Location: Porto, Portugal

References

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