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

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Riaz, F (Author) Other; Nunes, PP (Author) Other; Ribeiro, MD (Author) Other; Coimbra, MT (Author) FCUP

Conference proceedings International

Title: 2013 IEEE 26TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Pages: 550-551

26th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

Porto, PORTUGAL, JUN 20-22, 2013

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Other information

Authenticus ID: P-008-HXK

DOI: 10.1109/CBMS.2013.6627872

Abstract (EN): 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 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 2