The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered. © 2011 Springer-Verlag.
CITATION STYLE
Riaz, F., Vilarino, F., Ribeiro, M. D., & Coimbra, M. (2011). Identifying potentially cancerous tissues in chromoendoscopy images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6669 LNCS, pp. 709–716). https://doi.org/10.1007/978-3-642-21257-4_88
Mendeley helps you to discover research relevant for your work.