Abstract
Confocal laser endomicroscopy is a recently introduced advanced imaging technique which enables microscopic imaging of the mucosa in-vivo. This technique has already been applied successfully during diagnosis of gastrointestinal diseases. Whereas for this purpose several computer aided diagnosis approaches exist, we present a classification system that is able to differentiate between healthy and pathological images of the oral cavity. Varying textural features of small rectangular regions are evaluated using random forests and support vector machines. Preliminary results reach up to 99.2% classification rate. This indicates that an automatic classification system to differentiate between healthy and pathological mucosa of the oral cavity is feasible.
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References
Mualla F, Sch¨oll S, Bohr C, et al. Epithelial cell detection in endomicroscopy images of the vocal folds. Springer Proc Phy. 2014;154:201–5.
Neumann H, Vieth M, Langner C, et al. Cancer risk in IBD: how to diagnose and how to manage DALM and ALM. World J Gastroenterol. 2011;17(27):3184–91.
Volgger V, Conderman C, Betz CS. Confocal laser endomicroscopy in head and neck cancer: steps forward? Curr Opin Otolaryngol Head Neck Surg. 2013;21(2):164–70.
Couceiro S, Barreto P, Freire P, et al. Machine learning in medical imaging: description and classification of confocal endomicroscopic images for the automatic diagnosis of inflammatory bowel disease. Lect Notes Comput Sci. 2012;7588:144–51.
Mualla F, Sch¨oll S, Sommerfeldt B, et al. Automatic cell detection in bright-field microscope images using SIFT, random forests, and hierarchical clustering. IEEE Trans Med Imaging. 2013;32(12):2274–86.
Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3(6):610–21.
Baraldi A, Parmiggiani F. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Trans Geosci Remote Sens. 1995;33(2):293–304.
Pietikainen M, Hadid A, Zhao G, et al. Computer Vision Using Local Binary Patterns. vol. 40 of Computational Imaging and Vision. Springer, London; 2011.
Platt J. Sequential minimal optimization: a fast algorithm for training support vector machines. Microsoft Research; 1998.
Breiman L. Random forests. Mach Learn. 2001; p. 5–32.
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Jaremenko, C. et al. (2015). Classification of Confocal Laser Endomicroscopic Images of the Oral Cavity to Distinguish Pathological from Healthy Tissue. In: Handels, H., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2015. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46224-9_82
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DOI: https://doi.org/10.1007/978-3-662-46224-9_82
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