Abstract
Confocal Endomicroscopy (CEM) is a newly developed diagnosis tool which provides in vivo examination of the gastrointestinal (GI) histological architecture, avoiding the traditional biopsy . The analysis of CEM images is a challenging task for experts, since there isn’t a clearly defined taxonomy of the several disease stages. We aim at building an automatic on-the-fly classifier to provide useful clinical advices for diagnosis. In this work, we propose to make a split between two main subsets of our expert-annotated database: low and high probability of pathology. We focus on segmentation techniques to extract relevant histological structures, and then encode this information in a feature vector used for classification.
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Couceiro, S., Barreto, J.P., Freire, P., Figueiredo, P. (2012). Description and Classification of Confocal Endomicroscopic Images for the Automatic Diagnosis of Inflammatory Bowel Disease. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_18
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DOI: https://doi.org/10.1007/978-3-642-35428-1_18
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