Abstract
Optical endomicroscopy (OEM) is a novel real-time imaging technology that provides endoscopic images at the microscopic level. Clinical OEM procedures generate large datasets making their post procedural analysis a subjective and laborious task. There has been effort to automatically classify OEM frame sequences into relevant classes in aid of a fast and reliable diagnosis. Most existing classification approaches adopt established texture metrics, such as Local Binary Patterns (LBPs) derived from the regularly sampled grid images. However, due to the nature of image transmission through coherent fibre bundles, raw OEM data are sparsely and irregularly sampled, post-processed to a regularly sampled grid image format. This paper adapts Local Binary Patterns, a commonly used image texture descriptor, taking into consideration the sparse, irregular sampling imposed by the imaging fibre bundle on OEM images. The performance of Sparse Irregular Local Binary Patterns (SILBP) is assessed in conjunction with widely used classifiers, including Support Vector Machines, Random Forests and Linear Discriminant Analysis, for the detection of uninformative frames (i.e. noise and motion-artefacts) within pulmonary OEM frame sequences. Uninformative frames can comprise a considerable proportion of a dataset, increasing the resources required to analyse the data and impacting on any automated quantification analysis. SILBPs achieve comparable performance to the optimal LBPs (>92% overall accuracy), while employing <13% of the associated data.
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Acknowledgements
We thank the Engineering & Physical Sciences Research Council (UK) for the support via EP/K03197X/1. AA is a supported by Cancer Research, UK.
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Leonovych, O. et al. (2018). Texture Descriptors for Classifying Sparse, Irregularly Sampled Optical Endomicroscopy Images. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_17
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