Texture Descriptors for Classifying Sparse, Irregularly Sampled Optical Endomicroscopy Images

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.

[1]  Guang-Zhong Yang,et al.  Imaging parenchymal lung diseases with confocal endomicroscopy. , 2012, Respiratory medicine.

[2]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[3]  Laurent Heutte,et al.  Characterization of Endomicroscopic Images of the Distal Lung for Computer-Aided Diagnosis , 2009, ICIC.

[4]  Stephen McLaughlin,et al.  Automated Detection of Uninformative Frames in Pulmonary Optical Endomicroscopy , 2017, IEEE Transactions on Biomedical Engineering.

[5]  Caroline Petitjean,et al.  An SVM-based distal lung image classification using texture descriptors , 2012, Comput. Medical Imaging Graph..

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[10]  K. Dhaliwal,et al.  Highly specific, multi-branched fluorescent reporters for analysis of human neutrophil elastase. , 2013, Organic & biomolecular chemistry.

[11]  David Wilson,et al.  Multi-class classification of pulmonary endomicroscopic images , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[12]  Seetha Hari,et al.  Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.

[13]  G. Bourg-Heckly,et al.  Human in vivo fluorescence microimaging of the alveolar ducts and sacs during bronchoscopy , 2009, European Respiratory Journal.

[14]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Nikola Krstajic,et al.  Two-color widefield fluorescence microendoscopy enables multiplexed molecular imaging in the alveolar space of human lung tissue , 2016, Journal of biomedical optics.

[16]  Tom Kamiel Magda Vercauteren,et al.  In vivo imaging of the bronchial wall microstructure using fibered confocal fluorescence microscopy. , 2007, American journal of respiratory and critical care medicine.

[17]  Mathieu Salaün,et al.  Confocal fluorescence endomicroscopy of the human airways. , 2009, Proceedings of the American Thoracic Society.

[18]  Rebecca Richards-Kortum,et al.  High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging , 2011, Journal of visualized experiments : JoVE.

[19]  A. Akram,et al.  A labelled-ubiquicidin antimicrobial peptide for immediate in situ optical detection of live bacteria in human alveolar lung tissue† †Electronic supplementary information (ESI) available: Experimental details and Fig. S1–S5. See DOI: 10.1039/c5sc00960j , 2015, Chemical science.

[20]  Tom Vercauteren,et al.  Image registration and mosaicing for dynamic In vivo fibered confocal microscopy : Image Registration and Mosaicing for Dynamic In Vivo Fibered Confocal Microscopy. (Recalage et mosaïques d'images pour la microscopie confocale fibrée dynamique in vivo) , 2008 .

[21]  Alberto Del Bimbo,et al.  The Mesh-LBP: Computing Local Binary Patterns on Discrete Manifolds , 2013, 2013 IEEE International Conference on Computer Vision Workshops.