Morphological image analysis for classification of gastrointestinal tissues using optical coherence tomography

P. Beatriz Garcia-Allende; Iakovos Amygdalos; Hiruni Dhanapala; Robert D. Goldin; George B. Hanna; Daniel S. Elson

doi:10.1117/12.907835

30 January 2012 Morphological image analysis for classification of gastrointestinal tissues using optical coherence tomography

P. Beatriz Garcia-Allende, Iakovos Amygdalos, Hiruni Dhanapala, Robert D. Goldin, George B. Hanna, Daniel S. Elson

Author Affiliations +

Proceedings Volume 8213, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XVI; 821328 (2012) https://doi.org/10.1117/12.907835
Event: SPIE BiOS, 2012, San Francisco, California, United States

Abstract

Computer-aided diagnosis of ophthalmic diseases using optical coherence tomography (OCT) relies on the extraction of thickness and size measures from the OCT images, but such defined layers are usually not observed in emerging OCT applications aimed at "optical biopsy" such as pulmonology or gastroenterology. Mathematical methods such as Principal Component Analysis (PCA) or textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric auto-correlation (CSAC) and spatial grey-level dependency matrices (SGLDM), as well as, quantitative measurements of the attenuation coefficient have been previously proposed to overcome this problem. We recently proposed an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technology for feature quantification. OCT images were first segmented in the axial direction in an automated manner according to intensity. Afterwards, a morphological analysis of the segmented OCT images was employed for quantifying the features that served for tissue classification. In this study, a PCA processing of the extracted features is accomplished to combine their discriminative power in a lower number of dimensions. Ready discrimination of gastrointestinal surgical specimens is attained demonstrating that the approach further surpasses the algorithms previously reported and is feasible for tissue classification in the clinical setting.

Citation Download Citation

P. Beatriz Garcia-Allende, Iakovos Amygdalos, Hiruni Dhanapala, Robert D. Goldin, George B. Hanna, and Daniel S. Elson "Morphological image analysis for classification of gastrointestinal tissues using optical coherence tomography", Proc. SPIE 8213, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XVI, 821328 (30 January 2012); https://doi.org/10.1117/12.907835

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KEYWORDS

Image segmentation

Optical coherence tomography

Tissues

Principal component analysis

Image classification

Feature extraction

Stomach

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