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Human pathological samples, including skin ( Gambichler et al., 2005, 2011), There has been extensive research on using OCT/OCM images for analysis of Numerical aperture objectives to provide cellular resolution images ( Izatt et al., 1994 Aguirre et al., 2010b, a Ahsen et al., 2013 Lee et al., 2013). Microscopy (OCM) combines the advantages of OCT and confocal microscopy using high Non-invasive optical imaging modality that can provide 3D, high-resolution images ofД«iological tissue architectures without staining ( Huang et al., 1991 Fujimoto,Дў003 Fujimoto et al., 2000 Tearney et al., 1997b).

Optical coherence tomography (OCT) provides an alternative Tissue is processed to extremely thin slices and stained before being observed underДЄ microscope. Microscope to determine the presence or extent of a disease. Samples are removed from a human subject and then examined by a pathologist under a Biopsy is a frequently used medical test in which tissue Improves with screening and early detection ( BoyleД®t al., 2008). It accounted forДЄn estimated 40,000 deaths and 232,670 new cases in the United States in 2014 ( American Cancer Society, 2014 Siegel et al., 2014). Selection are also effective mechanisms to improve accuracy while maintainingД«reast cancer is a high-incidence cancer in women worldwide. Using features from multiple scales and performing feature Several other experiments alsoДemonstrate the complementary nature of LBP and the two variants (ALBP and BLBPДЇeatures) and the significance of integrating these texture features forД¬lassification. A sensitivity level ofДЎ00% and specificity level of 85.2% was achieved toДifferentiate benign from malignant samples. Obtained with an area under the curve value of 0.959. A receiver operating characteristic (ROC) curve was The integrated feature was also used to classify large-field OCM Our experiments show thatД«y integrating a selected set of LBP and the two new variant (ALBP and BLBP)ДЇeatures at multiple scales, the classification accuracy increased fromДЁ1.7% (using LBP features alone) to 93.8% using a neural networkД¬lassifier. (regions outside of the specimens, n = 572). Labeled with one of the five different classes: adipose tissue (n =ДЈ47), fibrous stroma (n = 2,065), breast lobules (n = 199),Д¬arcinomas (pooled from all sub-types, n = 1,127), and background 4310 small OCM imageД«locks (500 Г— 500 pixels) each paired withД¬orresponding H&E histology was extracted from large-field OCM images and Corresponding H&E histology was obtained forД®ach sample and used to provide ground truth diagnosis. Lobular carcinoma in situ) were imaged with large field OCM with an imaging areaД 00 pixels) for each sample. invasive ductal carcinoma, ductal carcinoma in situ and fibroadenoma, fibrocystic disease and usual ductal hyperplasia) and 19Д«reast carcinoma (e.g. Neighboring pixels and among certain blocks of pixels in the neighborhood.ДЇourty-six freshly excised human breast tissue samples, including 27 benign Structure in a local neighborhood by looking at intensity differences among LBP feature, ALBP and BLBP features provide an enhanced encoding of the texture Namely average LBP (ALBP) and block based LBP (BLBP). Improve classification accuracy, we propose novel variants of LBP features, WeД®xamined multiple types of texture features and found Local Binary Pattern (LBP)ДЇeatures to perform better in classifying tissues imaged by OCM. However, pose unique challenges to image analysis and pattern classification. OCM images, acquired without tissue staining, OCM is an emerging imaging modality for rapid tissue screeningДЄnd has the potential to provide high resolution microscopic images thatДЄpproach those of histology. This paper proposes a texture analysis technique that can effectivelyД¬lassify different types of human breast tissue imaged by Optical Coherence

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