A texture analysis technique improved classification of different types of breast tissue imaged with optical coherence microscopy (OCM), according to results of a study published in Medical Image Analysis.

OCM is a combination of optical coherence tomography and confocal microscopy. It provides improved penetration depth compared with confocal microscopy and better resolution than optical coherence tomography.

The imaging technique is emerging as a modality that could quicken tissue screening and provide resolution nearly as high as that in histology without the need to stain the tissue.

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The researchers examined several types of texture features from 46 breast tissue samples; 27 were benign tissue (eg, fibroadenoma and usual ductal hyperplasia) and 19 were malignant (eg, invasive ductal carcinoma and carcinoma in situ).

The samples were viewed using large-field OCM. Corresponding histological analyses provided ground truth diagnoses.

In total, 4310 small OCM image blocks of 500 by 500 pixels, each paired with histology, were extracted from the large-field OCM images. Analyses labeled the smaller images as fat (n = 347), fibrous stroma (n = 2065), breast lobules (n = 199), carcinomas (n = 1127), and background (n = 572).

OCM can be used with a technique called local binary pattern (LBP). Here, researchers also determined accuracy of average LBP (ALBP) and block-based LBP (BLBP). Both imaging techniques demonstrated improved textural analyses.

By integrating ALBP and BLBP with LBP, the accuracy of classification improved from 81.7% with LBP only to 93.8% with a combination. Additional analyses indicated a sensitivity of 100% and specificity of 85.2% in differentiating benign from malignant tissue.


Wan S, Lee HC, Huang X, et al. Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy [published online March 8, 2017]. Med Image Anal. doi: 10.1016/j.media.2017.03.002