Binary tissue classification studies on resected human breast tissues using optical coherence tomography images

We report the results of a comparative study of Fourier domain analysis (FDA) and texture analysis (TA) of optical coherence tomography (OCT) images of resected human breast tissues for binary classification between normal-abnormal classes and benign-malignant classes. With the incorporation of Fisher linear discriminant analysis (FLDA) in TA for feature extraction, the TA-based algorithm provided improved diagnostic performance as compared to the FDA-based algorithm in discriminating OCT images corresponding to breast tissues with three different pathologies. The specificity and sensitivity values obtained for normal-abnormal classification were both 100%, whereas they were 90% and 85%, respectively for benign-malignant classification