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.