In breast cancer, 20%–30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recurrence, accurate detection of the cancer margin by the clinician or surgeons needs some assistance. In this paper, an automated volumetric analysis of normal and breast cancer tissue is done by a machine learning algorithm to separate them into two classes. The proposed method is based on a support-vector-machine-based classifier by dissociating 10 features extracted from the A-line, texture, and phase map by the swept-source optical coherence tomographic intensity and phase images. A set of 88 freshly excised breast tissue [44 normal and 44 cancers (invasive ductal carcinoma tissues)] samples from 22 patients was used in our study. The algorithm successfully classifies the cancerous tissue with sensitivity, specificity, and accuracy of 91.56%, 93.86%, and 92.71% respectively. The present computational technique is fast, simple, and sensitive, and extracts features from the whole volume of the tissue, which does not require any special tissue preparation nor an expert to analyze the breast cancer as required in histopathology. Diagnosis of breast cancer by extracting quantitative features from optical coherence tomographic images could be a potentially powerful method for cancer detection and would be a valuable tool for a fine-needle-guided biopsy.

Single Shot White Light Interference Microscopy for 3D Surface Profilometry Using Single Chip Color Camera Interferometry;Profilometry;Microscopy; We present a single shot low coherence white light Hilbert phase microscopy (WL-HPM) for quantitative phase imaging of Si optoelectronic devices, i.e., Si integrated circuits (Si-ICs) and Si solar cells. White light interferograms were recorded by a color CCD camera and the interferogram is decomposed into the three colors red, green and blue. Spatial carrier frequency of the WL interferogram was increased sufficiently by means of introducing a tilt in the interferometer. Hilbert transform fringe analysis was used to reconstruct the phase map for red, green and blue colors from the single interferogram. 3D step height map of Si-ICs and Si solar cells was reconstructed at multiple wavelengths from a single interferogram. Experimental results were compared with Atomic Force Microscopy and they were found to be close to each other. The present technique is non-contact, full-field and fast for the determination of surface roughness variation and morphological features of the objects at multiple wavelengths.