Worldwide, a considerable number of female cancer cases are attributed to breast cancer, making it a prevalent and serious problem. As diagnoses surge, the traditional approach of manual histological assessment is becoming increasingly inefficient. So, to expedite diagnosis and eliminate the need for specialised expertise, researchers are turning to automated alternatives. Polarization-Sensitive Optical coherence tomography (PS-OCT) emerges as a promising tool, offering a rapid alternative to traditional histology. It stands out by exploiting the polarization of reflected light to boost image contrast. By evaluating polarized backscattered light, the PS-OCT system is able to detect birefringence in cancerous tissue, indicative of collagen changes associated with cancer. The main focus of this study is the development of an automated Full-field PS-OCT (FF-PS-OCT) system for the diagnosis of breast cancer. The system recorded 220 sample images in order to extract phase information. The birefringence and degree of polarization uniformity information is calculated from the recorded phase images. Different features have been extracted from birefringence and degree of polarization uniformity images to train an ensemble model that has been validated by the technique for order preference by similarity to ideal solution (TOPSIS) to distinguish between normal and malignant breast tissue. The multi-layer ensemble model demonstrates enhanced performance in terms of recall and precision, achieving remarkable metrics on the testing dataset: 92.3% precision, 90% recall, 91.1% F-score, and 79.7% Matthews correlation coefficient. These preliminary results underscore the potential of FF-PS-OCT as rapid, non-contact, and label-free imaging tool. Its implementation shows potential in empowering medical professionals with the insights needed for making informed decisions during interventions.
We developed an automated high-resolution full-field spatial coherence tomography (FF-SCT) microscope for quantitative phase imaging that is based on the spatial, rather than the temporal, coherence gating. The Red and Green color laser light was used for finding the quantitative phase images of unstained human red blood cells (RBCs). This study uses morphological parameters of unstained RBCs phase images to distinguish between normal and infected cells. We recorded the single interferogram by a FF-SCT microscope for red and green color wavelength and average the two phase images to further reduced the noise artifacts. In order to characterize anemia infected from normal cells different morphological features were extracted and these features were used to train machine learning ensemble model to classify RBCs with high accuracy.
To quantitatively obtain the phase map of Onion and human red blood cell (RBC) from white light interferogram we used Hilbert transform color fringe analysis technique. The three Red, Blue and Green color components are decomposed from single white light interferogram and Refractive index profile for Red, Blue and Green colour were computed in a completely non-invasive manner for Onion and human RBC. The present technique might be useful for non-invasive determination of the refractive index variation within cells and tissues and morphological features of sample with ease of operation and low cost.
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