Medical imaging plays a critical role in cancer diagnosis and planning. Many of these patients rely on surgical intervention for curative outcomes. This requires a careful identification of the primary and microscopic tumors, and the complete removal of cancer. Although there have been efforts to adapt traditional imaging modalities for intraoperative image guidance, they suffer from several constraints such as large hardware footprint, high operation cost, and disruption of the surgical workflow. Because of the ease of image acquisition, relatively low cost devices and intuitive operation, optical imaging methods have received tremendous interests for use in real-time image-guided surgery. To improve imaging depth under low interference by tissue autofluorescence, many of these applications utilize light in the near-infra red (NIR) wavelengths, which is invisible to human eyes. With the availability of a wide selection of tumor-avid contrast agents, advancements in imaging sensors, electronic and optical designs, surgeons are able to combine different attributes of NIR optical imaging techniques to improve treatment outcomes. The emergence of diverse commercial and experimental image guidance systems, which are in various stages of clinical translation, attests to the potential high impact of intraoperative optical imaging methods to improve speed of oncologic surgery with high accuracy and minimal margin positivity.

This paper presents bilinear and bicubic interpolation methods tailored for the division of focal plane polarization imaging sensor. The interpolation methods are targeted for a 1-Mega pixel polarization imaging sensor operating in the visible spectrum. The five interpolation methods considered in this paper are: bilinear, weighted bilinear, bicubic spline, an approximated bicubic spline and a bicubic interpolation method. The modulation transfer function analysis is applied to the different interpolation methods, and test images as well as numerical error analyses are also presented. Based on the comparison results, the full frame bicubic spline interpolation achieves the best performance for polarization images.

In this paper, we present recent work on bioinspired polarization imaging sensors and their applications in biomedicine. In particular, we focus on three different aspects of these sensors. First, we describe the electro–optical challenges in realizing a bioinspired polarization imager, and in particular, we provide a detailed description of a recent low-power complementary metal–oxide–semiconductor (CMOS) polarization imager. Second, we focus on signal processing algorithms tailored for this new class of bioinspired polarization imaging sensors, such as calibration and interpolation. Third, the emergence of these sensors has enabled rapid progress in characterizing polarization signals and environmental parameters in nature, as well as several biomedical areas, such as label-free optical neural recording, dynamic tissue strength analysis, and early diagnosis of flat cancerous lesions in a murine colorectal tumor model. We highlight results obtained from these three areas and discuss future applications for these sensors.

The inability to identify microscopic tumors and assess surgical margins in real-time during oncologic surgery leads to incomplete tumor removal, increases the chances of tumor recurrence, and necessitates costly repeat surgery. To overcome these challenges, we have developed a wearable goggle augmented imaging and navigation system (GAINS) that can provide accurate intraoperative visualization of tumors and sentinel lymph nodes in real-time without disrupting normal surgical workflow. GAINS projects both near-infrared fluorescence from tumors and the natural color images of tissue onto a head-mounted display without latency. Aided by tumor-targeted contrast agents, the system detected tumors in subcutaneous and metastatic mouse models with high accuracy (sensitivity = 100%, specificity = 98% ± 5% standard deviation). Human pilot studies in breast cancer and melanoma patients using a near-infrared dye show that the GAINS detected sentinel lymph nodes with 100% sensitivity. Clinical use of the GAINS to guide tumor resection and sentinel lymph node mapping promises to improve surgical outcomes, reduce rates of repeat surgery, and improve the accuracy of cancer staging.

Recent advancements in nanotechnology and nanofabrication have allowed for the emergence of the division-of-focal-plane (DoFP) polarization imaging sensors. These sensors capture polarization properties of the optical field at every imaging frame. However, the DoFP polarization imaging sensors suffer from large registration error as well as reduced spatial-resolution output. These drawbacks can be improved by applying proper image interpolation methods for the reconstruction of the polarization results. In this paper, we present a new gradient-based interpolation method for DoFP polarimeters. The performance of the proposed interpolation method is evaluated against several previously published interpolation methods by using visual examples and root mean square error (RMSE) comparison. We found that the proposed gradient-based interpolation method can achieve better visual results while maintaining a lower RMSE than other interpolation methods under various dynamic ranges of a scene ranging from dim to bright conditions.