Photoacoustic Remote Sensing (PARS™), an all-optical, non-contact, reflection-mode, label-free biomedical imaging modality, is sensitive to optical absorption in biological tissues. Images are formed by scanning across tissue, collecting time-domain signals at each point which represent the initial pressures generated through induced thermoelastic expansion. Previously, when obtaining pixel intensity, the rich temporal and spectral content of the time-domain signals has largely been ignored. In this exploratory work, meaningful features are intelligently extracted from the time-domain signals. Based on these extracted features, attributes relevant to pixel intensity can be used to form images of improved contrast and greater detail.
Observing the circular dichroism (CD) caused by organic molecules in biological fluids can provide powerful indicators of patient health and provide diagnostic clues for treatment. Methods for this kind of analysis involve tabletop devices that weigh tens of kilograms with costs on the order of tens of thousands of dollars, making them prohibitive in point-of-care diagnostic applications. In an e ort to reduce the size, cost, and complexity of CD estimation systems for point-of-care diagnostics, we propose a novel method for CD estimation that leverages a vortex half-wave retarder in between two linear polarizers and a two-dimensional photodetector array to provide an overall complexity reduction in the system. This enables the measurement of polarization variations across multiple polarizations after they interact with a biological sample, simultaneously, without the need for mechanical actuation. We further discuss design considerations of this methodology in the context of practical applications to point-of-care diagnostics.
An ideal laser is a useful tool for the analysis of biological systems. In particular, the polarization property of lasers can allow for the concentration of important organic molecules in the human body, such as proteins, amino acids, lipids, and carbohydrates, to be estimated. However, lasers do not always work as intended and there can be effects such as mode hopping and thermal drift that can cause time-varying intensity fluctuations. The causes of these effects can be from the surrounding environment, where either an unstable current source is used or the temperature of the surrounding environment is not temporally stable. This intensity fluctuation can cause bias and error in typical organic molecule concentration estimation techniques. In a low-resource setting where cost must be limited and where environmental factors, like unregulated power supplies and temperature, cannot be controlled, the hardware required to correct for these intensity fluctuations can be prohibitive. We propose a method for computational laser intensity stabilisation that uses Bayesian state estimation to correct for the time-varying intensity fluctuations from electrical and thermal instabilities without the use of additional hardware. This method will allow for consistent intensities across all polarization measurements for accurate estimates of organic molecule concentrations.
Fluorescent imaging, often synonymous with microscopic imaging, is an imaging modality whereby various features of a target are observed based on assignment of chemical labels. These labels are in most cases indirect tracers of specific structures or chemical compounds which cannot be otherwise identified. The tracers are excited by an illuminating source and they in turn emit light at specific wavelengths. This light is then captured by an imaging device and represented as an indirect observation of the specific feature in the sample. The process of excitation and imaging of the emitted light is performed sequentially and is proportional to the number of tracers or fluorescence species present in the sample. We present an imaging system that can image fluorescent tracers, in the visible and the near Infra-red, simultaneously. This system is capable of illuminating the target with different excitation light sources and capture the corresponding fluorescence images in one snapshot using a series of mirrors to capture different views of the sample. The simultaneously captured image are fused using a computational reconstruction process to present a coherent multispectral fluorescence image. The system is proposed for use in applications where the rapid enumeration of fluorescent species in a large field of view is paramount as opposed to their microscopic image in a narrow field of view. The system was tested using a controlled cocktail solution of four different types fluorescent microspheres and was able to enumerate the microspheres based on their different fluorescent signatures as captured by the system.
Safe drinking water is essential for human health, yet over a billion people worldwide do not have access to safe drinking water. Due to the presence and accumulation of biological contaminants in natural waters (e.g., pathogens and neuro-, hepato-, and cytotoxins associated with algal blooms) remain a critical challenge in the provision of safe drinking water globally. It is not financially feasible and practical to monitor and quantify water quality frequently enough to identify the potential health risk due to contamination, especially in developing countries. We propose a low-cost, small-profile multispectral (MS) system based on Digital Holographic Microscopy (DHM) and investigate methods for rapidly capturing holographic data of natural water samples. We have developed a test-bed for an MSDHM instrument to produce and capture holographic data of the sample at different wavelengths in the visible and the near Infra-red spectral region, allowing for resolution improvement in the reconstructed images. Additionally, we have developed high-speed statistical signal processing and analysis techniques to facilitate rapid reconstruction and assessment of the MS holographic data being captured by the MSDHM instrument. The proposed system is used to examine cyanobacteria as well as Cryptosporidium parvum oocysts which remain important and difficult to treat microbiological contaminants that must be addressed for the provision of safe drinking water globally.
Polarimetry is a common technique used in chemistry for solution characterization and analysis, giving insight into the molecular structure of a solution measured through the rotation of linearly polarized light. This rotation is characterized by the Boits law. Without large optical path lengths, or high concentrations of solution, these optical rotations are typically very small, requiring elaborate and costly apparatuses. To ensure that the rotation measurements are accurate, these devices usually perform complex optical procedures or time-averaged point measurements to ensure that any intensity variation seen is a product of optical rotation and not from inherent noise sources in the system, such as sensor or shot noise. Time averaging is a lengthy process and rarely utilizes all of the information available on the sensor. To this end, we have developed a novel integrated, miniature, computational imaging system that enhances polarimetric measurements by taking advantage of the full spot size observed on an array detector. This computational imaging system is capable of using a single acquisition at unity gain to enhance the polarimetric measurements using a probabilistic framework, which accounts for inherent noise and optical characteristics in the acquisition process, to take advantage of spatial intensity relations. This approach is faster than time-averaging methods and can better account for any measurement uncertainties. In preliminary experiments, this system has produced comparably consistent measurements across multiple trials with the same chemical solution than time averaging techniques.
Block-transform lossy image compression is the most widely-used approach for compressing and storing images or video. A novel algorithm to restore highly compressed images with greater image quality is proposed. Since many block-transform coefficients are reduced to zero after quantization, the compressed image restoration problem can be treated as a sparse reconstruction problem where the original image is reconstructed based on sparse, degraded measurements in the form of highly quantized block-transform coefficients. The sparse reconstruction problem is solved by minimizing a homotopic regularized function, subject to data fidelity in the block-transform domain. Experimental results using compressed natural images at di erent levels of compression show improved performance by using the proposed algorithm compared to other methods.
Mobile robots that rely on vision, for navigation and object detection, use saliency approaches to identify a set
of potential candidates to recognize. The state of the art in saliency detection for mobile robotics often rely upon
visible light imaging, using conventional camera setups, to distinguish an object against its surroundings based
on factors such as feature compactness, heterogeneity and/or homogeneity. We are demonstrating a novel multi-
polarimetric saliency detection approach which uses multiple measured polarization states of a scene. We leverage
the light-material interaction known as Fresnel reflections to extract rotationally invariant multi-polarimetric
textural representations to then train a high dimensional sparse texture model. The multi-polarimetric textural
distinctiveness is characterized using a conditional probability framework based on the sparse texture model
which is then used to determine the saliency at each pixel of the scene. It was observed that through the
inclusion of additional polarized states into the saliency analysis, we were able to compute noticeably improved
saliency maps in scenes where objects are difficult to distinguish from their background due to color intensity
similarities between the object and its surroundings.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.