Quantification and influence of skin chromophores for remote detection of anemic conditions

Current standards for diagnosing and monitoring anemia are relatively invasive. The superficial symptoms of this condition are due to an underlying deficiency of red blood cells (RBC) or erythrocytes, and hemoglobin in the blood. This results in an inadequate supply of oxygen to the body’s tissues. For point-of-care diagnostic systems, remote determination of blood conditions will depend on an understanding of the interaction of light with hemoglobin. However, the skin acts as the first barrier for this detection. In this study, we pursue the possibility of detecting anemic conditions from the perfused blood in the dermis using optical models and Monte Carlo (MC) methods. The skin is composed of two primary layers, the epidermis and the dermis. The avascular epidermis absorbs light due to its primary chromophore, melanin. Subsequently, the absorption in the dermis layer is quantified by hematocrit and hemoglobin concentrations. Two-layer models of the human skin are set up and optical properties are assigned to these models. The optical variability across these models are defined by six melanin (epidermis) and two erythrocytes (dermis) concentrations. The twelve combinations of optical properties are assessed at six wavelengths of interest in the Virtual Tissue Simulator (VTS) environment. The chosen wavelengths range across the visible and near-infrared spectrum, which is a known and important diagnostic window for biological tissues. In this study, we explore the variability of light interactions for healthy and anemic blood conditions quantified in the dermis while accounting for variable melanin concentrations in the epidermis

Application of spectral and spatial indices for specific class identification in Airborne Prism EXperiment (APEX) imaging spectrometer data for improved land cover classification

Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c–Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and 59.50% (optimally determined principal component

Progress towards recalibration of spectrographs

The spectral resolution of a spectrograph depends on the input slit width, the diffraction grating grooves and the number of imaging sensor/detector pixels. Due to the proprietary nature of spectrograph designs, recalibration by end-users can be challenging. Most calibration procedures currently published are applicable to in-house instruments or spectrographs with access to the internal specifications. Narrowing the input slit improves the resolution but also reduces the throughput of the imaging system. We attempted to recalibrate an Offner-based spectrograph by using a larger detector plane (an imaging system with a larger sensor), to vary the distance along the focal plane; and by utilising lens optics. Basic experiments were conducted by varying the distance from the exit window and inserting a lens to magnify the spectrograph output onto the larger detector plane. We concluded that the calibration could not be achieved using simple optics within the scope of our experiments. This article addresses a gap in literature that does not present the research community with the unsuccessful steps that are not applicable to similar problem statements. The alternative would be to rely on reflective optics, but this approach may reduce portability

Implications of spectral and spatial features to improve the identification of specific classes

Dimensionality is one of the greatest challenges when deciphering hyperspectral imaging data. Although the multiband nature of the data is beneficial, algorithms are faced with a high computational load and statistical incompatibility due to the insufficient number of training samples. This is a hurdle to downstream applications. The combination of dimensionality and the real-world scenario of mixed pixels makes the identification and classification of imaging data challenging. Here, we address the complications of dimensionality using specific spectral indices from band combinations and spatial indices from texture measures for classification to better identify the classes. We classified spectral and combined spatial–spectral data and calculated measures of accuracy and entropy. A reduction in entropy and an overall accuracy of 80.50% was achieved when using the spectral–spatial input, compared with 65% for the spectral indices alone and 59.50% for the optimally determined principal components

Compressed sensing in the far-field of the spatial light modulator in high noise conditions

Single-pixel imaging techniques as an alternative to focal-plane detector arrays are being widely investigated. The interest in these single-pixel techniques is partly their compatibility with compressed sensing but also their applicability to spectral regions where focal planes arrays are simply not obtainable. Here, we show how a phased-array modulator source can be used to create Hadamard intensity patterns in the far-field, thereby enabling single-pixel imaging. Further, we successfully illustrate an implementation of compressed sensing for image reconstruction in conditions of high noise. In combination, this robust technique could be applied to any spectral region where spatial light phase modulators or phased-array sources are available

Modular light sources for microscopy and beyond (ModLight)

Modular light (ModLight) sources can be integrated into complex systems for microscopy, medical imaging, remote sensing, and many more. Motivated by the need for affordable and open-access alternatives that are globally relevant, we have designed and presented light devices that use simple, off-the-shelf components. Red, green, blue, white and near-infrared LEDs are combined using mirrors and X-Cube prisms in novel devices. This modular nature allows portability and mounting flexibility. The ModLight suite can be used with any optical system that requires single- or multi-wavelength illumination such as bright-field and epifluorescence microscopes

Modular light sources for microscopy and beyond (ModLight)

Modular light (ModLight) sources can be integrated into complex systems for microscopy, medical imaging, remote sensing, and many more. Motivated by the need for affordable and open-access alternatives that are globally relevant, we have designed and presented light devices that use simple, off-the-shelf components. Red, green, blue, white and near-infrared LEDs are combined using mirrors and X-Cube prisms in novel devices. This modular nature allows portability and mounting flexibility. The ModLight suite can be used with any optical system that requires single- or multi-wavelength illumination such as bright-field and epifluorescence microscopes

Open-source microscopic solution for classification of biological samples

Open-source technologies and solutions have paved the way for making science accessible the world over. Motivated to contribute to the direction of open-source methods, our current research presents a complete workflow of building a microscope using 3D printing and easily accessible optical components to collect images of biological samples. Further, these images are classified using machine learning algorithms to illustrate both the effectiveness of this method and show the disadvantages of classifying images that are visually similar. The second outcome of this research is an openly accessible dataset of the images collected, OPEN-BIOset, and made available to the machine learning community for future research. The research adopts the OpenFlexure Delta Stage microscope (https://openflexure.org/) that allows motorised control and maximum stability of the samples when imaging. A Raspberry Pi camera is used for imaging the samples in a transmission-based illumination setup. The imaging data collected is catalogued and organised for classification using TensorFlow. Using visual interpretation, we have created subsets from amongst the samples to experiment for the best classification results. We found that by removing similar samples, the categorical accuracy achieved was 99.9% and 99.59% for the training and testing sets. Our research shows evidence of the efficacy of open source tools and methods. Future approaches will use improved resolution images for classification and other modalities of microscopy will be realised based on the OpenFlexure microscope

Challenging point scanning across electron microscopy and optical imaging using computational imaging

Solving challenges of enhanced imaging (resolution or speed) is a continuously changing frontier of research. Within this sphere, ghost imaging (and the closely related single-pixel imaging) has evolved as an alternative to focal plane detector arrays owing to advances in detectors and/or modulation devices. The interest in these techniques is due to their robustness to varied sets of patterns and applicability to a broad range of wavelengths and compatibility with compressive sensing. To achieve a better control of illumination strategies, modulators of many kinds have long been available in the optical regime. However, analogous technology to control of phase and amplitude of electron beams does not exist. We approach this electron microscopy challenge from an optics perspective, with a novel approach to imaging with non-orthogonal pattern sets using ghost imaging. Assessed first in the optical regime and subsequently in electron microscopy, we present a methodology that is applicable at different spectral regions and robust to non-orthogonality. The distributed illumination pattern sets also result in a reduced peak intensity, thereby potentially reducing damage of samples during imaging. This imaging approach is potentially translatable beyond both regimes explored here, as a single-element detector system