Liquid Crystal Tunable Filter Research Articles

Compressive spectral imaging system based on liquid crystal tunable filter.

Liquid crystal tunable filters (LCTF) are extensively used in hyperspectral imaging systems to successively acquire different spectral components of scenes by adjusting the center wavelength of the filter. However, the spectral and spatial resolutions of the imager are limited by the bandwidth of LCTF, and the pitch dimension of the detector, respectively. This paper applies compressive sensing principles to improve both of the spatial and spectral resolutions of the LCTF-based hyperspectral imaging system. An accurate transmission model of the LCTF is used to represent its bandpass filtering effects on the spectra. In addition, a random coded aperture placed behind the LCTF is used to modulate the spectral images in the spatial domain. Then, the three-dimensional encoded spectral images are projected onto a two-dimensional detector. Benefiting from the spectral-dependent transmission property of the LCTF, information of the entire spectrum is collected by a few snapshots using different center wavelengths of the LCTF. Super-resolution hyperspectral images can be reconstructed from a small set of compressive measurements by solving a convex optimization problem. Simulations and experiments show that the proposed method can effectively improve the spectral and spatial resolutions of traditional LCTF-based spectral imager without changing the structures of the LCTF and detector. Finally, a multi-channel spectral coding method is proposed to further increase the compression capacity of the system.

Application driven key wavelengths mining method for aflatoxin detection using hyperspectral data

The first step of developing Aflatoxin intelligent sorter is to determine the key wavelengths for aflatoxin detection. In order to find more accurate wavelengths, in this paper, three kinds of sensor system are built separately: the first sensor is hyper-spectrometer by ASD spectrometer, the second is the multispectral camera system based on Liquid crystal tunable filter (LCTF) and the third one is the hyperspectral camera based on Grating spectrometer module (GSM). Under 365 nm UV LED illumination, using these three systems, three hypersepctral datasets of 45, 41 and 73 peanut samples before and after aflatoxin contaminated have be collected separately. In order to select the best key wavelengths, four feature selection methods (Fisher, SPA, BestFist and Ranker) and four classifier models (KNN, SVM, BP-ANN, RandomForest) were analyzed and compared. Using all selected wavelengths based on different datasets, a weighted voting method was proposed and 10 key wavelengths (440 380 410 460 420 370 450 490 700 600 nm) were selected. Based on the best model (Random Forest), the best integrated average recognition rate is 94.5%. And then, using these key wavelengths and the best classification model, a new design system for aflatoxin sorter base on a Ploygon mirror was proposed. The structure of this system is simple, detection accuracy is high, which can be applied to online sorting of aflatoxin detection.

A practical wide-field Raman imaging method with high spectral and spatial resolution.

Raman imaging has a great advantage in characterizing inhomogeneous systems. A practical wide-field Raman imaging platform is developed that shows major improvements on imaging speed, sensitivity, and resolution. Different from the traditional Raman imaging systems using the wavelength-fixed lasers and the chromatic dispersion devices, this system adopts an inverted architecture, integrated with a tunable laser and the wavelength-fixed filters. Owing to the high transmission of the fixed filters, the imaging sensitivity can be improved 5-10 times in comparison to the present wide-field Raman imaging setups using liquid-crystal tunable filters. Via combining with the high-power tunable laser, Raman images could be obtained in minutes and the Raman shift of the images could be tuned easily and accurately. The resolution of this system can reach 1.5 cm-1 in the spectrum and 490 nm in space, which could provide more fingerprint details of the analytes. This effective Raman imaging method allowing us to see chemical spatial variations on microscale is anticipated to be widely applied in scientific research fields.

Transmission surface plasmon resonance image detection by a smartphone camera

Abstract

Measuring the Human Ultra-Weak Photon Emission Distribution Using an Electron-Multiplying, Charge-Coupled Device as a Sensor

Ultra-weak photon emission (UPE) is the spontaneous emission from living systems mainly attributed to oxidation reactions, in which reactive oxygen species (ROS) may play a major role. Given the capability of the next-generation electron-multiplying CCD (EMCCD) sensors and the easy use of liquid crystal tunable filters (LCTF), the aim of this work was to explore the potential of a simple UPE spectrometer to measure the UPE from a human hand. Thus, an easy setup was configured based on a dark box for inserting the subject’s hand prior to LCTF as a monochromator and an EMCCD sensor working in the full vertical binning mode (FVB) as a spectra detector. Under controlled conditions, both dark signals and left hand UPE were acquired by registering the UPE intensity at different selected wavelengths (400, 450, 500, 550, 600, 650, and 700 nm) during a period of 10 min each. Then, spurious signals were filtered out by ignoring the pixels whose values were clearly outside of the Gaussian distribution, and the dark signal was subtracted from the subject hand signal. The stepped spectrum with a peak of approximately 880 photons at 500 nm had a shape that agreed somewhat with previous reports, and agrees with previous UPE research that reported UPE from 420 to 570 nm, or 260 to 800 nm, with a range from 1 to 1000 photons s−1 cm−2. Obtaining the spectral distribution instead of the total intensity of the UPE represents a step forward in this field, as it may provide extra information about a subject’s personal states and relationship with ROS. A new generation of CCD sensors with lower dark signals, and spectrographs with a more uniform spectral transmittance, will open up new possibilities for configuring measuring systems in portable formats.

HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing.

Although nano/microsatellites have great potential as remote sensing platforms, the spatial and spectral resolutions of an optical payload instrument are limited. In this study, a high spatial resolution multispectral sensor, the High-Precision Telescope (HPT), was developed for the RISING-2 microsatellite. The HPT has four image sensors: three in the visible region of the spectrum used for the composition of true color images, and a fourth in the near-infrared region, which employs liquid crystal tunable filter (LCTF) technology for wavelength scanning. Band-to-band image registration methods have also been developed for the HPT and implemented in the image processing procedure. The processed images were compared with other satellite images, and proven to be useful in various remote sensing applications. Thus, LCTF technology can be considered an innovative tool that is suitable for future multi/hyperspectral remote sensing by nano/microsatellites.

High Throughput AOTF Hyperspectral Imager for Randomly Polarized Light

The acousto-optic tunable filter (AOTF) is one of the most used techniques for hyperspectral imaging (HSI), and is capable of fast and random wavelength access, high diffraction efficiency, and good spectral resolution. Typical AOTF-HSI works with linearly polarized light; hence, its throughput is limited for randomly polarized applications such as fluorescence imaging. We report an AOTF-based imager design using both polarized components of the input light. The imager is designed to operate in the 450 to 800 nm region with resolutions in the range of 1.5–4 nm. The performance characterization results show that this design leads to 68% improvement in throughput for randomly polarized light. We also compared its performance against a liquid crystal tunable filter (LCTF)-based imager.

Identification of muskmelon seed variety using hyperspectral imaging technology combined with machine learning

Abstract: Liquid crystal tunable filter (LCTF) based on hyperspectral imaging technology combined with machine learning is developed to identify muskmelon seed variety rapidly and non-destructively. LCTF-based hyperspectral imaging system equipped with a cold ring LED source is constructed to acquire the reflectance spectra of the muskmelon seeds. Discriminating models based on support vector machine (SVM), linear discriminant analysis (LDA), and convolutional neural network (CNN) are then established to identify the seed variety with reflectance spectra as input. It is found that the LDA model achieved the highest classification accuracy of 100% for the test set while a relatively low value of 96% and 83% was obtained for the SVM and CNN model respectively. To improve classification accuracy of the model, data preprocessing (Savitzky-Golay smoothing and multiple scattering correction) and spectral feature extraction algorithm (successive projections algorithm and principal component analysis) were employed to treat the reflectance data. With these treatments, the classification accuracy of the test set was improved to the highest value of over 99% for the SVM model and 86% for the CNN model. The results showed that the LCTF-based hyperspectral imaging technology combined with machine learning was feasible to identify the muskmelon seed variety. Keywords: hyperspectral imaging; liquid crystal tunable filter; variety identification; muskmelon seed; support vector machine; linear discriminant analysis; convolution neural network DOI:  10.33440/j.ijpaa.20200303.93  Citation: Luo K L, Ye W C, Diao Y H, Zhao J, Chen W J, Liu H C, Long Y B. Identification of muskmelon seed variety using hyperspectral imaging technology combined with machine learning.  Int J Precis Agric Aviat, 2020; 3(3): 14–20.

A novel approach for vegetation classification using UAV-based hyperspectral imaging

The use of unmanned aerial vehicle (UAV)-based spectral imaging offers considerable advantages in high-resolution remote-sensing applications. However, the number of sensors mountable on a UAV is limited, and selecting the optimal combination of spectral bands is complex but crucial for conventional UAV-based multispectral imaging systems. To overcome these limitations, we adopted a liquid crystal tunable filter (LCTF), which can transmit selected wavelengths without the need to exchange optical filters. For calibration and validation of the LCTF-based hyperspectral imaging system, a field campaign was conducted in the Philippines during March 28–April 3, 2016. In this campaign, UAV-based hyperspectral imaging was performed in several vegetated areas, and the spectral reflectances of 14 different ground objects were measured. Additionally, the machine learning (ML) approach using a support vector machine (SVM) model was applied to the obtained dataset, and a high-resolution classification map was then produced from the aerial hyperspectral images. The results clearly showed that a large amount of misclassification occurred in shaded areas due to the difference in spectral reflectance between sunlit and shaded areas. It was also found that the classification accuracy was drastically improved by training the SVM model with both sunlit and shaded spectral data. As a result, we achieved a classification accuracy of 94.5% in vegetated areas.

Image processing pipeline for segmentation and material classification based on multispectral high dynamic range polarimetric images.

We propose a method for the capture of high dynamic range (HDR), multispectral (MS), polarimetric (Pol) images of indoor scenes using a liquid crystal tunable filter (LCTF). We have included the adaptive exposure estimation (AEE) method to fully automatize the capturing process. We also propose a pre-processing method which can be applied for the registration of HDR images after they are already built as the result of combining different low dynamic range (LDR) images. This method is applied to ensure a correct alignment of the different polarization HDR images for each spectral band. We have focused our efforts in two main applications: object segmentation and classification into metal and dielectric classes. We have simplified the segmentation using mean shift combined with cluster averaging and region merging techniques. We compare the performance of our segmentation with that of Ncut and Watershed methods. For the classification task, we propose to use information not only in the highlight regions but also in their surrounding area, extracted from the degree of linear polarization (DoLP) maps. We present experimental results which proof that the proposed image processing pipeline outperforms previous techniques developed specifically for MSHDRPol image cubes.

Multispectral open-air intraoperative fluorescence imaging.

Intraoperative fluorescence imaging informs decisions regarding surgical margins by detecting and localizing signals from fluorescent reporters, labeling targets such as malignant tissues. This guidance reduces the likelihood of undetected malignant tissue remaining after resection, eliminating the need for additional treatment or surgery. The primary challenges in performing open-air intraoperative fluorescence imaging come from the weak intensity of the fluorescence signal in the presence of strong surgical and ambient illumination, and the auto-fluorescence of non-target components, such as tissue, especially in the visible spectral window (400-650 nm). In this work, a multispectral open-air fluorescence imaging system is presented for translational image-guided intraoperative applications, which overcomes these challenges. The system is capable of imaging weak fluorescence signals with nanomolar sensitivity in the presence of surgical illumination. This is done using synchronized fluorescence excitation and image acquisition with real-time background subtraction. Additionally, the system uses a liquid crystal tunable filter for acquisition of multispectral images that are used to spectrally unmix target fluorescence from non-target auto-fluorescence. Results are validated by preclinical studies on murine models and translational canine oncology models.

Fluorescence depth estimation from wide-field optical imaging data for guiding brain tumor resection: a multi-inclusion phantom study.

Studies have shown that fluorescent agents demarcate tumor from surrounding brain tissue and offer intraoperative guidance during resection. However, visualization of fluorescence signal from tumor below the surgical surface or through the appearance of blood in the surgical field is challenging. We have previously described red light imaging techniques for estimating fluorescent depths in turbid media. In this study, we evaluate these methods over a broader range of fluorophore concentrations, and investigate the ability to resolve multiple fluorescent emissions in the same plane or at different depths along the axis of imaging. A tungsten halogen lamp is used as a broadband white light source for reflectance imaging. Fluorescence from Alexa Fluor 647 is excited with a 635 nm diode laser. Reflectance and fluorescence spectral data are gathered between 670 and 720 nm with the use of a liquid crystal tunable filter and recorded on a sCMOS camera. Results show that two fluorescent emissions can be resolved within 2 mm if they are in the same plane or within 3 mm if they are at different depths along the axis of imaging up to 6 mm below the surface.

Transmission surface plasmon resonance imaging of silver nanoprisms enhanced propagating surface plasmon resonance on a metallic grating structure

The localized surface plasmon resonance of in-plane dipole plasmon resonance of silver nanoprisms was used to facilitate transmission surface plasmon resonance (TSPR). The TSPR signal was detected by a camera coupled with liquid crystal tunable filters. This technique was further utilized for the multiple detection of hydrogen peroxide.

Development of a Multispectral Structured Illumination Reflectance Imaging (SIRI) System and Its Application to Bruise Detection of Apples

Abstract. SIRI is a promising new imaging modality for enhancing quality detection of food. A liquid-crystal tunable filter (LCTF)-based multispectral SIRI system was developed and used for selecting optimal wavebands to detect bruising in apples. Immediately after impact bruising, ‘Delicious’, ‘Royal Gala’, ‘Granny Smith’, and ‘Golden Delicious’ apples were imaged by the system over the spectral region of 650 to 950 nm with 20 nm increments under sinusoidally modulated illumination at a spatial frequency of 100 cycles m-1. Each sample was subjected to two phase-shifted sinusoidal patterns of illumination with phase offsets of 0 and 2p/3 that were generated by a digital light projector. For comparison, spectral images were also captured under conventional uniform illumination. Spiral phase transform, a newly developed two-phase based demodulation method, was then used to retrieve amplitude component (AC) and direct component (DC) images from the SIRI images, from which ratio images were obtained by dividing the AC images by the DC images. It was found that the uniform illumination images failed to reveal the bruises in apples, whereas bruises were distinctly visible in the ratio images, with contrast varying with wavelength. Principal component analysis (PCA) showed that seven wavelengths from 710 to 830 nm were more relevant to bruise detection. A modified Otsu thresholding method based on the between-class variance was proposed for bruise segmentation from the ratio images at each of the seven wavelengths as well as the first principal component (PC1) images, which resulted in overall detection errors of 11.7% to 14.2%. This study has shown the potential of using a multispectral SIRI system for defect detection of fruit. Further research is needed to develop a general algorithm for defect detection of apples and upgrade the system toward real-time detection. Keywords: Defects, Detection, Fruit, Image analysis, LCTF, Structured illumination.

Fast spectral surface plasmon resonance imaging sensor for real-time high-throughput detection of biomolecular interactions.

A fast surface plasmon resonance (SPR) imaging biosensor system based on wavelength interrogation using a liquid crystal tunable filter (LCTF) is presented. The system combines the merits of wide-dynamic detection range offered by the spectral approach and multiplexed high-throughput data collection with a two-dimensional (2-D) biosensor array. The key feature of the reported scheme is a feedback loop that drives the LCTF to achieve fast tracking of the SPR dip movement caused by the binding of target molecules to the sensor surface. Experimental results show that the system is capable of completing an SPR dip measurement within 4 s. Based on using a spectral window of about 100 nm, the experimental dynamic detection range and refractive index resolution are 4.63×10?2??RIU and 5.87×10?6??RIU, respectively. As also demonstrated herein using 2-D microsensor arrays, among the spectral SPR sensors, the reported system is most suitable for multiplexed label-free biosensing applications.

High-throughput imaging surface plasmon resonance biosensing based on an adaptive spectral-dip tracking scheme.

Imaging-based spectral surface plasmon resonance (λSPR) biosensing is predominantly limited by data throughput because of the multiplied data capacity emerging from 2-dimensional sensor array sites and the many data points required to produce an accurate measurement of the absorption dip. Here we present an adaptive feedback approach to address the data throughput issue in λSPR biosensing. A feedback loop constantly tracks the dip location while target-molecule binding occurs at the sensor surface. An adaptive window is then imposed to reduce the number of data points that each pixel has to capture without compromising measurement accuracy. Rapid wavelength scanning is performed with a liquid crystal tunable filter (LCTF). With the use of a feedback loop, our demonstration system can produce a dip measurement within 700ms, thus confirming that the reported λSPR approach is most suitable for real-time micro-array label-free biosensing applications.

Tunable bandpass filter with low insertion loss based on electrically controlled liquid crystal

This paper presents a low loss electrically tunable bandpass filter based on liquid crystal. By utilizing structure of transmission line and direct-connected feeding lines without coupling gaps which eliminate mismatch and radiation losses, then the proposed filter gets a low insertion loss. And the tunability of the filter is realized by utilizing electrically controlled liquid crystal (LC) material as the substrate. In order to verify the design concept, a liquid crystal tunable prototype filter is designed, fabricated and measured. Measured results agree well with the simulated results. With no bias voltage applied, the filter operates at center frequency of 11.5GHz with fractional bandwidth of 11.3%. And with voltage tuning from 0V to 10V, the filter gets a tuning range of 500MHz and the insertion losses are 2–3.1dB, respectively.

Abstract 4246: Multispectral open-air fluorescence-guided imaging and detection of tumors using a hands-free translational platform with liquid crystal tunable filters (LCTF)

Abstract Intraoperative identification and resection of tumors currently relies on the ability of the surgeon to visually detect or palpate the tumors and residual malignant tissue. As such, minuscule tumor nodules can go undetected or be inadequately removed, with such cases often resulting in the need for secondary treatment or additional surgical intervention. The Solaris™ platform is an open-air fluorescent imaging instrument designed for large animal fluorescence-guided surgery, with the advantage of real-time acquisition of fluorescence images/video under surgical light conditions. Solaris supports four fixed fluorescent channels ranging from visible to near infrared (NIR), and a multispectral channel where a liquid crystal tunable filter (LCTF) is used to acquire spectral data by sweeping across the green-to-red portion of the visible spectrum. This range of imaging channels allows for single-wavelength and multispectral imaging of widely used reagents (e.g. indocyanine green [ICG] and Fluorescein isothiocyanate [FITC]) and unique NIR fluorescent dyes used for detecting and labeling tumors. While fluorescent imaging using NIR imaging agents (680, 750, 800 nm) offered effective tumor detection, identification of tumors implanted in nude mice or rats using visible (400-650 nm) reagents such as FITC presented challenges considering the presence of auto-fluorescence originating from tissue and food (alfalfa). For these reagents, Solaris acquired multispectral images using the LCTF under ambient light conditions, and an automated spectral unmixing algorithm was applied to the multispectral data, after background correction and ambient light removal, to separate tissue and food auto-fluorescence from the reagent fluorescent signal. The algorithm used vertex component analysis to automatically extract the primary pure spectra present in the multispectral images and unmix the reagent fluorescent signal by non-negative least squares fitting. To test the spectral unmixing capabilities of Solaris, in vivo experiments were performed using small amounts of locally injected FITC in mice and rats. In the absence of unmixing, it was not possible to accurately detect sites of FITC signal, but with unmixing the labeled regions were well defined. Additional studies in tumor-bearing mice and rats substantiated the ability to spectrally unmix FITC agent signal in deep tumor masses imaged under ambient light, enhancing the ability to surgically resect them. To further validate this concept, bioluminescent tumor cell lines were implanted in mice. After image-guided tumor resection, both the residual tumor bed and the resected tumors were imaged to confirm complete removal. These data demonstrate that intraoperative image-guided resection of fluorescent-labeled tumors can be achieved using LCTF-based open-air multispectral imaging on the Solaris. Citation Format: Ali Behrooz, Kristine Vasquez, Peter Waterman, Jeff Meganck, Jeffrey Peterson, Peter Miller, Joshua Kempner, Wael Yared. Multispectral open-air fluorescence-guided imaging and detection of tumors using a hands-free translational platform with liquid crystal tunable filters (LCTF). [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4246.

Shortwave infrared hyperspectral reflectance imaging for cotton foreign matter classification

Cotton contaminants seriously reduce the commercial value of cotton lint and further degrade the quality of textile products. This research aims to investigate the potential of a non-contact technique, i.e., liquid crystal tunable filter (LCTF) hyperspectral imaging, to inspect foreign matter on the surface of cotton lint. The foreign matter samples used in this study included 11 types of botanical foreign matter and 5 types of non-botanical foreign matter. Hyperspectral images of the foreign matter were acquired using a LCTF hyperspectral imaging system with a spectral range from 900 to 1700nm. The mean spectra of the foreign matter and lint samples were extracted manually from the images. Linear discriminant analysis was applied to classify different types of foreign matter and cotton lint according to their spectral features. Classification accuracies of 96.5% and 95.1% were achieved with leave-one-out and four fold cross-validation, respectively. For pixel-level image classification, a majority of the pixels for different types of foreign matter were classified correctly by a support vector machine, using the top features of the minimum noise fraction transformation. The results demonstrate that non-contact liquid crystal tunable filter hyperspectral imaging is a promising method to discriminate foreign matter materials from cotton lint.

DESIGN OF A FREE AND OPEN SOURCE DATA PROCESSING, ARCHIVING, AND DISTRIBUTION SUBSYSTEM FOR THE GROUND RECEIVING STATION OF THE PHILIPPINE SCIENTIFIC EARTH OBSERVATION MICRO-SATELLITE

The Philippines’s PHL-Microsat program aims to launch its first earth observation satellite, DIWATA, on the first quarter of 2016. DIWATA’s payload consists of a high-precision telescope (HPT), spaceborne multispectral imager (SMI) with liquid crystal tunable filter (LCTF), and a wide field camera (WFC). Once launched, it will provide information about the Philippines, both for disaster and environmental applications. Depending on the need, different remote sensing products will be generated from the microsatellite sensors. This necessitates data processing capability on the ground control segment. Rather than rely on commercial turnkey solutions, the PHL-Microsat team, specifically Project 3:DPAD, opted to design its own ground receiving station data subsystems. This paper describes the design of the data subsystems of the ground receiving station (GRS) for DIWATA. The data subsystems include: data processing subsystem for automatic calibration and georeferencing of raw images as well as the generation of higher level processed data products; data archiving subsystem for storage and backups of both raw and processed data products; and data distribution subsystem for providing a web-based interface and product download facility for the user community. The design covers the conceptual design of the abovementioned subsystems, the free and open source software (FOSS) packages used to implement them, and the challenges encountered in adapting the existing FOSS packages to DIWATA GRS requirements.