Single Color Channel Research Articles

Performance of a Rapid Digital PCR Test for the Detection of Non-Small Cell Lung Cancer (NSCLC) Variants.

Next-generation sequencing is widely used for comprehensive molecular profiling for many cancers including lung cancer. However, the complex workflows and long turnaround times limit its access and utility. ChromaCode's High Definition PCR Non-Small Cell Lung Cancer Panel (HDPCR™ NSCLC Panel) is a low-cost, rapid turnaround, digital polymerase chain reaction assay that is designed to detect variants in nine NSCLC genes listed in National Comprehensive Cancer Network guidelines. This assay uses TaqMan® probe limiting chemistry and proprietary analysis software to enable multi-target detection within a single-color channel. We compared the performance of the HDPCR™ NSCLC Panel against an in-house, laboratory-developed, targeted next-generation sequencing panel used in the Molecular Diagnostics Laboratory at the University of Minnesota Medical Center to detect biomarkers for NSCLC. The overall accuracy of the HDPCR panel was 99.48% (95% confidence interval 99.01-99.76) with a sensitivity of 95.35% (95% confidence interval 88.52-98.72) and a specificity of 99.69% (95% confidence interval 99.29-99.90). The HDPCR wet lab workflow was 4 h, and the time to generate variant calls from raw data using the ChromaCode Cloud was 2 minutes. We demonstrated that the HDPCR™ NSCLC Panel provides timely, comprehensive, and sensitive mutation detection in NSCLC samples with results in less than 24 h.

A Motion and Illumination Resistant Non-Contact Method Using Undercomplete Independent Component Analysis and Levenberg-Marquardt Algorithm.

Heart Rate (HR) estimation is of utmost importance due to its applicability in diverse fields. Conventional methods for HR estimation require skin contact and are not suitable in certain scenarios such as sensitive skin or prolonged unobtrusive HR monitoring. Therefore remote photoplethysmography (rPPG) methods have become an active area of research. These methods utilize the facial videos acquired using a camera followed by extracting the Blood Volume Pulse (BVP) signal for heart rate calculation. The existing rPPG methods either utilized a single color channel or weighted color differences, which has certain limitations dealing with motion and illumination artifacts. This study considered BVP extraction as an undercomplete problem and proposed a method resistant to motion and illumination variation artifacts. This method is based on an undercomplete independent component analysis, aiming to estimate the unmixing matrix using a non-linear Cumulative Density Function (CDF) that has been optimized using the customized Levenberg-Marquardt algorithm. Therefore, the method is named U-LMA. The proposed method was tested under three scenarios: constrained, motion, and illumination variations scenarios. High Pearson correlation coefficient values and smaller lower-upper statistical limits of Bland-Altman plots justified the outstanding performance of the proposed U-LMA. Furthermore, its comparative analysis with the state-of-the-art methods demonstrated its efficacy and reliability, which was proven by the lowest error and highest correlation values (0.01 significance level). Additionally, higher accuracy satisfying the clinically accepted error differences also justified its clinical relevance.

Abstract 2929: High-Definition Digital PCR (HDPCR™) enables sensitive measurement of DNA and RNA variants in non-small cell lung cancer samples

Abstract Digital PCR (dPCR) is an emerging platform for detecting genomic variants in cancer genomes due to its high sensitivity and fast time to result compared to massively parallel sequencing. However, translational oncology applications often require the measurement of more biomarkers than there are color channels available on dPCR platforms. One approach to address this is to split a sample across many wells and profile a subset of variants in each well. For input-limited samples, however, this results in fewer molecules being profiled in each well, resulting in a reduction in sensitivity and fewer samples processed per instrument run. ChromaCode has developed a research use only (RUO) high-definition digital PCR assay, for multiplexed detection of 14 DNA variants and 15 RNA fusion variants relevant in non-small cell lung cancer (NSCLC) samples. The assay is constructed using both amplitude modulation and multi-channel resilient signal encoding methods. Amplitude modulation enables different variants to generate a signal at different intensity levels in single color channel, allowing for greater than n targets in n color channels. In contrast, resilient encoding generates a signal in more than one color channel to create a form of error detecting code. DNA and RNA samples were obtained using a combination of synthetic templates, cell line nucleic acids, and commercially available reference materials. Samples were run with the HDPCR NSCLC RUO assay on the Thermo Fisher Absolute Q digital PCR system, and data analysis was performed with custom analysis algorithms. The HDPCR NSCLC RUO assay demonstrated a limit of detection as low as 10 variant copies in a 10,000 haploid human genome copy DNA background. By employing HDPCR technology on digital PCR systems, it is possible to build a comprehensive and sensitive research assay that quickly detects many genomic alterations relevant to NSCLC samples. Citation Format: Bryan Leatham, Katie McNall, Hari K. Subramanian, John Alvarado, Lucien Jacky, Mimi Wang, Aditya Rajagopal, Jerrod Schwartz. High-Definition Digital PCR (HDPCR™) enables sensitive measurement of DNA and RNA variants in non-small cell lung cancer samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2929.

High-definition PCR (HDPCR) detection of DNA and RNA variants in non-small cell lung cancer (NSCLC) samples.

e20603 Background: Digital PCR (dPCR) is an emerging technology platform for detecting genomic variants in cancer genomes due to its high sensitivity and fast time to results compared to sequencing. However, translational oncology applications often require the measurement of more biomarkers than there are color channels available on dPCR platforms. One approach to address this limitation with dPCR is to split a sample across many wells and profile a subset of variants in each well. For input-limited samples, however, this results in fewer molecules being profiled in each dPCR well, resulting in a reduction in sensitivity and fewer patient samples processed per instrument run. ChromaCode has developed a research use only (RUO) digital High Definition PCR (HDPCR) NSCLC assay, for multiplexed detection of 14 DNA variants and 15 RNA fusion variants relevant in non-small cell lung cancer samples. The assay is constructed using both amplitude modulation and multi-channel resilient signal encoding methods. Amplitude modulation enables different variants to generate a distinguishable signal at different intensity levels in a single color channel, allowing for multiple targets to be detected within that single-color channel. In addition, resilient encoding generates a signal in more than one color channel to create a form of error detection in the assay design. Methods: Assay benchmarking was performed using over 500 contrived human biological FFPE samples, consisting of synthetic DNA or RNA variants spiked into a background matrix of FFPE-extracted DNA or RNA; over 500 contrived human biological plasma samples, consisting of synthetic DNA or RNA variants spiked into a background matrix of plasma-extracted cell free DNA or RNA; and residual human biological FFPE and plasma NSCLC samples that were previously characterized using a targeted sequencing workflow. The samples were tested using the HDPCR NSCLC assay on the QuantStudio Absolute Q Digital PCR system, and data analysis was performed with custom analysis algorithms. Results: For the more than 500 contrived FFPE and plasma samples, the HDPCR NSCLC assay had high overall agreement with expectation across a range of mutant allele fractions for both DNA and RNA analytes (≥99% PPA and ≥99% NPA). For a set of N = 25 residual human biological FFPE samples, the assay was also highly concordant (100% PPA and 99% NPA) with a targeted panel sequencing comparator. The hands-on workflow time from isolation start to analysis complete was < 24 hours. Conclusions: The HDPCR NSCLC assay is a robust RUO tool for the sensitive and rapid detection of commonly targeted variants relevant to NSCLC samples. This technology could complement sequencing assays when there is a need for a rapid turnaround time or there are limited amounts of isolated nucleic acid.

Multi-Color Channels Based Group Sparse Model for Image Restoration

The group sparse representation (GSR) model combines local sparsity and nonlocal similarity in image processing, and achieves excellent results. However, the traditional GSR model and all subsequent improved GSR models convert the RGB space of the image to YCbCr space, and only extract the Y (luminance) channel of YCbCr space to change the color image to a gray image for processing. As a result, the image processing process cannot be loyal to each color channel, so the repair effect is not ideal. A new group sparse representation model based on multi-color channels is proposed in this paper. The model processes R, G and B color channels simultaneously when processing color images rather than processing a single color channel and then combining the results of different channels. The proposed multi-color-channels-based GSR model is compared with state-of-the-art methods. The experimental contrast results show that the proposed model is an effective method and can obtain good results in terms of objective quantitative metrics and subjective visual effects.

Photoswitch-Based Fluorescence Encoding of Microspheres in a Limited Spectral Window for Multiplexed Detection.

Fluorescence barcoding with multicolor fluorophores is limited by spectral crowding. Herein, we propose a fluorescence encoding method in a single-color channel with photoswitches. The photochromic naphthopyran was used to mediate the fluorescence of polystyrene microspheres through resonance energy transfer. The initial fluorescence intensity (F0) and the fluorescence after UV light activation (F/F0) were combined to generate hundreds of 2-dimensional barcodes. The coding capacity was further expanded with the different chemical kinetics of the photoswitches. The photoswitch-based fluorescence barcodes were applied to simultaneously and selectively detect the DNA sequences of COVID-19 (with related mutations) as a proof-of-concept for real applications. The compatibility with the state-of-the-art fluorescence microscopes and simple encoding and decoding make the method very attractive for multiplexed and high-throughput analyses.

Color image encryption based on discrete trinion Fourier transform and random-multiresolution singular value decomposition

With more and more color images are spread and shared in the untrusted cloud server remotely over the open Internet, some private or confidential information may be contained, it is necessary to develop cryptosystem for these images to free from serious threats. This paper investigates a secure and robust cryptosystem for color image based on discrete trinion Fourier transform and random-multiresolution singular value decomposition. Firstly, a color image is represented into a trinion matrix by precoding and followed by block-wise discrete trinion Fourier transform. Then a mapping from trinion number domain to real number domain is implemented to facilitate random-multiresolution singular value decomposition. To further enhance the randomness of the transformed data, the scrambling based on chaotic sequence is applied to obtain the cipherimage. Experimental results have demonstrated that the proposed color image encryption algorithm is feasible. The adoption of logistic map ensures that the proposed cryptosystem attains high level security. Furthermore, the quality of the retrieved image is better than that of the algorithm based on single color channel processing separately and quaternion matrix based algorithms when it is decrypted correctly or under Gaussian noise and shear attacks. Moreover, employing a trinion matrix to represent a color image is more compact than using quaternion matrix. It would have potential applications in digital image protection.

Alpha particle detection by Makrofol® DE1-1 and CR-39 NTDs: A comparative study

In this present study, a color contrast approach was proposed to find a method for quantify uncertainty in alpha particle spectroscopy using nuclear track detectors (NTDs). According to this method, this color contrast approach was applied to NTDs as an alpha particle spectrometer. A comparison between the registration of 4.0 MeV alpha particles by Makrofol®DE1-1 and CR-39 NTDs using a color contrast method was carried out in alpha spectroscopy. The contrast of the alpha tracks of each single color channel whether red, green and blue resulting from split RGB image was determined. The measurements were proved that green color channel produced the highest contrast of the alpha tracks of about 85%, in both Makrofol® DE1-1 and CR-39 NTDs. The results of 4.0 MeV alpha particle etch-pits diameters in Makrofol® DE1-1 didn't show homogenous diameters for short etching time. The standard deviation of 4.0 MeV alpha-track diameters in Makrofol® DE1-1 went up to a maximum value of 2.23 μm at a removal thickness of 12.25 μm. When increasing the etching time, they decreased sharply to about 1.18 ± 0.02 μm at a ratio of change 0.047 that was less than 6%. The standard deviation of 4.0 MeV alpha particle etch-pit diameters in CR-39 detector was linearly correlated with the removal thickness at a ratio of change of 0.02 which was less than 5%. Results revealed that the etch-pit diameter in Makrofol® DE1-1detector, in alpha particle spectrometry was best measured around the end of the alpha particle range. Results of this study are discussed within the frame work of nuclear track formation theories and etching mechanism in nuclear track detectors (NTDs).

Faster, sharper, more precise: Automated Cluster-FLIM in preclinical testing directly identifies the intracellular fate of theranostics in live cells and tissue

Fluorescence microscopy is widely used for high content screening in 2D cell cultures and 3D models. In particular, 3D tissue models are gaining major relevance in modern drug development. Enabling direct multiparametric evaluation of complex samples, fluorescence lifetime imaging (FLIM) adds a further level to intensity imaging by the sensitivity of the fluorescence lifetime to the microenvironment. However, the use of FLIM is limited amongst others by the acquisition of sufficient photon numbers without phototoxic effects in live cells. Herein, we developed a new cluster-based analysis method to enhance insight, and significantly speed up analysis and measurement time for the accurate translation of fluorescence lifetime information into pharmacological pathways.Methods: We applied a fluorescently-labeled dendritic core-multishell nanocarrier and its cargo Bodipy as molecules of interest (MOI) to human cells and reconstructed human tissue. Following the sensitivity and specificity assessment of the fitting-free Cluster-FLIM analysis of data in silico and in vitro, we evaluated the dynamics of cellular molecule uptake and intracellular interactions. For 3D live tissue investigations, we applied multiphoton (mp) FLIM. Owing to Cluster-FLIM's statistics-based fitting-free analysis, we utilized this approach for automatization.Results: To discriminate the fluorescence lifetime signatures of 5 different fluorescence species in a single color channel, the Cluster-FLIM method requires only 170, respectively, 90 counts per pixel to obtain 95% sensitivity (hit rate) and 95% specificity (correct rejection rate). Cluster-FLIM revealed cellular interactions of MOIs, representing their spatiotemporal intracellular fate. In a setting of an automated workflow, the assessment of lysosomal trapping of the MOI revealed relevant differences between normal and tumor cells, as well as between 2D and 3D models.Conclusion: The automated Cluster-FLIM tool is fitting-free, providing images with enhanced information, contrast, and spatial resolution at short exposure times and low fluorophore concentrations. Thereby, Cluster-FLIM increases the applicability of FLIM in high content analysis of target molecules in drug development and beyond.

A Method of Ultrasonic Finger Gesture Recognition Based on the Micro-Doppler Effect

With the popularity of small-screen smart mobile devices, gestures as a new type of human–computer interaction are highly demanded. Furthermore, finger gestures are more familiar to people in controlling devices. In this paper, a new method for recognizing finger gestures is proposed. Ultrasound was actively emitted to measure the micro-Doppler effect caused by finger motions and was obtained at high resolution. By micro-Doppler processing, micro-Doppler feature maps of finger gestures were generated. Since the feature map has a similar structure to the single channel color image, a recognition model based on a convolutional neural network was constructed for classification. The optimized recognition model achieved an average accuracy of 96.51% in the experiment.

Illumination-aware faster R-CNN for robust multispectral pedestrian detection

Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. However, there is still a lack of studies on how to fuse the two modalities effectively. In this paper, we deeply compare six different convolutional network fusion architectures and analyse their adaptations, enabling a vanilla architecture to obtain detection performances comparable to the state-of-the-art results. Further, we discover that pedestrian detection confidences from color or thermal images are correlated with illumination conditions. With this in mind, we propose an Illumination-aware Faster R-CNN (IAF R-CNN). Specifically, an Illumination-aware Network is introduced to give an illumination measure of the input image. Then we adaptively merge color and thermal sub-networks via a gate function defined over the illumination value. The experimental results on KAIST Multispectral Pedestrian Benchmark validate the effectiveness of the proposed IAF R-CNN.

Feature fusion with covariance matrix regularization in face recognition

The fusion of multiple features is important for achieving state-of-the-art face recognition results. This has been proven in both traditional and deep learning approaches. Existing feature fusion methods either reduce the dimensionality of each feature first and then concatenate all low-dimensional feature vectors, named as DR-Cat, or the vice versa, named as Cat-DR. However, DR-Cat ignores the correlation information between different features which is useful for classification. In Cat-DR, on the other hand, the correlation information estimated from the training data may not be reliable especially when the number of training samples is limited. We propose a covariance matrix regularization (CMR) technique to solve problems of DR-Cat and Cat-DR. It works by assigning weights to cross-feature covariances in the covariance matrix of training data. Thus the feature correlation estimated from training data is regularized before being used to train the feature fusion model. The proposed CMR is applied to 4 feature fusion schemes: fusion of pixel values from 3 color channels, fusion of LBP features from 3 color channels, fusion of pixel values and LBP features from a single color channel, and fusion of CNN features extracted by 2 deep models. Extensive experiments of face recognition and verification are conducted on databases including MultiPIE, Georgia Tech, AR and LFW. Results show that the proposed CMR technique significantly and consistently outperforms the best single feature, DR-Cat and Cat-DR.

Reliability of different color spaces to estimate nitrogen SPAD values in maize

A method to estimate the leaf nitrogen concentration based on image processing techniques was developed. The images came from maize leaves with known levels of nitrogen, measured with a SPAD meter. In order to establish a correlation between the N concentration and the SPAD measurement, an average value in SPAD units and a representative color value were assigned to every leaf. In fact, the representative color values were computed by creating a weighting average of three representative leaf color groups. Accordingly, a prototype of every group and the pixel's area of each group were used to compute the weighting average. Thereby, these groups were created according to their degree of similarity using the k-means clustering technique, which provides a prototype of every group. In specific, the clustering was performed over images in the L∗a∗b∗ color space. In addition, correlation models to predict SPAD values, using a single color channel and two color channels, from the RGB, HSV and L∗a∗b∗ color spaces were tested. The analysis shows that it is possible to correlate SPAD values and color data with the concentration of N, furthermore, shows that when single color channel correlations were performed, the channels G, b∗ and V provide the better modeling accuracy. Moreover, it was likewise shown that better correlations were obtained when a combination of two color channels, from the same or different color spaces, were applied in the correlation. In particular, the most accurate prediction models were obtained held the pairs of color channels S-V and G-b∗.

MCCT: a multi-channel complementary census transform for image classification

Census transformation and its variants have gained popularity in image classification for their simplicity and better performance. To describe a texture pattern, these approaches generally use sign information while comparing neighboring pixels. However, our observation is that sign and magnitude in a single color channel as well as in different color channels hold complementary information where sign component captures texture in an image and the saliency of that texture can be captured by the magnitude component. Considering these issues, a multi-channel complementary census transform (MCCT) is proposed in this paper by combining all of these information to capture more discriminating features. Rigorous experiments on nine different datasets which belong to six different applications such as flower, gender, aerial orthoimagery, event, leaf, indoor and outdoor scene classification demonstrate that MCCT outperforms existing state-of-the-art techniques.

Text recognition in scene image and video frame using Color Channel selection

In recent years, recognition of text from natural scene image and video frame has got increased attention among the researchers due to its various complexities and challenges. Because of low resolution, blurring effect, complex background, different fonts, color and variant alignment of text within images and video frames, etc., text recognition in such scenario is difficult. Most of the current approaches usually apply a binarization algorithm to convert them into binary images and next OCR is applied to get the recognition result. In this paper, we present a novel approach based on color channel selection for text recognition from scene images and video frames. In the approach, at first, a color channel is automatically selected and then selected color channel is considered for text recognition. Our text recognition framework is based on Hidden Markov Model (HMM) which uses Pyramidal Histogram of Oriented Gradient features extracted from selected color channel. From each sliding window of a color channel our color-channel selection approach analyzes the image properties from the sliding window and then a multi-label Support Vector Machine (SVM) classifier is applied to select the color channel that will provide the best recognition results in the sliding window. This color channel selection for each sliding window has been found to be more fruitful than considering a single color channel for the whole word image. Five different features have been analyzed for multi-label SVM based color channel selection where wavelet transform based feature outperforms others. Our framework has been tested on different publicly available scene/video text image datasets. For Devanagari script, we collected our own data dataset. The performances obtained from experimental results are encouraging and show the advantage of the proposed method.

A Novel Combined Color Channel and ISNT Rule Based Automatic Glaucoma Detection from Color Fundus Images

Background/Objectives: Glaucoma is an eye disease causing optic nerve damage and subsequently leading to progressive, irreversible vision loss. The need for early detection of glaucoma arises due to its asymptomatic and irreversible vision loss nature. For large scale screening of glaucoma, Color Fundus Images (CFI) is suitable due to its cost effectiveness. Therefore many works have been done toward automated glaucoma detection system by analyzing the Optic Nerve Head (ONH) from the CFI and calculating the Cup-to-Disk Ratio (CDR), a key indicator of glaucoma. Methods/Statistical Analysis: Unlike past works which relies on a single color channel for extracting the Optic Disk (OD) and Optic Cup (OC) used in CDR calculation, we propose a novel combined color channel and ISNT rule based automated glaucoma detection. Findings: The result shows that the proposed method betters single channel based giving an overall efficiency of more than 97%. Applications/Improvements: Screening of glaucoma at large scale by manual means requires skilled ophthalmologists which are scarce and also the cost of screening is very costly. Hence Computer-Aided Diagnosis (CAD) based automated glaucoma screening using CFI would be apt for large scale screening.

Color Holographic Wavefront Printing Technique for Realistic Representation

This paper presents design and implementation of a color wavefront holographic printer. The printer output is a white light viewable volume hologram composed as a two-dimensional (2-D) array of elemental holograms. The three-dimensional (3-D) input data for the printer are encoded as computer generated holograms (CGHs) displayed in succession on an amplitude type spatial light modulator. The printer optical head extracts and demagnifies the beam with the 3-D object information from the beam diffracted by the modulator to produce a small size elemental hologram. This allows for mosaic delivery of exposures at primary colors with each elemental hologram acting as a single color channel. Computer generation of digital contents is accelerated by design of a phase-added stereogram approach based on partitioning of each CGH. Bright 3-D reconstruction with a motion parallax at saturated colors from holograms of test objects is presented. The holograms are printed on a silver-halide emulsion. The achieved high quality proves feasibility of recording analog color volume holograms from digital contents by mosaic delivery of the primary colors. Two quality enhancement approaches are proposed and tested.

Joint image splicing detection in DCT and Contourlet transform domain

Splicing is a fundamental and popular image forgery method and image splicing detection is urgently called for digital image forensics recently. In this paper, a Markov based approach is proposed to detect image splicing. The paper applies the Markov model in the block discrete cosine transform (DCT) domain and the Contourlet transform domain. First, the original Markov features of the inter-block between block DCT coefficients are improved by considering the different frequency ranges of each block DCT coefficients. Then, additional features are extracted in Contourlet transform domain to characterize the dependency of positions among Contourlet subband coefficients. And these features are extracted from single color channel for gray image while extracted from three color channels for color image. Finally, Support Vector Machines (SVMs) are exploited to classify the authentic and spliced images for the gray image dataset while ensemble classifier to the color image dataset. The experiment results demonstrate that the proposed detection scheme outperforms some state-of-the-art methods when applied to Columbia Image Splicing Detection Evaluation Dataset (DVMM), and ranks fourth in phase 1 on the Live Ranking of the first Image Forensics Challenge.

Color wavefront printer with mosaic delivery of primary colors

The paper presents design and implementation of a color wavefront holographic printer which prints white light viewable holograms of three-dimensional (3D) objects from digital contents. Similarly to other holographic printers, the printed hologram is composed as a two-dimensional array of elemental volume holograms. In the proposed wavefront printer, the 3D information was encoded in computer generated holograms displayed in succession on an amplitude spatial light modulator. The light beam diffracted from the modulator was filtered to extract the beam coming from the object and demagnified to be recorded onto the holographic emulsion as a small size elemental hologram that made possible application of mosaic delivery of exposures at primary colors. As a result, each elemental hologram corresponded to a single color channel. A modified phase-added stereogram approach based on a hologram partitioning method was proposed to accelerate computer generation of digital contents. We achieved bright 3D reconstruction with a motion parallax at saturated colors from holograms of test objects that were printed on a silver-halide emulsion. Thus we proved experimentally feasibility of recording analog color volume holograms from digital contents by applying spatially separated exposures at primary colors to the elemental holograms.

Identification of Graphene Layer Numbers from Color Combination Contrast Image for Wide-Area Characterization

Identification of the number of graphene layers using an optical microscope images taken at various magnifications is investigated from the viewpoint of simple wide-area inspection. For graphene on 300-nm-thick SiO2, combination of red and green color contrast gives more accurate contrast value and provides better contrast even at the low magnification as compared with the single color channel contrast. The color combination with suitable weighting factors taking account of light wavelength and intensity dependences of the system response results in the contrast that agrees well with the theoretical values from Fresnel's law. Simple image processing is also investigated to improve the signal-to-noise ratio (SNR) of the image. Median filtering improves the SNR of the image having high pixel density, whereas dithering is effective for the low magnification image having block noise due to low pixel density.