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Topical dual-probe staining using quantum dot-labeled antibodies for identifying tumor biomarkers in fresh specimens
摘要： Purpose Rapid, intra-operative identification of tumor tissue in the margins of excised specimens has become an important focus in the pursuit of reducing re-excision rates, especially for breast conserving surgery. Dual-probe difference specimen imaging (DDSI) is an emerging approach that uses the difference in uptake/clearance kinetics between a pair of fluorescently-labeled stains, one targeted to a biomarker-of-interest and the other an untargeted isotype, to reveal receptor-specific images of the specimen. Previous studies using antibodies labeled with either enhanced Raman particles or organic fluorophores have shown promising tumor vs. normal diagnostic performance. Yet, the unique properties of quantum dot-labeled antibody complexes (QDACs), which provide spectrally-distinct fluorescence emission from a common excitation source, make them ideal candidates for this application. Herein, we evaluate the diagnostic performance of QDAC-based DDSI in excised xenografts. Procedures Excised fresh specimens of normal tissue and human tumor xenografts with elevated expression of HER2 were stained with a HER2-targeted QDAC and an untargeted QDAC isotype. Stained specimens were imaged on a custom hyperspectral imaging system capable of spectrally separating the quantum dot signatures, and images processed using the DDSI approach. The diagnostic performance of this technique under different incubation temperatures and probe concentrations was evaluated using receiver-operator characteristic analysis. Results HER2-targeted QDAC-DDSI was able to distinguish HER2(+) tumors from normal tissue with reasonably high diagnostic performance; however, this performance was sensitive to temperature during the staining procedure. Area under the curve values were 0.61 when staining at room temperature but increased to over 0.81 when staining at 37 ?C. Diagnostic performance was not affected by increasing stain concentration. Conclusions This study is the first to report dual-probe difference imaging of specimens using QDACs and hyperspectral imaging. Our results show promising diagnostic performance under certain conditions, and compel further optimization and evaluation of this intra-operative margin assessment technique.
关键词： tumor biomarkers,hyperspectral imaging,fresh specimens,quantum dot-labeled antibodies,dual-probe difference specimen imaging
更新于2025-09-19 17:13:59
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Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics
摘要： Hyperspectral imaging technology at 416–1000 nm was investigated to detect moisture content in peanut kernels. Four varieties of peanuts were scanned using a “push-broom” system to acquire hyperspectral images. In this study, three models including partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVR) were established to detect moisture content in peanut kernels based on full wavelengths. The performance of SVR was the best with determination coefficient (R2) of .9432, root mean square errors (RMSE) of 0.7054%, and residual prediction deviation (RPD) of 3.9694 for prediction set. In order to simplify modeling process and improve calculation speed of the models, successive projections algorithm (SPA) and regression coefficient were applied for optimal wavelengths selection. Then, PCR, PLSR, and SVR models were established based on these selected wavelengths, respectively. As a result, SPA–SVR generated a satisfied effect with R2 of .9363, RMSE of 0.7021%, and RPD of 3.988 for prediction set. All results in this study indicated that the combination of chemometrics and hyperspectral imaging technology could achieve rapid and nondestructive detection of moisture content in peanut kernels.
关键词： moisture content,nondestructive detection,peanut kernels,chemometrics,hyperspectral imaging technology
更新于2025-09-19 17:13:59
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Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis
摘要： Color index and water content are important indicators for evaluating the quality of fresh-cut potato tuber slices. In this study, hyperspectral imaging combined with multivariate analysis was used to detect the color parameters (L*, a*, b*, Browning index (BI), L*/b*) and water content of fresh-cut potato tuber slices. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic wavelengths, partial least squares (PLS) and least squares support vector machine (LS-SVM) were utilized to establish regression models. For color prediction, R2 p and RPD of all the LSSVM models established for the five color indicators L*, a*, b*, BI, L*/b* were exceeding 0.90, 0.84 and 2.1, respectively. For water content prediction, R2 p, and RPD of the LSSVM models were over 0.80, 0.77 and 1.9, respectively. LS-SVM model based on full spectra was used to reappear the spatial distribution of color and water content in fresh-cut potato tuber slices by pseudo-color imaging since it performed best in most cases. The results illustrated that hyperspectral imaging could be an effective method for color and water content prediction, which could provide solid theoretical basis for subsequent grading and processing of fresh-cut potato tuber slices.
关键词： water content,browning,hyperspectral imaging,fresh-cut potato tuber slices,color index
更新于2025-09-19 17:13:59
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[IEEE 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Amsterdam, Netherlands (2019.9.24-2019.9.26)] 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - A Framework For An Artificial Neural Network Enabled Single Pixel Hyperspectral Imager
摘要： Compressive Sensing enables improvement of acquisition of a variety of signals in various applications with little to no discernible loss in terms of recovered image quality. The current work proposes a signal processing framework for the acquisition and fast reconstruction of compressively sampled hyperspectral images using an artificial neural network architecture. This ANN-based approach is capable of performing a fast reconstruction by avoiding the requirement of solving a computationally intensive image-specific optimization problem. The proposed framework contributes to advance single-pixel hyperspectral imaging device methodologies, which enable a significant reduction in device mechanical complexity, imaging rate, and cost. Our experiments demonstrate that a hyperspectral image can be reconstructed using only 10% of the samples without compromising classification performance. Specifically, the results show that classification performance of the compressively sampled hyperspectral image recovered using artificial neural networks is equal or higher to that of those obtained using current scanning hyperspectral imaging platforms.
关键词： remote sensing,deep learning,hyperspectral imaging,compressive sensing
更新于2025-09-19 17:13:59
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Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data
摘要： High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature.
关键词： photovoltaic panels,detection and area estimation,urban areas,hyperspectral unmixing,hyperspectral imaging,partial nonnegative matrix factorization
更新于2025-09-16 10:30:52
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[IEEE 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) - Berlin, Germany (2019.7.23-2019.7.27)] 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Hyperspectral imaging for thermal effect monitoring in in vivo liver during laser ablation
摘要： Thermal ablation is a minimally invasive technique used to induce a controlled necrosis of malignant cells by increasing the temperature in localized areas. This procedure needs an accurate and real-time monitoring of thermal effects to evaluate and control treatment outcome. In this work, a hyperspectral imaging (HSI) technique is proposed as a new and non-invasive method to monitor ablative therapy. HSI provides images of the target object in several spectral bands, hence the reflectance/absorbance spectrum for each pixel. This paper presents a preliminary and original HSI-based analysis of the thermal state in the in vivo porcine liver undergoing laser ablation. In order to compare the spectral response between treated and untreated areas of the organ, proper Regions of Interest (ROIs) were chosen on the hyperspectral images; for each ROI, the absorbance variation for the selected wavelengths (i.e., 630, 760, and 960nm, for deoxyhemoglobin, methemoglobin, and water respectively) was assessed. Results obtained during and after laser ablation show that the absorbance of the methemoglobin peaks increases up to 40% in the burned region with respect to the non-ablated one. Conversely, the relative change of deoxyhemoglobin and water peaks is less marked. Based on these results, absorbance threshold values were retrieved and used to visualize the ablation zone on the images. This preliminary analysis suggests that a combination of the absorbance information is essential to achieve a more accurate identification of the ablation region. The results encourage further studies on the correlation between thermal effects and the spectral response of biological tissues undergoing thermal ablation, for final clinical use.
关键词： laser ablation,absorbance spectrum,in vivo porcine liver,hyperspectral imaging,thermal ablation
更新于2025-09-16 10:30:52
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[IEEE 2019 21st International Conference on Transparent Optical Networks (ICTON) - Angers, France (2019.7.9-2019.7.13)] 2019 21st International Conference on Transparent Optical Networks (ICTON) - Active Hyperspectral Mid-Infrared Imaging Based on Widely Tunable QCL Laser
摘要： Thermal imaging and the recent availability of widely tunable infrared QCL lasers (Quantum Cascade Laser) allow us to propose an active hyperspectral imaging system operating in mid-infrared (MIR) band to obtain simultaneously large amounts of spatial and spectral information on the samples. In order to evaluate more precisely the capacities of the active hyperspectral imaging, we propose a system composed of four powerful QCL tunable lasers (in order to cover 3 – 5 μm and 7 – 11 μm wavelengths) and three cameras: a visible and near-infrared (NIR) range, a bolometer for 7 – 13 μm range and an InSb cooled camera for 3 – 5 μm range. We present the algorithm for image acquisition, image and data processing. Finally, we present and discuss some preliminary results using this system to characterize plant leaves under controlled growing conditions.
关键词： plant monitoring,tunable QCL laser,mid-infrared,hyperspectral imaging,image processing
更新于2025-09-16 10:30:52
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[Laser Institute of America ICALEO? 2017: 36th International Congress on Applications of Lasers & Electro-Optics - Atlanta, Georgia, USA (October 22–26, 2017)] International Congress on Applications of Lasers & Electro-Optics - Influence of vapor on hyperspectral imaging for monitoring laser-based material processing
摘要： In this paper a further characterization of hyperspectral imaging (HSI) system that we developed is presented. This camera-based system is meant to be used for temperature determination in laser-based material processing. It comprises a high-speed camera and a self-developed HSI-lens system. It offers a time resolution in the μs-range and a spectral resolution of ~4 nm. In HSI spatial and spectral information upon the object/process to be investigated can be acquired simultaneously. The spectral information reveals spectral features of the process itself and it is used to derive temperature information. For temperature determination, the process emissions are considered to be black/gray-body radiation. Based upon very promising initial HSI results, we present and discuss further HSI-derived observations of the laser-beam welding process within this paper. These results include temperature information as well as spectral characteristics. In addition, simultaneous high-speed camera observations were conducted and the results were connected with HSI-derived findings. This way, the influence of vapor on the measurement is evaluated and the HSI-technique is validated to be beneficial in monitoring laser-based material processing.
关键词： hyperspectral imaging,laser-based material processing,vapor influence,temperature determination,high-speed camera
更新于2025-09-16 10:30:52
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Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network
摘要： Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise spectra were extracted and preprocessed, and average spectra were also obtained. Convolutional neural networks (CNN) using the average spectra and pixel-wise spectra of different numbers of soybeans were built. Pixel-wise CNN models obtained good performance predicting pixel-wise spectra and average spectra. With the increase of soybean numbers, performances were improved, with the classification accuracy of each variety over 90%. Traditionally, the number of samples used for modeling is large. It is time-consuming and requires labor to obtain hyperspectral data from large batches of samples. To explore the possibility of achieving decent identification results with few samples, a majority vote was also applied to the pixel-wise CNN models to identify a single soybean variety. Prediction maps were obtained to present the classification results intuitively. Models using pixel-wise spectra of 60 soybeans showed equivalent performance to those using the average spectra of 810 soybeans, illustrating the possibility of discriminating soybean varieties using few samples by acquiring pixel-wise spectra.
关键词： a majority vote,convolutional neural network,hyperspectral imaging technology,soybean,pixel-wise spectra
更新于2025-09-16 10:30:52
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Ancient Greek text concealed on the back of unrolled papyrus revealed through shortwave-infrared hyperspectral imaging
摘要： Only a few Herculaneum rolls exhibit writing on their reverse side. Since unrolled papyri are permanently glued to paperboard, so far, this fact was known to us only from 18th-century drawings. The application of shortwave-infrared (SWIR; 1000-2500 nm) hyperspectral imaging (HSI) to one of them (PHerc. 1691/1021) has revealed portions of Greek text hidden on the back more than 220 years after their first discovery, making it possible to recover this primary source for the ongoing new edition of this precious book. SWIR HSI has produced better contrast and legibility even on the extensive text preserved on the front compared to former imaging of Herculaneum papyri at 950 nm (improperly called multispectral imaging), with a substantial impact on the text reconstruction. These promising results confirm the importance of advanced techniques applied to ancient carbonized papyri and open the way to a better investigation of hundreds of other such papyri.
关键词： ancient Greek text,shortwave-infrared hyperspectral imaging,Philodemus’ History of the Academy,text reconstruction,Herculaneum papyri
更新于2025-09-16 10:30:52