Hyperspectral imaging system based on a single-pixel camera design for detecting differences in tissue properties.

In order to quickly distinguish infertile eggs from fertile eggs, the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs. Before hatching eggs were incubated, a hyperspectral imaging system (wavelength between 400 to 1 000 nm) was used to acquire the images one-by-one manually. The characteristic information of ratios of length to short axis, elongation, roundness and the ratios of the yolk area to the whole area was extracted based on the images. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimensions of image-spectrum fusion information; the top six principal components were extracted. Support vector machine (SVM) method was used to establish classification of fertile and infertile eggs models, which are based on image, spectrum and image-spectrum fusion information respectively. The accuracy rates of the SVM models were 84.00%, 90.00% and 93.00% respectively. The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model. Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation. Keywords: hyperspectral image, hatching eggs, information fusion, nondestructive detection DOI: 10.3965/j.ijabe.20150804.1672 Citation: Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69－76.

In this paper, a comprehensive spatio-spectral and temporal analysis for Chromobacterium violaceum colonies is reported. A hyperspectral imaging (HSI) system is used to recover the spectral signatures of pigment production in a non-homogeneous media with high spectral resolution and high sensitivity in vivo, without destructing the sample. This non-contact sensing technique opens avenues to study the temporal growing of a specific section in the bacterial colony. Further, from a 580 [nm] and 764 [nm] spatio-spectral time series, a wild-type and mutant Chromobacterium violaceum strains are characterized. Such study provides quantitative information about kinetic parameters of pigment production and bacterial growing.

Radiometric calibration of all optical sensors is a key component to achieve high accuracy data, which is traceable to international standards and repeatable through time. This study introduces a Hyperspectral Imaging (HSI) system that RAL Space has developed and presents a thorough analysis of the radiometric reflectance calibration chain required for both static and aerial applications, where the HSI system is mounted on an Unoccupied Aerial System (UAS). RAL Space HSI system comprises two commercially available Ximea snapshot mosaic imagers, one sensitive in the visible (VIS) and the other in the near infrared (NIR) spectral ranges, that are coupled with a dedicated PC and customized LabVIEW code for the capture, process and storage of the raw hyperspectral information (hypercubes). Both imagers are optically characterized for their electrical gain and offset, the vignetting and stray light effects, while the spectral response of each band has also been determined. The corrected digital numbers (DN) are then translated into reflectance values with the use of reflectance standards. For the aerial surveys, an additional step has been developed into the calibration chain that corrects for any changes in ambient illumination during flight with the use of ground based upward-looking measured spectra. Future work will include any correction needed due to temperature dependencies of the calibration chain steps.

Polarized spectra–hyperspectral data fusion technique was used to estimate the soluble sugar (SS), total nitrogen (N), and their ratio (SS/N), of greenhouse tomato leaves. Fresh tomato leaves of five different growth stages (seedling, flowering, initial fruiting, mid-fruiting and picking stage) and five different nitrogen treatments (severe stress 25%, moderate stress 50%, mild stress 75%, normal 100%, and excess 150%) at every stage were collected for spectra acquisition and SS and N determination. Polarized reflectance spectra were acquired with a polarization reflectance spectrum spectro-goniophotometer system and four polarization degree features were extracted. Hyperspectral data were collected with a hyperspectral imaging system and four reflectance spectrum features and eight image features were extracted. Initially, models were built with polarization degree features, image features, and spectral features respectively. Linear and nonlinear fusion methods were comparatively used for modeling based on normalized data of the three sources. The results suggest that the performances of SS/N models are better than those of N and SS models, and the prediction capability of the Support Vector Machine (SVM) models of N and SS/N are superior to those obtained with single kind feature. This work indicates that the polarized spectrum-hyperspectral multidimensional information detecting method can feasibly judge the tomato nutrient stress conditions. Multi-features data fusion analysis technique can enhance the prediction accuracy of spectral diagnostics technology in precision agriculture. Keywords: polarized spectra, hyperspectral, soluble sugar (SS), total nitrogen (N), data fusion, tomato leaf DOI: 10.25165/j.ijabe.20201302.4280 Citation: Zhu W J, Li J Y, Li L, Wang A C, Wei X H, Mao H P. Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion. Int J Agric & Biol Eng, 2020; 13(2): 189–197.

We have recently conducted a series of laboratory and field test to demonstrate the utility of combining active illumination with hyperspectral imaging for the detection of concealed targets in natural terrain. The active illuminator, developed at MIT Lincoln Laboratory, is a novel microlaser-pumped fiber Raman source that provides high- brightness, subnanosecond-pulse-length output spanning the visible through near-IR spectral range. The hyperspectral- imaging system is comprised of a compact, grating-based spectrometer that uses a gateable, intensified CCD array as the detector element. The illuminator and hyperspectral imaging system are mounted on a small platform that is itself mounted on a tripod and scanned in azimuth to build an image scene of up to several hundred spectral bands. The system has been deployed under a variety of environmental conditions, including night-time illumination, and on a variety of target scenes, including exposed and concealed plastic and metallic mine-like targets. Targets have been detected and identified on the basis of spectral reflectance, fluorescence signatures, degree of polarization, and range-to-target information. The combination of laser-like broadband illumination and hyperspectral imaging offers great promise in concealed or obscured target detection. On-going developments include the incorporation of broadband illuminators in the 1 to 2 micrometers and 3 to 5 micrometers spectral bands, with corresponding increases in spectral coverage of the imaging and detection systems.

Hyperspectral imaging provides complex image data with spectral information from many fluorescent species contained within the sample such as the fluorescent labels and cellular or pigment autofluorescence. To maximize the utility of this spectral imaging technique it is necessary to couple hyperspectral imaging with sophisticated multivariate analysis methods to extract meaningful relationships from the overlapped spectra. Many commonly employed multivariate analysis techniques require the identity of the emission spectra of each component to be known or pure component pixels within the image, a condition rarely met in biological samples. Multivariate curve resolution (MCR) has proven extremely useful for analyzing hyperspectral and multispectral images of biological specimens because it can operate with little or no a priori information about the emitting species, making it appropriate for interrogating samples containing autofluorescence and unanticipated contaminating fluorescence. To demonstrate the unique ability of our hyperspectral imaging system coupled with MCR analysis techniques we will analyze hyperspectral images of four-color in-situ hybridized rat brain tissue containing 455 spectral pixels from 550 - 850 nm. Even though there were only four colors imparted onto the tissue in this case, analysis revealed seven fluorescent species, including contributions from cellular autofluorescence and the tissue mounting media. Spectral image analysis will be presented along with a detailed discussion of the origin of the fluorescence and specific illustrations of the adverse effects of ignoring these additional fluorescent species in a traditional microscopy experiment and a hyperspectral imaging system.

The primary application of hyperspectral imaging is to classify/quantify objects/materials in the scene based on their spectral signatures. The spectral features that are useful can sometimes fall outside the spectral range of a single hyperspectral imager which is usually limited by the spectral response range of the sensor material of the focal plane array within the imager. For these wide spectrum applications, some users are combining data from two (or more) hyperspectral imaging systems. Aside from the optical alignment, size and synchronization issues involved in such a setup, the process of pixel co-registration, i.e. geometrically transforming data from the 2 hyperspectral imaging systems to overlay one another is tedious and complex. Headwall Photonics has integrated two of their off-the-shelf highly optimized hyperspectral imagers (Vis-NIR and SWIR) in an optically co-boresighted configuration together with a high performance data processor to produce a compact system which is easy to use and outputs wide spectrum pixel co-registered hyperspectral data. The process of pixel co-registration in this system is computationally very cheap enabling real-time wide-spectrum hyperspectral imaging applications. This paper presents actual imaging and performance data from these systems showing excellent pixel co-registration, sensitivity and spectral resolution.

A precision weed control system was developed to autonomously detect, identify and map weed species in the seedline of directly-seeded processing tomatoes and to apply a precise and lethal spray to the weed foliage. The weed sensor, based upon a hyperspectral imaging subsystem equipped with a line imaging spectrograph, had a spectral range of 385 - 810 nm at a 1.6 nm resolution and a spatial resolution of 0.4 mm across the seedline. Hyperspectral field images were collected in a processing tomato field in real-time from a continuously moving cultivation sled. A multivariate Bayesian classifier was constructed off-line to identify the plant species of each pixel in the hyperspectral images. At the pixel level, cross-validation results demonstrated a significant improvement in classifier performance on field data when site-specific classifier training was conducted. A site-specific classifier correctly recognized 95% of tomato foliage and more than 84%, of four weed species (black nightshade (Solanum nigrum L.), lambsquarter (Chenopodium album L.), red-root pigweed (Amaranthus retroflexus L.), and purslane (Portulaca oleracea L.)). At the plant level, nearly all plant species were correctly identified. The hyperspectral imaging system was robust in the identification of partially occluded foliage and provided an accurate weed map suitable for species-specific targeted herbicide application.

.Significance: Hyperspectral imaging (HSI) can support intraoperative perfusion assessment, the identification of tissue structures, and the detection of cancerous lesions. The practical use of HSI for minimal-invasive surgery is currently limited, for example, due to long acquisition times, missing video, or large set-ups.Aim: An HSI laparoscope is described and evaluated to address the requirements for clinical use and high-resolution spectral imaging.Approach: Reflectance measurements with reference objects and resected human tissue from 500 to 1000 nm are performed to show the consistency with an approved medical HSI device for open surgery. Varying object distances are investigated, and the signal-to-noise ratio (SNR) is determined for different light sources.Results: The handheld design enables real-time processing and visualization of HSI data during acquisition within 4.6 s. A color video is provided simultaneously and can be augmented with spectral information from push-broom imaging. The reflectance data from the HSI system for open surgery at 50 cm and the HSI laparoscope are consistent for object distances up to 10 cm. A standard rigid laparoscope in combination with a customized LED light source resulted in a mean SNR of 30 to 43 dB (500 to 950 nm).Conclusions: Compact and rapid HSI with a high spatial- and spectral-resolution is feasible in clinical practice. Our work may support future studies on minimally invasive HSI to reduce intra- and postoperative complications.

PDF HTML阅读 XML下载 导出引用 引用提醒 分子超光谱成像系统应用于糖网病药物疗效研究 DOI: 作者: 作者单位: 华东师范大学极化材料与器件教育部重点实验室,华东师范大学极化材料与器件教育部重点实验室,华东师范大学极化材料与器件教育部重点实验室,华东师范大学科学与技术跨学科高等研究院,华东师范大学极化材料与器件教育部重点实验室,中国科学院上海技术物理研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（面上项目，重点项目，重大项目）;国家教育部博士点基金 Evaluation of erythropoietin efficacy on diabetic retinopathy based on molecular hyperspectral imaging (MHSI) system Author: Affiliation: Key Laboratory of Polor Materials and Devices,East China Normal University,Shanghai,PRC,Key Laboratory of Polor Materials and Devices,East China Normal University,Shanghai,PRC,Key Laboratory of Polor Materials and Devices,East China Normal University,Shanghai,PRC,Institute for Advanced Interdisciplinary Research IAIR,East China Normal University,Shanghai,PRC,Key Laboratory of Polor Materials and Devices,East China Normal University,Shanghai,PRC,Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai,PRC Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:将成像技术和光谱技术相结合，再配合显微镜技术，研制了基于AOTF（acoustooptic tunable filters）的分子超光谱成像系统.使用该系统采集了正常、糖尿病和EPO(erythropoietin,促红细胞生长素)药物治疗的大鼠视网膜组织切片的分子超光谱图像数据.通过对正常组、糖尿病组、药物治疗组共30例样本的分子超光谱图像数据进行处理，获得了3组样本的单波段图像和伪彩色合成图像，并提取了各组样本外核层的典型透射光谱曲线.从图像上分析各组ONL (outer nuclear layer, 外核层)的厚度，由大到小依次为正常组、治疗组、糖尿病组，由于糖尿病会引起视网膜外核层细胞凋亡和厚度减少，实验结果表明经EPO治疗后可增加视网膜外核层的厚度.从光谱上分析各组的透射强度，糖尿病大鼠视网膜外核层组织在550~1000 nm光谱范围内的透射强度整体高于正常组，经EPO治疗后，透射强度介于正常组和糖尿病组之间；通过光谱相似性分析，治疗组与正常组之间的光谱相似性高于糖尿病与正常组.实验结果表明EPO能减少视网膜外核层细胞凋亡，对糖网病大鼠有一定的疗效.因而分子超光谱成像系统可以作为一种新的手段，辅助科研人员对糖网病的发病机理和致盲原因及药物疗效进行研究. Abstract:Integrate imaging technology, spectrum technology and microscope, a novel molecular hyperspectral imaging (MHSI) system based on AOTF (acoustooptic tunable filters) was presented. The system was used to capture hyperspectral images of rat retinal sections selected from three groups: normal(N), diabetic(D), and EPOtreated(E0). By processing hyperspectral images data of 30 samples, single band images and the pseudocolor images of N、D、E0 group are gained. Typical spectra curves of the ONL cells of N、D、E0 are extracted. By analyzing these images, descending order of the ONL thickness is N、E0、D. The experimental results cite that the ONL thickness of E0 group will be increased after EPO treated. By analyzing these spectrums, transmission intensity of D group is generally higher than that of N group in the spectral field from 550 nm to 1000nm and transmission intensity of E0 group is in the middle of D and N. By analyzing spectrum relations, results show the spectral signature of the E0 group was more similar to that of N than that of D to N. The above results suggest EPO may prevent cell apoptosis and has protective effect on rat DR at a certain level. MHSI system may probably provide a potential approach to researching pathogenesis、causes of blindness and drug efficacy of DR in future. 参考文献 相似文献 引证文献

Investigating the detection of peanuts in chopped nut products using hyperspectral imaging systems

Rooibos (Aspalathus linearis) and Honeybush (Cyclopia species) are teas indigenous to South Africa that are popularly consumed locally as well as internationally for its purported health benefits. Studies have confirmed anti-oxidant, antimutagenic and anticancer activity mainly due to the presence of polyphenols. Hyperspectral imaging (HSI) integrates conventional spectroscopy and imaging to obtain spectral and spatial information from a sample. The potential of short wave infrared (SWIR) hyperspectral imaging in combination with chemometric data analysis as a rapid quality control method for commercially important herbal teas was investigated. Images were acquired with the sisuChema short wave infrared (SWIR) hyperspectral pushbroom imaging system at a spectral range of 920 – 2514nm. Principal Component Analysis (PCA) was performed to remove background pixels and to visualize the data. Classes (i.e. Rooibos or Honeybush) were interactively assigned using the score images and plots. Partial Least Squares Discriminant Analysis (PLS-DA) classification models developed were subsequently used to accurately predict the species identity of commercially available herbal tea samples introduced as an external dataset, producing a visually interpretable result. The results showed that the labeling information is correct but that Honeybush tea is in most cases present in very low amounts. It is evident that hyperspectral imaging is an objective and non-destructive quality control method that can be successfully used to determine the botanical species included in herbal teas in addition to giving an indication of the abundance of each species in polyherbal tea mixtures.

Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400−1000 nm) and short-wave infrared (SWIR) (935−1720 nm) for predicting four strawberry quality attributes (firmness—FF, total soluble solid content—TSS, titratable acidity—TA, and dry matter—DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R2 = 0.959), DM (R2 = 0.947), and TA (R2 = 0.877), whereas good prediction was observed for FF (R2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R2 = 0.924 for DM; R2 = 0.898 for TSS; R2 = 0.953 for TA; R2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R2 = 0.942 for DM; R2 = 0. 981 for TSS; R2 = 0.976 for TA; R2 = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product’s marketability.

Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds

Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta