Real-time Multispectral Imaging Research Articles

Navel Orange Maturity Classification by Multispectral Indexes Based on Hyperspectral Diffuse Transmittance Imaging

Maturity grading is important for the quality of fruits. Nondestructive maturity detection can be greatly beneficial to the consumer and fruit industry. In this paper, a hyperspectral image of navel oranges was obtained using a diffuse transmittance imaging based system. Multispectral indexes were built to identify the maturity with the hyperspectral technique. Five indexes were proposed to combine the spectra at wavelengths of 640, 760 nm (red edges), and 670 nm (for chlorophyll content) to grade the navel oranges into three maturity stages. The index of (T670+T760-T640)/(T670+T760+T640) seemed to be more appropriate to classify maturity, especially to distinguish immature oranges that can be straightly identified in accordance with the value of this index ((T670+T760-T640)/(T670+T760+T640)). Different indexes were used as the input of linear discriminate analysis (LDA) and of k-nearest neighbor (k-NN) algorithm to identify the maturity, and it was found that k-NN with (T670+T760-T640)/(T670+T760+T640) could reach the highest correct classification rate of 96.0%. The results showed that the built index was feasible and accurate in the nondestructive classification of oranges based on the hyperspectral diffuse transmittance imaging. It will greatly help to develop low-cost and real-time multispectral imaging systems for the nondestructive detection of fruit quality in the industry.

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.

Advances in real-time multispectral optoacoustic imaging and its applications

Optoacoustic imaging, or photoacoustic imaging, is insensitive to photon scattering within biological tissue and, unlike conventional optical imaging methods, makes high-resolution optical visualization deep within tissue possible. Recent advances in laser technology, detection strategies and inversion techniques have led to significant improvements in the capabilities of optoacoustic systems. A key empowering feature is the development of video-rate multispectral imaging in two and three dimensions, which offers fast, spectral differentiation of distinct photoabsorbing moieties. We review recent advances and capabilities in the technology and its corresponding emerging biological and clinical applications.

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

In poultry processing plants, fecal material and ingesta are the primary source of carcass contamination with microbial pathogens. The current practice of the poultry inspection in the United States is primarily human visual observations. Since the visual inspection is becoming more challenging in poultry processing plants adopting high-speed lines, a rapid sorting system could significantly improve the detection and monitoring of carcasses with surface fecal material and ingesta. As a result, we developed a prototype line-scan hyperspectral imaging system configured as a real-time multispectral imaging subsystem for online detection of surface fecal material and ingesta. Specifically, we integrated a commercially available off-the-shelf hyperspectral image camera into the system with two line lights and a custom software program for real-time multispectral imaging. The bottleneck of the imaging system was the data acquisition. For that reason, a multithreaded software architecture was designed and implemented not only to meet the application requirements such as speed and detection accuracy, but also to be customizable to different imaging applications such as systemic disease detection in the future. The image acquisition and processing speed tests confirmed the system could operate to scan poultry carcasses in commercial poultry processing plants. The fecal detection algorithm was based on the previous research using different hyperspectral imaging systems. A new carcass detection and image formation algorithm was developed to allow existing image processing and detection algorithms reusable without any modifications. Sixteen chicken carcasses and four different types of fecal and ingesta samples were used in a study to test the imaging system at two different speeds (140 birds per minute and 180 birds per minute) in a pilot-scale poultry processing facility. The study found that the system could grab and process three waveband images of carcasses moving up to 180 birds per minute (a line-scan rate 286 Hz) and detect fecal material and ingesta on their surfaces. The detection accuracy of the system varied between 89% and 98% with minimum false positive errors (less than 1%), depending on tested detection algorithms. Therefore, these findings provide the basis of not only a commercially viable imaging platform for fecal detection but also a single poultry inspection system for multiple tasks such as systemic disease detection and quality sorting.

Enhancing Surgical Vision by Using Real-Time Imaging of αvβ3-Integrin Targeted Near-Infrared Fluorescent Agent

This study was designed to improve the surgical procedure and outcome of cancer surgery by means of real-time molecular imaging feedback of tumor spread and margin delineation using targeted near-infrared fluorescent probes with specificity to tumor biomarkers. Surgical excision of cancer often is confronted with difficulties in the identification of cancer spread and the accurate delineation of tumor margins. Currently, the assessment of tumor borders is afforded by postoperative pathology or, less reliably, intraoperative frozen sectioning. Fluorescence imaging is a natural modality for intraoperative use by directly relating to the surgeon's vision and offers highly attractive characteristics, such as high-resolution, sensitivity, and portability. Via the use of targeted probes it also becomes highly tumor-specific and can lead to significant improvements in surgical procedures and outcome. Mice bearing xenograft human tumors were injected with αvβ3-integrin receptor-targeted fluorescent probe and in vivo visualized by using a novel, real-time, multispectral fluorescence imaging system. Confirmatory ex vivo imaging, bioluminescence imaging, and histopathology were used to validate the in vivo findings. Fluorescence images were all in good correspondence with the confirming bioluminescence images in respect to signal colocalization. Fluorescence imaging detected all tumors and successfully guided total tumor excision by effectively detecting small tumor residuals, which occasionally were missed by the surgeon. Tumor tissue exhibited target-to-background ratio of ~4.0, which was significantly higher compared with white-light images representing the visual contrast. Histopathology confirmed the capability of the method to identify tumor negative margins with high specificity and better prediction rate compared with visual inspection. Real-time multispectral fluorescence imaging using tumor specific molecular probes is a promising modality for tumor excision by offering real time feedback to the surgeon in the operating room.

In-plant Test of In-line Multispectral Imaging System For Fecal Detection during Poultry Processing

Food Safety Inspection Service established new requirements for the zero tolerance for fecal contaminated broiler carcasses entering the chiller. The hyperspectral imaging research accomplished at ARS has demonstrated the real-time multispectral imaging system was able to effectively detect fecal contaminants during poultry processing. The imaging system with two-band (517 and 565 nm) was able to inspect every bird in real-time, in-line mode at commercial processing speed. With several real-time image processing methods including binning, cuticle removal filter, median filter, and morphological analysis, the system performance was improved by maximizing detection accuracy and minimizing false positive errors. Most false positives were found at wing bone, thigh, vent, and boundary. Since the number of true positives confirmed organoleptically by human inspector was only 14 out of 29,821 carcasses (0.05%), more samples need to be tested to confirm the performance of detection accuracy. Overall, the ARS multispectral imaging system was able to detect contaminants with high detection accuracy (approximately 91%) and 3.3% false positive errors at the processing speed of 150 birds/min from the two-day in-plant trials. However, the performance of ARS real-time in-line imaging system for fecal contaminant detection on broiler carcasses can be improved by adopting a better camera which has higher signal-to-noise ratio such as line-scan real-time hyperspectral imaging system currently being investigated for its applicability for real-time processing.

Adaptive Image Processing Methods for Improving Contaminant Detection Accuracy on Poultry Carcasses

A real-time multispectral imaging system has demonstrated a science-based tool for fecal and ingesta contaminant detection during poultry processing. In order to implement this imaging system in the commercial poultry processing industry, the false positives must be removed. To do this, we tested and implemented additional image processing algorithms including binning, cuticle removal filter, median filtering, and morphological analysis in real-time mode to maximize detection accuracy and minimize false positives (FPs). The median filtering and binning processes were able to reduce FPs up to 98.7% and 95.2%, respectively, by eliminating most of the salt-and-pepper noise from the raw images. The detection accuracy varied with the parameter values of the image processing algorithms, including binning, thresholding, median filtering, and morphological analysis. Overall contaminant detection accuracy on moving birds varied from 84.3% to 97.8%, and FP errors were 1.9% and 41.8%, respectively. Although neither the overall detection accuracy nor FP errors were affected by the camera gain, the detection accuracy results changed slightly, from 87.4% to 95.1%. In this case, the FP errors were 1.8% and 15.9%, respectively. Thus, the USDA-ARS multispectral imaging system was able to detect contaminants with 91.6% accuracy and 3.3% FP errors by selecting optimum image processing methods at a processing speed of 140 birds per minute.

Real-time multispectral imaging system for online poultry fecal inspection using unified modeling language

A prototype real-time multispectral imaging system for fecal and ingesta contaminant detection on broiler carcasses has been developed. The prototype system includes a common aperture camera with three optical trim filters (517, 565 and 802-nm wavelength), which were selected by visible/NIR spectroscopy and validated by a hyperspectral imaging system with decision tree algorithm. The on-line testing results showed that the multispectral imaging technique can be used effectively for detecting feces (from duodenum, ceca, and colon) and ingesta on the surface of poultry carcasses with a processing speed of 140 birds per minute. This paper demonstrated both multispectral imaging hardware and real-time image processing software. For the software development, the Unified Modeling Language (UML) design approach was used for on-line application. The UML models included class, object, activity, sequence, and collaboration diagram. User interface model included 17 inputs and 6 outputs. A window-based real-time image processing software composed of 11 components, which represented class, architecture, and activity. Both hardware and software for a real-time fecal detection were tested at the pilot-scale poultry processing plant. The run-time of the software including online calibration was fast enough to inspect carcasses on-line with an industry requirement. Based on the preliminary test at the pilot-scale processing line, the system was able to acquire poultry images in real-time. According to the test results, the imaging system is reliable for the harsh environments and UML-based image processing software is flexible and easy to be updated when additional parameters are needed for in-plant trials.

Optimization of Fecal Detection Using Hyperspectral Imaging and Kernel Density Estimation

This article addresses the development of an iterative search algorithm to find an optimal threshold to detect surface contaminants on poultry carcasses for a real-time multispectral imaging application. Previous studies showed that a band-ratio algorithm with a 517 nm band and a 565 nm band could detect contaminants on the surface of poultry carcasses. In this study, thresholding for the band-ratio algorithm was optimized in a statistical sense. A fundamental problem of the thresholding is that there is a theoretical performance bound from the standpoint of statistical hypothesis testing. In a Neyman-Pearson (NP) framework, a lower bound of detection accuracy can be determined for minimizing false positives. An iterative search algorithm was designed to obtain an optimal threshold in the NP framework. For the design of the search algorithm, statistical density distributions of fecal and non-fecal image data were estimated by kernel density estimation, and characterized by edge models on a projection axis perpendicular to a linear decision boundary. Three necessary criteria were investigated for the selection of the optimum threshold of the band-ratio algorithm. Numerical simulations with hyperspectral poultry images showed that the optimum threshold was 1.05.

Real time multispectral high temperature measurement: Application to control in the industry

Many devices are used to realize non-contact temperature measurements. Whenever the body to be controlled behaves as a black body, all the devices inferring the true temperature from the body radiation are accurate and reliable. On the other hand, when it exhibits a behavior different from the black body, emissivity compensations need to be done. In case of a known emissivity, the spectral system (single wavelength) is used whereas, for gray body (unknown but constant emissivity in a narrow bandwidth) a bicolor system is more likely to be utilized. For all other cases, assumptions about the emissivity variations as a function of the wavelength and the temperature have to be ascertained and multispectral system or “hybrid-systems” which are a mix between spectral and multispectral systems have been built and used in particular cases. In this paper, a real time multispectral imaging system based on two CCD cameras is presented. The system is herein carefully characterized and applications such as vision control system are presented. An extension of the system with three cameras is also exposed in the perspectives.

Real-time Multispectral Image Processing for Medical Imaging

Real-time Multispectral Image Processing for Medical Imaging

Real-time Multi-spectral Image Processing for Mapping Pigmentation in Human Skin

Real-time Multi-spectral Image Processing for Mapping Pigmentation in Human Skin

REAL-TIME DUAL-WAVELENGTH IMAGE PROCESSING FOR POULTRY SAFETY INSPECTION

Real-time multispectral image processing algorithms were developed for online poultry carcass inspection. Neural network models with different learning rules (delta and hyperbolic tangent) and transfer functions (sigmoid and normcum-sigmoid) were examined using features extracted from spectral images at 540 nm and 700 nm. The classification accuracy using dual wavelength spectral images was much higher than single wavelength spectral images in identifying unwholesome poultry carcasses. The spectral image features at 700 nm were useful to identify wholesome carcasses, while the combination of spectral image features at 540 nm, 700 nm, and their ratio improved the classification accuracy of unwholesome carcasses. The optimum neural classifier utilized delta learning rule and hyperbolic tangent transfer function. The classification accuracy was 91.1 % for wholesome and 83.3 % for unwholesome carcasses when the spectral images of all 540 nm, 700 nm, and their ratio were used as inputs to the neural network model.