A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Development status of novel spectral imaging techniques and application to traditional Chinese medicine

Traditional Chinese medicine(TCM)is a treasure of the Chinese nation,providing effective solutions to current medical requisites.Various spectral techniques are undergoing continuous development and provide new and reliable means for evaluating the efficacy and quality of TCM.Because spectral techniques are noninvasive,convenient,and sensitive,they have been widely applied to in vitro and in vivo TCM evaluation systems.In this paper,previous achievements and current progress in the research on spectral technologies(including fluorescence spectroscopy,photoacoustic imaging,infrared thermal imaging,laser-induced breakdown spectroscopy,hyperspectral imaging,and surface enhanced Raman spectroscopy)are discussed.The advantages and disadvantages of each technology are also presented.Moreover,the future applications of spectral imaging to identify the origins,components,and pesticide residues of TCM in vitro are elucidated.Subsequently,the evaluation of the efficacy of TCM in vivo is presented.Identifying future applications of spectral imaging is anticipated to promote medical research as well as scientific and technological explorations.

Research on the detection of early caries based on hyperspectral imaging

Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument that could be used for screening and detection of early dentalcaries.Methods:Eighteen extracted human teeth(molars and premolars),with varying degrees ofnatural pathology and no degree of decay involving dentin were obtained.HSI system with awavelength range from 400 to 1000nm was used to obtain images of all 18 teeth containingsound,carious and pigmented areas.We compared the spectra of the wavebands at both 500 nmand 780 nm from the different tooth states,and the reflectance diference bet ween sound versuscarious lesions and sound versus pigmented areas,respectively.Results:There was a slight diference in refectance bet ween carious areas and pigmented areas at500 nm.A substantial difference was additionally noted in refectance bet ween carious areas andpigmented areas at 780 nm.Conclusion:The results have shown that the interference of tooth surface pigment can be elim-inated in the near-infrared(NIR)waveband,and the caries can be effectively identifed from the pigmented areas.Thus,it could be used to detect carious areas of teeth in place of the traditionalvisual inspection method or white light endoscopy.Clinical significance:The NIR difused light signal enables the identification of early caries frompigment and other interference,providing a reasonable detection tool for early detection andearly treatment of teeth diseases.

NEAR-INFRARED,BROAD-BAND SPECTRAL IMAGING OF THE HUMAN BREAST FOR QUANTITATIVE OXIMETRY:APPLICATIONS TO HEALTHY AND CANCEROUS BREASTS

We have examined ten human subjects with a previously developed instrument for near-infrared diffuse spectral imaging of the female breast.The instrument is based on a tandem,planar scan of two collinear optical fibers(one for illumination and one for collection)to image a gently compressed breast in a transmission geometry.The optical data collection features a spatial sampling of 25 points/cm2 over the whole breast,and a spectral sampling of 2 points/nm in the 650-900nm wavelength range.Of the ten human subjects examined,eight are healthy subjects and two are cancer patients with unilateral invasive ductal carcinoma and ductal carcinoma in situ,respectively.For each subject,we generate second-derivative images that identify a network of highly absorbing structures in the breast that we assign to blood vessels.A previously developed paired-wavelength spectral method assigns oxygenation values to the absorbing structures displayed in the second-derivative images.The resulting oxygenation images feature average values over the whole breast that are significantly lower in cancerous breasts(69±14%,n=2)than in healthy breasts(85±7%,n=18)(p<0.01).Furthermore,in the two patients with breast cancer,the average oxygenation values in the cancerous regions are also significantly lower than in the remainder of the breast(invasive ductal carcinoma:49±11%vs 61±16%,p<0.01;ductal carcinoma in situ:58±8%vs 77±11%,p<0.001).

Application of gemstone spectral imaging for efficacy evaluation in hepatocellular carcinoma after transarterial chemoembolization

AIM: To assess the value of gemstone spectral imaging(GSI) in efficacy evaluation in hepatocellular cancer(HCC) after transcatheter arterial chemoembolization(TACE) treatment.METHODS: Thirty patients with HCC underwent GSI, including nonenhanced, arterial, portalvenous and delayed phase scans, after TACE treatment. Arterial phase images were acquired with GSI for reconstruction of virtual nonenhanced images and color overlay images. Digital subtraction angiography(DSA) was performed in all these patients. Two blinded and independent readers evaluated the data in two reading sessions; standard nonenhanced, arterial, portalvenous, and delayed phase images were read in session A, and the optimal monochromatic images, iodine/water based images and spectrum features were read in session B. Sensitivity and specificity were calculated with the DSA data as the reference standard. The sensitivity and specificity were compared using the χ2 test.RESULTS: DSA revealed 154 lesions in 30 patients, and 100 of them had blood supply. Overall sensitivity and specificity were 72%( 72/100) and 77.8 %(42/54) for session A, and 97%(97/100) and 94.4%(51/54) for session B, respectively. The sensitivity and specificity of the two reading sessions were significantly different(χ2 = 23.04, χ2 = 7.11, P < 0.05).CONCLUSION: Compared with conventional CT, GSI could significantly improve the detection of small and multiple lesions without increasing the radiation dose. Based on spectrum features, GSI could assess tumor homogeneity and more accurately identify residual tumors and recurrent or metastatic lesions during efficacy evaluation and follow-up in HCC after TACE treatment.

Ultraslim endoscopy with flexible spectral imaging color enhancement for upper gastrointestinal neoplasms

AIM:To conduct a preliminary study on the effect of flexible spectral imaging color enhancement (FICE) used in combination with ultraslim endoscopy by focusing on the enhanced contrast between tumor and non-tumor lesions. METHODS: We examined 50 lesions of 40 patients with epithelial tumors of the upper gastrointestinal tract before endoscopic submucosal dissection using ultraslim endoscopy with conventional natural color imag ing and with FICE imaging. We retrospectively invest igated the effect of the use of FICE on endoscopic diagn osis in comparison with normal light. RESULTS: Visibility of the epithelial tumors of the upper gastrointestinal tract with FICE was superior to normal light in 54% of the observations and comparable to normal light in 46% of the observations. There was no lesion for which visibility with FICE was inferior to that with normal light. FICE visualized 69.6% of hyperemic lesions and 58.8% of discolored lesions better than conventional endoscopy with natural color imaging. FICE sign if icantly improved the visibility of lesions with hyp ere mia or discoloration compared with normocolored lesions. CONCLUSION: This study suggests that the use of FICE would improve the ability of ultraslim endoscopy to detect epithelial tumors of the upper gastrointestinal tract.

A Study of Estimation Model for the Chlorophyll Content of Wheat Leaf Based on Hyperspectral Imaging

In order to explore the spectral features and sensitive wave band of wheat leaf,we establish a quantitative relationship model between wheat chlorophyll content and spectral features to promote the application of hyperspectral technology in precise wheat fertilization and fast,non-destructive growth monitoring.Using the relational analysis,we analyze the relationship between chlorophyll content and spectral reflectance or the first derivative,and establish the chlorophyll content monitoring model.By selection and verification,the best estimation models for wheat chlorophyll content are as follows:SPAD = 36.75 + 188.168R387,SPAD =2094.242R'7153+ 112646.744 R'7152-1.561E7 R'715+42.991.The two models can well estimate the SPAD value of wheat leaf,and comparatively speaking,the SPAD estimation model based on wave band R387 has greater accuracy.

Incoherent digital holographic spectral imaging with high accuracy of image pixel registration

Fresnel incoherent correlation holography(FINCH) is a unique three-dimensional(3D) imaging technique which has the advantages of scanning-free,high resolution,and easy matching with existing mature optical systems.In this article,an incoherent digital holographic spectral imaging method with high accuracy of spectral reconstruction based on liquid crystal tunable filter(LCTF) and FINCH is proposed.Using the programmable characteristics of spatial light modulator(SLM),a series of phase masks,none of whose focal lengths changes with wavelength,is designed and made.For each wavelength of LCTF output,SLM calls three phase masks with different phase constants at the corresponding wavelength,and CCD records three holograms.The spectral images obtained by this method have a constant magnification,which can achieve pixel-level image registration,restrain image registration errors,and improve spectral reconstruction accuracy.The results show that this method can not only obtain the 3D spatial information and spectral information of the object simultaneously,but also have high accuracy of spectral reconstruction and excellent color reproducibility.

A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images.Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental conditions.Here,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions.Accordingly,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images.The divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDR algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize endmembers.The endmember with the minimum error is chosen as the final endmember in each specific category.The proposed method is simple and automatically considers endmember variability in hyperspectral images.The efficiency of the proposed method is evaluated using two real hyperspectral datasets.The average spectral angle and abundance angle are used to analyze the performance measures.

Feasibility Study for Applying Spectral Imaging for Wheat Grain Authenticity Testing in Pasta

Authentication of pasta is currently determined using molecular biology-based techniques focusing on DNA as the target analyte. Whilst proven to be effective, these approaches can be criticised as being destructive, time consuming, and requiring specialist instrument training. Advances in the field of multispectral imaging (MSI) and hyperspectral imaging (HSI) have facilitated the development of compact imaging platforms with the capability to rapidly differentiate a range of materials (inclusive of grains and seeds) based on surface colour, texture and chemical composition. This preliminary investigation evaluated the applicability of spectral imaging for identification and quantitation of durum wheat grain samples in relation to pasta authenticity. MSI and HSI were capable of rapidly distinguishing between durum wheat and adulterant common wheat cultivars and assigning percentage adulteration levels characterised by low biases and good repeatability estimates. The results demonstrated the potential for spectral imaging based seed/grain adulteration testing to augment existing standard molecular approaches for food authenticity testing.

Hypergraph Regularized Deep Autoencoder for Unsupervised Unmixing Hyperspectral Images

Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(HSIs)due to its powerful ability of feature extraction and data reconstruction.However,most existing AE-based unmixing algorithms usually ignore the spatial information of HSIs.To solve this problem,a hypergraph regularized deep autoencoder(HGAE)is proposed for unmixing.Firstly,the traditional AE architecture is specifically improved as an unsupervised unmixing framework.Secondly,hypergraph learning is employed to reformulate the loss function,which facilitates the expression of high-order similarity among locally neighboring pixels and promotes the consistency of their abundances.Moreover,L_(1/2)norm is further used to enhance abundances sparsity.Finally,the experiments on simulated data,real hyperspectral remote sensing images,and textile cloth images are used to verify that the proposed method can perform better than several state-of-the-art unmixing algorithms.

Detection of illegally added drugs in dietary supplements by near-infrared spectral imaging

The application to detect ilally added drugs in dietary supplerments by near-infrared spectral imaging was studied with the focus on nifedipine,diclofenac and metformin.The method is based on near-infrared spectral images correlation cofficient to detect ilally added drugs.The results comply 100%with HPLC methods test results with no false positive results.

SPECTRAL IMAGING AND ANALYSIS TO YIELD TISSUE OPTICAL PROPERTIES

An introduction to the basics of spectral imaging as applied to biological tissues is presented.An example of a spectral image of a face is used to demonstrate the data and spectral analysis that specify the melanin content(M),blood content(B),tissue oxygen saturation(S),water content(W),fraction of scattering due to Rayleigh scattering(f)and due to Mie scattering(1−f),and the reduced scattering coefficient at 500-nm wavelength(µ
s 500 nm).The sensitivity of reflectance spectra to variation in the various parameters is illustrated.

Double-AOTF-based aberration-free spectral imaging endoscopic system for biomedical applications

The problem of in vrivo photoluminescence diagnostics of the tissues acessible by endoscopes is discussed.The spectral imaging module attachable to conventional rigid and flexible medical endoscopes is developed and described.It is based on a double acousto-optical tunable filter(AOTF)and a specialized optical coupling system.The module provides wide field of view(FOV),absence of image distortions,random spectral access,fast spectral image acquisition at any wavelength in the visible range and accurate measurement of reflectance spectrum in each pixel of the image.Images of typical biomedical samples are presented and discussed.Their spectra are compared to the reference data.

Demands and technical developments of clinical flow cytometry with emphasis in quantitative,spectral,and imaging capabilities

As the gold-standard method for single-cell analysis,flow cytometry enables high-throughput and multiple-parameter characterization of individual biological cells.This review highlights the demands for clinical flow cytometry in laboratory hematology(e.g.,diagnoses of minimal residual disease and various types of leukemia),summarizes state-of-the-art clinical flow cytometers(e.g.,FACSLyricTMby Becton Dickinson,DxFLEX by Beckman Coulter),then considers innovative technical improvements in flow cytometry(including quantitative,spectral,and imaging approaches)to address the limitations of clinical flow cytometry in hematology diagnosis.Finally,driven by these clinical demands,future developments in clinical flow cytometry are suggested.

Bitumen Removal Determination on Asphalt Pavement Using Digital Imaging Processing and Spectral Analysis

This research aims to define an efficient and fast quantification of bitumen removal on the road surface by Digital Imaging Processing (DIP) and spectral analysis. The retrieval of bitumen removal is an important issue for road management and environmental studies related to asphalt wear and environmental pollution. The calculation of the Exposed Aggregate Index (EAI), based on DIP, allows to quantify in each frame the superficial removal of bitumen and the exposure of aggregates. A procedure, based on non-parametric classification process of digital images, gives a fast response of EAI. A correlation among EAI and spectral data, between 390 nm and 900 nm range, is evaluated. Results show a good correlation between spectral data at different wavelength and EAI. Finally, this work evaluates the possibility to retrieve asphalt bitumen removal through remote sensed imagery.

Piecewise spectrally band-pass for compressive coded aperture spectral imaging

Coded aperture snapshot spectral imaging(CASSI) has been discussed in recent years. It has the remarkable advantages of high optical throughput, snapshot imaging, etc. The entire spatial-spectral data-cube can be reconstructed with just a single two-dimensional(2D) compressive sensing measurement. On the other hand, for less spectrally sparse scenes,the insufficiency of sparse sampling and aliasing in spatial-spectral images reduce the accuracy of reconstructed threedimensional(3D) spectral cube. To solve this problem, this paper extends the improved CASSI. A band-pass filter array is mounted on the coded mask, and then the first image plane is divided into some continuous spectral sub-band areas. The entire 3D spectral cube could be captured by the relative movement between the object and the instrument. The principle analysis and imaging simulation are presented. Compared with peak signal-to-noise ratio(PSNR) and the information entropy of the reconstructed images at different numbers of spectral sub-band areas, the reconstructed 3D spectral cube reveals an observable improvement in the reconstruction fidelity, with an increase in the number of the sub-bands and a simultaneous decrease in the number of spectral channels of each sub-band.

Optimized energy thresholds in a spectral computed tomography scan for contrast agent imaging

Spectral computed tomography(CT) based on photon counting detectors(PCDs) is a well-researched topic in the field of X-ray imaging. When PCD is applied in a spectral CT system, the PCD energy thresholds must be carefully selected, especially for K-edge imaging, which is an important spectral CT application. This paper presents a threshold selection method that yields better-quality images in K-edge imaging. The main idea is to optimize the energy thresholds ray-by-ray according to the targeted component coefficients, followed by obtaining an overall optimal energy threshold by frequency voting. A low-dose pre-scan is used in practical implementations to estimate the line integrals of the component coefficients for the basis functions. The variance of the decomposed component coefficients is then minimized using the Cramer–Rao lower bound method with respect to the energy thresholds. The optimal energy thresholds are then used to take a full scan and gain better image reconstruction with less noise than would be given by a full scan using the non-optimal energy thresholds. Simulations and practical experiments on imaging iodine and gadolinium solutions, which are commonly used as contrast agents in medical applications, were used to validate the method. The noise was significantly reduced with the same dose relative to the non-optimal energy thresholds in both simulations and in practical experiments.

Research on Spectral Polarization Imaging Technique

The mechanism and characteristics of spectral polarization imaging technique are presented. The present research and developing trend of spectral polarization remote sensing are introduced. A novel method of spectral polarization imaging technique is discussed, which is based on static intensity modulation adding with double refraction crystal spectrometer. The static intensity modulation consists of two retarders and one polarizer. The double refraction crystal is used to generate interference image. The spectral and four Stokes vectors information can be obtained only by one measurement. The method of static intensity modulation is deduced in detail and is simulated by computer. The spectropolarimeter experimental system is also established in the laboratory. The basic concept of the technique is verified.

Novel approach for the automated detection of allergy test using spectral imaging

This paper proposes a novel approach for the automatic detection of allergy test (allergy lesion). A hyperspectral microscope system was used to image the test samples which were diagnosed by dermatologist. It was found that allergy of different levels, and healthy skin cells show absorption spectra, which are sufficiently characteristic and yet reproducible enough to allow for differentiation when using a spectroscopic system. Principal components analysis was used to extract relevant features that could be used for classification from these spectra. Preliminary results indicate that the different types of allergy cells can be reliably distinguished by these features. We conclude that hyperspectral microscopic analysis is a promising approach for improving and automating the diagnosis of allergy test as well as another skin lesions.