Optical design of a novel near-infrared phase contrast imaging(NI-PCI)diagnostic on the HL-2A tokamak

The optical design of near-infrared phase contrast imaging(NI-PCI)diagnosis on HL-2A is introduced in this paper.This scheme benefits from the great progress of near-infrared laser technology and is a broadening of traditional phase contrast technology.This diagnostic can work as a keen tool to measure plasma wavenumber spectra by inferring string-integrated plasma density fluctuations.Design of both the front optical path which is the path before the laser transmitting into the tokamak plasma and the rear optics which is the path after the laser passing through the plasma is detailed.The 1550 nm laser is chosen as the probe beam and highprecision optical components are designed to fit the laser beam,in which a phase plate with a 194-nm-deep silver groove is the key.Compared with the conventional 10.6μm laser-based PCI system on HL-2A,NI-PCI significantly overcomes the unwanted phase scintillation effect and promotes the measurement capability of high-wavenumber turbulence with an increased maximal measurable wavenumber from 15 cm^(-1)to 32.6 cm^(-1).

Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

Meibomian glands segmentation in infrared images with limited annotation

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

Real-time and high-transmission middle-infrared optical imaging system based on a pixel-wise metasurface micro-polarization array

Real-time polarization medium-wave infrared(MIR)optical imaging systems enable the acquisition of infrared and polarization information for a target.At present,real-time polarization MIR devices face the following problems:poor real-time performance,low transmission and high requirements for fabrication and integration.Herein,we aim to improve the performance of real-time polarization imaging systems in the MIR waveband and solve the above-mentioned defects.Therefore,we propose a MIR polarization imaging system to achieve real-time polarization-modulated imaging with high transmission as well as improved performance based on a pixel-wise metasurface micro-polarization array(PMMPA).The PMMPA element comprises several linear polarization(LP)filters with different polarization angles.The optimization results demonstrate that the transmittance of the center field of view for the LP filters is up to 77%at a wavelength of4.0μm and an extinction ratio of 88 d B.In addition,a near-diffraction-limited real-time MIR imaging optical system is designed with a field of view of 5°and an F-number of 2.The simulation results show that an MIR polarization imaging system with excellent real-time performance and high transmission is achieved by using the optimized PMMPA element.Therefore,the method is compatible with the available optical system design technologies and provides a way to realize real-time polarization imaging in MIR wavebands.

Evaluation of facial temperature distribution changes during meditation using infrared thermal imaging:An experimental,cross-over study

Objective:To investigate the differences between meditation and resting states using infrared thermal imaging(IRTI)to determine facial temperature distribution features during meditation and annotate the patterns of facial temperature changes during meditation from the perspective of traditional Chinese medicine facial diagnosis.Methods:Each participant performed 10 min meditation and 10 min resting but in different sequences.A concentration test was set as the task load,followed by a meditation/resting or resting/meditation session,during which the participants'facial temperatures were observed using IRTI.Participants were scored on the Big Five Inventory(BFI)and Mindful Attention Awareness Scale(MAAS).Results:Forehead temperatures decreased more during meditation than during the resting state.The chin temperature increased only during meditation(P<.0001).For the subjects with meditation experience,there were significant differences in the temperatures of the left forehead(P<.01),right forehead(P<.01)and chin(P<.05)between the meditation and resting state at the 10~(th)min.In the nontask state,the BFI-Extraversion showed a negative correlation with the temperature of the left forehead(R=-0.41,P=.03).In the post-task state,the temperature of the left forehead was negatively correlated with scores on the MAAS(R=-0.42,P=.02).Conclusion:Using IRTI to study meditation offers a practical solution to the challenges in meditation research.The results indicate that an increase in chin temperature may be a representative feature of a meditation state,and forehead temperature is also a potential indicator.

Feasibility study of assessing cotton fiber maturity from near infrared hyperspectral imaging technique

Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.

Heat transfer and temperature evolution in underground mininginduced overburden fracture and ground fissures: Optimal time window of UAV infrared monitoring

Heat transfer and temperature evolution in overburden fracture and ground fissures are one of the essential topics for the identification of ground fissures via unmanned aerial vehicle(UAV) infrared imager. In this study, discrete element software UDEC was employed to investigate the overburden fracture field under different mining conditions. Multiphysics software COMSOL were employed to investigate heat transfer and temperature evolution of overburden fracture and ground fissures under the influence of mining condition, fissure depth, fissure width, and month alternation. The UAV infrared field measurements also provided a calibration for numerical simulation. The results showed that for ground fissures connected to underground goaf(Fissure Ⅰ), the temperature difference increased with larger mining height and shallow buried depth. In addition, Fissure Ⅰ located in the boundary of the goaf have a greater temperature difference and is easier to be identified than fissures located above the mining goaf. For ground fissures having no connection to underground goaf(Fissure Ⅱ), the heat transfer is affected by the internal resistance of the overlying strata fracture when the depth of Fissure Ⅱ is greater than10 m, the temperature of Fissure Ⅱ gradually equals to the ground temperature as the fissures’ depth increases, and the fissures are difficult to be identified. The identification effect is most obvious for fissures larger than 16 cm under the same depth. In spring and summer, UAV infrared identification of mining fissures should be carried out during nighttime. This study provides the basis for the optimal time and season for the UAV infrared identification of different types of mining ground fissures.

IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection

Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents.To tackle the challenges posed by the low recognition accuracy and the substan-tial computational burden associated with current infrared pedestrian-vehicle detection methods,an infrared pedestrian-vehicle detection method A proposal is presented,based on an enhanced version of You Only Look Once version 5(YOLOv5).First,A head specifically designed for detecting small targets has been integrated into the model to make full use of shallow feature information to enhance the accuracy in detecting small targets.Second,the Focal Generalized Intersection over Union(GIoU)is employed as an alternative to the original loss function to address issues related to target overlap and category imbalance.Third,the distribution shift convolution optimization feature extraction operator is used to alleviate the computational burden of the model without significantly compromising detection accuracy.The test results of the improved algorithm show that its average accuracy(mAP)reaches 90.1%.Specifically,the Giga Floating Point Operations Per second(GFLOPs)of the improved algorithm is only 9.1.In contrast,the improved algorithms outperformed the other algorithms on similar GFLOPs,such as YOLOv6n(11.9),YOLOv8n(8.7),YOLOv7t(13.2)and YOLOv5s(16.0).The mAPs that are 4.4%,3%,3.5%,and 1.7%greater than those of these algorithms show that the improved algorithm achieves higher accuracy in target detection tasks under similar computational resource overhead.On the other hand,compared with other algorithms such as YOLOv8l(91.1%),YOLOv6l(89.5%),YOLOv7(90.8%),and YOLOv3(90.1%),the improved algorithm needs only 5.5%,2.3%,8.6%,and 2.3%,respectively,of the GFLOPs.The improved algorithm has shown significant advancements in balancing accuracy and computational efficiency,making it promising for practical use in resource-limited scenarios.

Application value of indocyanine green fluorescence imaging in guiding sentinel lymph node biopsy diagnosis of gastric cancer: Meta-analysis

BACKGROUND Gastric cancer is a common malignant tumor of the digestive system worldwide,and its early diagnosis is crucial to improve the survival rate of patients.Indocyanine green fluorescence imaging(ICG-FI),as a new imaging technology,has shown potential application prospects in oncology surgery.The meta-analysis to study the application value of ICG-FI in the diagnosis of gastric cancer sentinel lymph node biopsy is helpful to comprehensively evaluate the clinical effect of this technology and provide more reliable guidance for clinical practice.AIM To assess the diagnostic efficacy of optical imaging in conjunction with indocya-nine green(ICG)-guided sentinel lymph node(SLN)biopsy for gastric cancer.METHODS Electronic databases such as PubMed,Embase,Medline,Web of Science,and the Cochrane Library were searched for prospective diagnostic tests of optical imaging combined with ICG-guided SLN biopsy.Stata 12.0 software was used for analysis by combining the"bivariable mixed effect model"with the"midas"command.The true positive value,false positive value,false negative value,true negative value,and other information from the included literature were extracted.A literature quality assessment map was drawn to describe the overall quality of the included literature.A forest plot was used for heterogeneity analysis,and P<0.01 was considered to indicate statistical significance.A funnel plot was used to assess publication bias,and P<0.1 was considered to indicate statistical significance.The summary receiver operating characteristic(SROC)curve was used to calculate the area under the curve(AUC)to determine the diagnostic accuracy.If there was interstudy heterogeneity(I2>50%),meta-regression analysis and subgroup analysis were performed.analysis were performed.RESULTS Optical imaging involves two methods:Near-infrared(NIR)imaging and fluorescence imaging.A combination of optical imaging and ICG-guided SLN biopsy was useful for diagnosis.The positive likelihood ratio was 30.39(95%CI:0.92-1.00),the sensitivity was 0.95(95%CI:0.82-0.99),and the specificity was 1.00(95%CI:0.92-1.00).The negative likelihood ratio was 0.05(95%CI:0.01-0.20),the diagnostic odds ratio was 225.54(95%CI:88.81-572.77),and the SROC AUC was 1.00(95%CI:The crucial values were sensitivity=0.95(95%CI:0.82-0.99)and specificity=1.00(95%CI:0.92-1.00).The Deeks method revealed that the"diagnostic odds ratio"funnel plot of SLN biopsy for gastric cancer was significantly asymmetrical(P=0.01),suggesting significant publication bias.Further meta-subgroup analysis revealed that,compared with fluorescence imaging,NIR imaging had greater sensitivity(0.98 vs 0.73).Compared with optical imaging immediately after ICG injection,optical imaging after 20 minutes obtained greater sensitivity(0.98 vs 0.70).Compared with that of patients with an average SLN detection number<4,the sensitivity of patients with a SLN detection number≥4 was greater(0.96 vs 0.68).Compared with hematoxylin-eosin(HE)staining,immunohistochemical(+HE)staining showed greater sensitivity(0.99 vs 0.84).Compared with subserous injection of ICG,submucosal injection achieved greater sensitivity(0.98 vs 0.40).Compared with 5 g/L ICG,0.5 and 0.05 g/L ICG had greater sensitivity(0.98 vs 0.83),and cT1 stage had greater sensitivity(0.96 vs 0.72)than cT2 to cT3 clinical stage.Compared with that of patients≤26,the sensitivity of patients>26 was greater(0.96 vs 0.65).Compared with the literature published before 2010,the sensitivity of the literature published after 2010 was greater(0.97 vs 0.81),and the differences were statistically significant(all P<0.05).CONCLUSION For the diagnosis of stomach cancer,optical imaging in conjunction with ICG-guided SLN biopsy is a therapeut-ically viable approach,especially for early gastric cancer.The concentration of ICG used in the SLN biopsy of gastric cancer may be too high.Moreover,NIR imaging is better than fluorescence imaging and may obtain higher sensitivity.

Model-based deep learning for fiber bundle infrared image restoration

As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is proposed.Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep learning.By building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated simultaneously.Numerical results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise.On the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements.The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.

Research on Infrared Image Fusion Technology Based on Road Crack Detection

This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.

Near-Infrared Imaging Using a High-Speed Monitoring Near Infrared Hyperspectral Camera(Compovision)

This review paper reports near-infrared(NIR)imaging studies using a newly-developed NIR camera,Compovision.Compovision can measure a significantly wide area of 150mm×250mm at high speed of between 2and 5s.It enables a wide spectral region measurement in the 1 000~2 350nm range at 6nm intervals.We investigated the potential of Compovision in the applications to industrial problems such as the evaluation of pharmaceutical tablets and polymers.Our studies have demonstrated that NIR imaging based on Compovision can solve several issues such as long acquisition times and relatively low sensitivity of detection.NIR imaging with Compovision is strongly expected to be applied not only to pharmaceutical tablet monitoring and polymer characterization but also to various applications such as those to food products,biomedical substances and organic and inorganic materials.

Technological,environmental and biological factors:referent variance values for infrared imaging of the bovine

Background： Despite its variety of potential applications, the wide implementation of infrared technology in cattle production faces technical, environmental and biological challenges similar to other indicators of metabolic state. Nine trials, divided into three classes （technological, environmental and biological factors） were conducted to illustrate the influence of these factors on body surface temperature assessed through infrared imaging. Results： Evaluation of technological factors indicated the following： measurements of body temperatures were strongly repeatable when taken within ]0 s; appropriateness of differing infrared camera technologies was influenced by distance to the target; and results were consistent when analysis of thermographs was compared between judges. Evaluation of environmental factors illustrated that wind and debris caused decreases in body surface temperatures without affecting metabolic rate; additionally, body surface temperature increased due to sunlight but returned to baseline values within minutes of shade exposure. Examination/investigation/exploration of animal factors demonstrated that exercise caused an increase in body surface temperature and metabolic rate. Administration of sedative and anti-sedative caused changes on body surface temperature and metabolic rate, and during late pregnancy a foetal thermal imprint was visible through abdominal infrared imaging. Conclusion： The above factors should be considered in order to standardize operational procedures for taking thermographs, thereby optimizing the use of such technology in cattle operations.

Zynq-7000 SoC-based portable uncooled infrared imaging system

A novel portable infrared imaging system based on uncooled focal plane array and programmable system-on-chip（SoC）was proposed.The latest Xilinx Zynq-7000 was used to integrate the main part of the system into a single SoC.Parallel arithmetic units and digital modules were implemented on the programmable logic（PL）of Zynq-7000 to decrease system size and ensure the real-time p nonuniformity correction,while programs running on the processing system（PS）of Zynq-7000 controlled the system work flow and provided human-machine interfaces using open-source software such as Linux and OpenCV.Meanwhile,industry standard advanced extendable interface（AXI）buses were adopted to encapsulating standardized IP cores and build high speed data exchange bridges between units within Zynq-7000.Test results indicate that the image quality and real-time performance of the system can meet application requirements.And it provided a more flexible and extendable solution for evaluating and deploying infrared image enhancement and nonuniformity correction algorithms.

Fano Resonance Enabled Infrared Nano-Imaging of Local Strain in Bilayer Graphene

Detection of local strain at the nanometer scale with high sensitivity remains challenging.Here we report near-field infrared nano-imaging of local strains in bilayer graphene by probing strain-induced shifts of phonon frequency.As a non-polar crystal,intrinsic bilayer graphene possesses little infrared response at its transverse optical phonon frequency.The reported optical detection of local strain is enabled by applying a vertical electrical field that breaks the symmetry of the two graphene layers and introduces finite electrical dipole moment to graphene phonon.The activated phonon further interacts with continuum electronic transitions,and generates a strong Fano resonance.The resulted Fano resonance features a very sharp near-field infrared scattering peak,which leads to an extraordinary sensitivity of-0.002%for the strain detection.Our results demonstrate the first nano-scale near-field Fano resonance,provide a new way to probe local strains with high sensitivity in non-polar crystals,and open exciting possibilities for studying strain-induced rich phenomena.

Near-infrared chemical imaging for quantitative analysis of chlorpheniramine maleate and distribution homogeneity assessment in pharmaceutical formulations

Near infrared chemical imaging(NIR-CI)combines conventional near infrared(NIR)spectros-copy with chemical imaging,thus provides spectral and spatial information simult aneously.It could be utilized to visualize the spatial distribution of the ingredients in a sample.The data acquired using NIR CI instrument are hyperspectral data cube(hypercube)containing thousands of spectra.Chemometric methodologies are necessary to transform spectral information into chemical information.Partial least squares(PLS)method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations.A series of samples which consisted of different CPM concentrations(w/w)were compressed and hypercube data were measured.The spectra extracted from the hypercube were used to establish the PLS model of CPM.The results of the model were R^(2)_(val)0.981,RMSEC 0.384%,RMSECV 0.483%,RMSEP 0.631%,indicating that this model was reliable.

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.

A"donor–acceptor"structured semiconductor polymer for near infrared fluorescence imaging guided photodynamic therapy

Near infrared(NIR)fluorescence imaging guided photodynamic therapy(PDT)is a technique which has been developed in many clinical trials due to its advantage of real-time optical monitoring,specific spatiotemporal selectivity,and minimal invasiveness.For this,photosensitizers with NIR fluorescence emission and high^(1)O_(2)generation quantum yield are highly desirable.Herein,we designed and synthesized a"donor-acceptor"(D-A)structured semiconductor polymer(SP),which was then wrapped with an amphiphilic compound(Pluronic■F127)to prepare water-soluble nanoparticles(F-SP NPs).The obtained F-SP NPs exhibit good water solubility,excellent particle size stability,strong absorbance at deep red region,and strong NIR fluorescent emission characteristics.The maximal mass extinction coe±cient and fluorescence quantum yield of these F-SPs were calculated to be 21.7 L/(g·cm)and 6.5%,respectively.Moreover,the^(1)O_(2)quantum yield of 89%for F-SP NPs has been achieved under 635 nm laser irradiation,which is higher than Methylene Blue,Ce6,and PpIX.The outstanding properties of these F-SP NPs originate from their unique D-A molecular characteristic.This work should help guide the design of novel semiconductor polymer for NIR fluorescent imaging guided PDT applications.

COMP ARISON OF MACROMOLECULAR COMPONENT DISTRIBUTIONS IN OSTEOARTHRITIC AND HEALTHY CARTILAGES BY FOURIER TRANSFORM INFRARED IMAGING

Fourier transform infrared imaging(FTIRI)was used to examine the depth-dependent content variations of macromolcular components,ollagen and protooglycan(PG),in osteoarthritic and healthy cartilages.Dried 6 pmm thick sections of canine knee cartilages were imaged at 6.25 pμrm pixel-size in FTIRI.By analyzing the infrared(IR)images and spectra,the depth dependence of characteristic band(sugar)intensity of PG show obvious difference bet ween the cartilage sections of(OA)and bealth.The result confimns that PG content decreases in the ostcoarthritic cartilage.However,no clear change occurs to collagen,suggesting that the OA influences little on the collagen content at early stage of OA.This observation will be helpful to further understand PG loss associated with pathological conditions in OA,and demonstrates that FIIRI has the po-tential to become an important analytical tool to identify early clinical signs of tissue degna-dation,such as PG loss even collagen disruption.

Comparative study on identi¯cation of healthy and osteoarthritic articular cartilages by fourier transform infrared imaging and chemometrics methods

Two discriminant methods,partial least squares-discriminant analysis(PLS-DA)and Fisher's discriminant analysis(FDA),were combined with Fourier transform infrared imaging(FTIRI)to differentiate healthy and osteoarthritic articular cartilage in a canine model.Osteoarthritic cartilage had been developed for up to two years after the anterior cruciate ligament(ACL)transection in one knee.Cartilage specimens were sectioned into 10μm thickness for FTIRI.A PLS-DA model was developed after spectral pre-processing.All IR spectra extracted from FTIR images were calculated by PLS-DA with the discriminant accuracy of 90%.Prior to FDA,principal component analysis(PCA)was performed to decompose the IR spectral matrix into informative princi pal component matrices.Based on the different discriminant mechanism,the discriminant accuracy(96%)of PCA-FDA with high convenience was higher than that of PLS-DA.No healthy cartilage sample was mis assigned by these two methods.The above mentioned suggested that both integrated technologies of FTIRI-PLS-DA and,especially,FTIRI-PCA-FDA could become a promising tool for the discrimination of healthy and osteoarthritic cartilage specimen as well as the diagnosis of cartilage lesion at microscopic level.The results of the study would be helpful for better understanding the pathology of osteoarthritics.