Image Regions Research Articles

Left atrial strain demonstrates the ability to distinguish patients with pre and post-capillary pulmonary hypertension

Abstract Background The characterization of pulmonary hypertension (PH) patients into pre or postcapillary (pcPH)phenotypes poses a challenge during screening due to the ambiguity surrounding their comorbidities. This distinction is crucial as it influences treatment decisions and prognostication. Purpose Investigating Left Atrial Strain as a Discriminant Marker for Distinguishing Pre and Post-Capillary PH. Methods Out of 164 patients exhibiting both pre- and post-capillary phenotypes, 97 had optimal left atrial 2D strain. Left atrial 2D strain was performed with a GE Vivid9 ultrasound machine equipped with dedicated software, ensuring meticulous attention to image quality and region of interest thickness restricted to a maximum of 6 mm. Subsequently, the peak left atrial strain during reservoir function was quantified as part of the investigation. To determine the optimal cut-off value, ROC curve analysis was employed. Results 60 patients had pcPH while 37 had pre-capillary PH. Mean age was 65±12y. Mean pulmonary artery pressure, wedge pressure, cardiac output and pulmonary vascular resistance averaged 40,9±10,7 mm Hg, 15,7±8,7 mm Hg, 5,9±2,0 l/min and 4,7±3,1 WU, respectively. Left atrial 2D strain ranged from +2,5% to 60%, with a mean of +18,2±11,2%. From ROC cure analysis, patients with LA 2D strain > +19,1% had a precapillary phenotype with a sensitivity and a specifity of 93% and 88% respectively (Fig 1). Left atrial 2D strain demonstrates higher sensitivity compared to various parameters, such as scores from the European Society of Respiratory Disease, left ventricular end-diastolic pressure estimation from the American Society of Echocardiography, HFA-PEFF score, and H2FPEF from the AHA. Conclusion Left atrial 2D strain demonstrates superior sensitivity in distinguishing between patients with pre and post-capillary pulmonary hypertension compared to other assessment methods.FIG 1:Left atrial 2D strainFig 2:SENS of parameters and scores

Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy

Over the past decade, the development of computer-aided detection tools for medical image analysis has seen significant advancements. However, tasks such as the automatic differentiation of tissues or regions in medical images remain challenging. Magnetic resonance imaging (MRI) has proven valuable for early diagnosis, particularly in conditions like prostate cancer, yet it often struggles to produce high-resolution images with clearly defined boundaries. In this article, we propose a novel segmentation approach based on minimum cross-entropy thresholding using the equilibrium optimizer (MCE-EO) to enhance the visual differentiation of tissues in prostate MRI scans. To validate our method, we conducted two experiments. The first evaluated the overall performance of MCE-EO using standard grayscale benchmark images, while the second focused on a set of transaxial-cut prostate MRI scans. MCE-EO’s performance was compared against six stochastic optimization techniques. Statistical analysis of the results demonstrates that MCE-EO offers superior performance for prostate MRI segmentation, providing a more effective tool for distinguishing between various tissue types.

Controllable Anime Image Editing via Probability of Attribute Tags

AbstractEditing anime images via probabilities of attribute tags allows controlling the degree of the manipulation in an intuitive and convenient manner. Existing methods fall short in the progressive modification and preservation of unintended regions in the input image. We propose a controllable anime image editing framework based on adjusting the tag probabilities, in which a probability encoding network (PEN) is developed to encode the probabilities into features that capture continuous characteristic of the probabilities. Thus, the encoded features are able to direct the generative process of a pre‐trained diffusion model and facilitate the linear manipulation. We also introduce a local editing module that automatically identifies the intended regions and constrains the edits to be applied to those regions only, which preserves the others unchanged. Comprehensive comparisons with existing methods indicate the effectiveness of our framework in both one‐shot and linear editing modes. Results in additional applications further demonstrate the generalization ability of our approach.

Seamless and Aligned Texture Optimization for 3D Reconstruction

AbstractRestoring the appearance of the model is a crucial step for achieving realistic 3D reconstruction. High‐fidelity textures can also conceal some geometric defects. Since the estimated camera parameters and reconstructed geometry usually contain errors, subsequent texture mapping often suffers from undesirable visual artifacts such as blurring, ghosting, and visual seams. In particular, significant misalignment between the reconstructed model and the registered images will lead to texturing the mesh with inconsistent image regions. However, eliminating various artifacts to generate high‐quality textures remains a challenge. In this paper, we address this issue by designing a texture optimization method to generate seamless and aligned textures for 3D reconstruction. The main idea is to detect misalignment regions between images and geometry and exclude them from texture mapping. To handle the texture holes caused by these excluded regions, a cross‐patch texture hole‐filling method is proposed, which can also synthesize plausible textures for invisible faces. Moreover, for better stitching of the textures from different views, an improved camera pose optimization is present by introducing color adjustment and boundary point sampling. Experimental results show that the proposed method can eliminate the artifacts caused by inaccurate input data robustly and produce high‐quality texture results compared with state‐of‐the‐art methods.

Enhanced Cross Layer Refinement Network for robust lane detection across diverse lighting and road conditions

With the rapid development of autonomous driving technology, lane detection, a key component of intelligent vehicle systems, is crucial for ensuring road safety and efficient vehicle navigation. In this paper, a new lane detection method is proposed to address the problem of degraded performance of existing lane detection methods when dealing with complex road environments. The proposed method evolves from the original Cross Layer Refinement Network (CLRNet) by incorporating two of our carefully designed core components: the Global Feature Optimizer (GFO) and the Adaptive Lane Geometry Aggregator (ALGA). The GFO is a multi-scale attention mechanism that mimics the human visual focusing ability, effectively filtering out unimportant information and focusing on the image regions most relevant to the task. The ALGA is a shape feature-aware aggregation module that utilizes the shape prior of lanes to enhance the correlation of anchor points in an image, better fusing global and local information. By integrating both components into CLRNet, an enhanced version called Enhanced CLRNet (E-CLRNet) is presented, which exhibits higher performance stability in complex roadway scenarios. Experiments on the CULane dataset reveal that E-CLRNet demonstrates superior performance stability over the original CLRNet in complex scenarios, including curves, shadows, missing lines, and dazzling light conditions. In particular, in the curves, the F1 score of E-CLRNet is improved by almost 3% over the original CLRNet. This study not only improves the accuracy and performance stability of lane detection but also provides a new solution for the application of autonomous driving technology in complex environments, which promotes the development of intelligent vehicle systems.

Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display

Recent advancements in the field of super resolution (SR) have seen the adoption of generative adversarial networks (GANs) for realistic images. In this case, when performing with low-resolution (LR) images, several challenges arise due to the loss of high-frequency details from high-resolution (HR) images, potentially leading to unwanted distortions in the generated SR images. Our paper presents a novel solution by using two-dimensional structure consistency (TSC) for image analysis. The TSC serves as a mask, enabling adaptive analysis based on the unique frequency characteristics of different image regions. Furthermore, a mutual loss mechanism, which dynamically adjusts the training process based on the results filtered by the TSC-based mask, is introduced. Additionally, the TSC loss is proposed to enhance our model capacity to generate precise TSC in high-frequency regions. As a result, our method effectively reduces distortions in high-frequency areas while preserving clarity in regions containing low-frequency components. Our method outperforms other SR techniques, demonstrating superior results in both qualitative and quantitative evaluations. Quantitative measurements, including PSNR, SSIM, and the perceptual metric LPIPS, show comparable PSNR and SSIM values, while the perceptual SR quality is notably improved according to the LPIPS metric.

VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-Modal Information Retrieval

The relations expressed in user queries are vital for cross-modal information retrieval. Relation-focused cross-modal retrieval aims to retrieve information that corresponds to these relations, enabling effective retrieval across different modalities. Pre-trained networks, such as Contrastive Language-Image Pre-training networks, have gained significant attention and acclaim for their exceptional performance in various cross-modal learning tasks. However, the Vision Transformer (ViT) used in these networks is limited in its ability to focus on image region relations. Specifically, ViT is trained to match images with relevant descriptions at the global level, without considering the alignment between image regions and descriptions. This article introduces VITR, a novel network that enhances ViT by extracting and reasoning about image region relations based on a local encoder. VITR is comprised of two key components. Firstly, it extends the capabilities of ViT-based cross-modal networks by enabling them to extract and reason with region relations present in images. Secondly, VITR incorporates a fusion module that combines the reasoned results with global knowledge to predict similarity scores between images and descriptions. The proposed VITR network was evaluated through experiments on the tasks of relation-focused cross-modal information retrieval. The results derived from the analysis of the Flickr30K, MS-COCO, RefCOCOg, and CLEVR datasets demonstrated that the proposed VITR network consistently outperforms state-of-the-art networks in image-to-text and text-to-image retrieval.

Medical Image Fusion for Multiple Diseases Features Enhancement

ABSTRACTThroughout the past 20 years, medical imaging has found extensive application in clinical diagnosis. Doctors may find it difficult to diagnose diseases using only one imaging modality. The main objective of multimodal medical image fusion (MMIF) is to improve both the accuracy and quality of clinical assessments by extracting structural and spectral information from source images. This study proposes a novel MMIF method to assist doctors and postoperations such as image segmentation, classification, and further surgical procedures. Initially, the intensity‐hue‐saturation (IHS) model is utilized to decompose the positron emission tomography (PET)/single photon emission computed tomography (SPECT) image, followed by a hue‐angle mapping method to discriminate high‐ and low‐activity regions in the PET images. Then, a proposed structure feature adjustment (SFA) mechanism is used as a fusion strategy for high‐ and low‐activity regions to obtain structural and anatomical details with minimum color distortion. In the second step, a new multi‐discriminator generative adversarial network (MDcGAN) approach is proposed for obtaining the final fused image. The qualitative and quantitative results demonstrate that the proposed method is superior to existing MMIF methods in preserving the structural, anatomical, and functional details of the PET/SPECT images. Through our assessment, involving visual analysis and subsequent verification using statistical metrics, it becomes evident that color changes contribute substantial visual information to the fusion of PET and MR images. The quantitative outcomes demonstrate that, in the majority of cases, the proposed algorithm consistently outperformed other methods. Yet, in a few instances, it achieved the second‐highest results. The validity of the proposed method was confirmed using diverse modalities, encompassing a total of 1012 image pairs.

Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis.

Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for large-scale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduce SpaTopic, a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue images. This algorithm integrates both cell type and spatial information within a topic modelling framework, originally developed for natural language processing and adapted for computer vision. Spatial information is incorporated into the flexible design of documents, representing densely overlapping regions in images. The model employs an efficient collapsed Gibbs sampling algorithm for both statistical and computational inference. We benchmarked the performance against five state-of-the-art algorithms through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. Our findings demonstrate that SpaTopic consistently identifies biologically and clinically significant spatial "topics" such as tertiary lymphoid structures (TLSs) and tracks dynamic changes in spatial features over disease progression. Its computational efficiency and broad applicability across various molecular imaging platforms will enhance the analysis of large-scale tissue imaging datasets.

Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier

Automatic assessment of brain regions in an MR image has emerged as a pivotal tool in advancing diagnosis and continual monitoring of neurological disorders through different phases of life. Nevertheless, current solutions often exhibit specificity to particular age groups, thereby constraining their utility in observing brain development from infancy to late adulthood. In our research, we introduce a novel approach for segmenting and classifying neonatal brain images. Our methodology capitalizes on minimum spanning tree (MST) segmentation employing the Manhattan distance, complemented by a shrunken centroid classifier empowered by the Brier score. This fusion enhances the accuracy of tissue classification, effectively addressing the complexities inherent in age-specific segmentation. Moreover, we propose a novel threshold estimation method utilizing the Brier score, further refining the classification process. The proposed approach yields a competitive Dice similarity index of 0.88 and a Jaccard index of 0.95. This approach marks a significant step toward neonatal brain tissue segmentation, showcasing the efficacy of our proposed methodology in comparison to the latest cutting-edge methods.

Multi-stage reasoning on introspecting and revising bias for visual question answering

Visual Question Answering (VQA) is a task that involves predicting an answer to a question depending on the content of an image. However, recent VQA methods have relied more on language priors between the question and answer rather than the image content. To address this issue, many debiasing methods have been proposed to reduce language bias in model reasoning. However, the bias can be divided into two categories: good bias and bad bias. Good bias can benefit to the answer prediction, while the bad bias may associate the models with the unrelated information. Therefore, instead of excluding good and bad bias indiscriminately in existing debiasing methods, we proposed a bias discrimination module to distinguish them. Additionally, bad bias may reduce the model’s reliance on image content during answer reasoning and thus attend little on image features updating. To tackle this, we leverage Markov theory to construct a Markov field with image regions and question words as nodes. This helps with feature updating for both image regions and question words, thereby facilitating more accurate and comprehensive reasoning about both the image content and question. To verify the effectiveness of our network, we evaluate our network on VQA v2 and VQA cp v2 datasets and conduct extensive quantity and quality studies to verify the effectiveness of our proposed network. Experimental resu- lts show that our network achieves significant performance against the previous state-of-the-art methods.

MCH-PAN: gastrointestinal polyp detection model integrating multi-scale feature information.

The rise of object detection models has brought new breakthroughs to the development of clinical decision support systems. However, in the field of gastrointestinal polyp detection, there are still challenges such as uncertainty in polyp identification and inadequate coping with polyp scale variations. To address these challenges, this paper proposes a novel gastrointestinal polyp object detection model. The model can automatically identify polyp regions in gastrointestinal images and accurately label them. In terms of design, the model integrates multi-channel information to enhance the ability and robustness of channel feature expression, thus better coping with the complexity of polyp structures. At the same time, a hierarchical structure is constructed in the model to enhance the model's adaptability to multi-scale targets, effectively addressing the problem of large-scale variations in polyps. Furthermore, a channel attention mechanism is designed in the model to improve the accuracy of target positioning and reduce uncertainty in diagnosis. By integrating these strategies, the proposed gastrointestinal polyp object detection model can achieve accurate polyp detection, providing clinicians with reliable and valuable references. Experimental results show that the model exhibits superior performance in gastrointestinal polyp detection, which helps improve the diagnostic level of digestive system diseases and provides useful references for related research fields.

SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network

PurposeThe problem of image denoising in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a fundamental challenge. Although various image processing techniques have been presented, they may degrade the contrast of denoised images. The proposed idea in this study is to use a deep neural network as the denoising procedure during the iterative reconstruction process rather than the post-reconstruction phase. This method could decrease the background coefficient of variation (COV_bkg) of the final reconstructed image, which represents the amount of random noise, while improving the contrast-to-noise ratio (CNR).MethodsIn this study, a generative adversarial network is used, where its generator is trained by a two-phase approach. In the first phase, the network is trained by a confined image region around the heart in transverse view. The second phase improves the network’s generalization by tuning the network weights with the full image size as the input. The network was trained and tested by a dataset of 247 patients who underwent two immediate serially high- and low-noise SPECT-MPI.ResultsQuantitative results show that compared to post-reconstruction low pass filtering and post-reconstruction deep denoising methods, our proposed method can decline the COV_bkg of the images by up to 10.28% and 12.52% and enhance the CNR by up to 54.54% and 45.82%, respectively.ConclusionThe iterative deep denoising method outperforms 2D low-pass Gaussian filtering with an 8.4-mm FWHM and post-reconstruction deep denoising approaches.

Specular highlight removal by federated generative adversarial network with attention mechanism

Specular highlight removal ensures the acquisition of high-quality images, which finds its important applications in stereo matching, text recognition and image segmentation. In order to prevent the leakage of images containing personal information, such as identification card (ID) photos, clients often train specular highlight removal models using local data resulting in a lack of precision and generalization of the trained model. To address this challenge, this paper introduces a new method to remove highlight in images using federated learning (FL) and attention generative adversarial network (AttGAN). Specifically, the former builds a global model in the central server and updates the global model by aggregating model parameters of clients. This process does not involve the transmission of image data, which enhances the privacy of clients; the later combining attention mechanisms and generative adversarial network aims to improve the quality of highlight removal by focusing on key image regions, resulting in more realistic and visually pleasing results. The proposed FL-AttGAN method is numerically evaluated, using SD1, SD2 amd RD datasets. The results show that the proposed FL-AttGAN outperforms existent methods.

Estimation of local variation in Young's modulus over a gold nanocontact using microscopic nanomechanical measurement method.

Nanoscale materials tend to have a single crystal domain, leading to not only size dependence but also orientation dependence of their mechanical properties. Recently, we developed a microscopic nanomechanical measurement method (MNMM), which enabled us to obtain equivalent spring constants (force gradients) of nanocontacts while observing their atomic structures by transmission electron microscopy (TEM). Therein, we evaluated Young's modulus based on a model that a newly introduced layer at the thinnest section of a nanocontact determined the change in the measured equivalent spring constant, and discussed their size dependence. However, this model is not general for other nanomaterials that do not exhibit the introduction of a new atomic layer while stretching. In this study, using MNMM, we propose a new analytical method to directly retrieve the local Young's modulus of nanomaterials by measuring initial lattice spacing and its displacement of a local region in the TEM image during the stretching of the nanocontact. This reveals the size dependence of local Young's modulus at various positions of the nanocontact at once. As a result, our estimated Young's modulus for a gold [111] nanocontact showed a size dependence similar to the one previously reported. This indicates that this analytical method benefits in revealing the mechanical properties of not only nanomaterials but also structurally heterogeneous materials such as high-entropy alloys.&#xD.

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

Foreground-background separation and deblurring super-resolution method

The limited depth of field (DOF) inherent in cameras often results in defocused and blurry backgrounds when capturing images in large aperture mode. This not only leads to the loss of crucial background information but also impedes the efficient reconstruction of the background regions. Usually, super-resolution (SR) techniques struggle to produce high-quality results for images captured with large apertures. To enhance the reconstruction quality of defocused regions in large aperture images, a foreground-background separation and deblurring super-resolution (FBSDSR) method was proposed in this paper. Based on the idea of foreground-background separation processing, we first divide the large aperture image into a sharp foreground region (If) and a blurry background region (Ib) based on depth information. The background region (Ib) is then deblurred using an end-to-end iterative filter adaptive network (IFAN). This deblurring process refocuses the background, ultimately restoring an image with sharp details throughout. Finally, the enhanced super-resolution generative adversarial networks (Real-ESRGAN) which specializes in images SR of realistic scenes was used to process the sharp all-in-focus image. This method results in high-quality reconstructions of both the foreground and background of large aperture images. The experimental results demonstrated that the proposed method achieved effective reconstruction of the entire large aperture images clearly, overcoming the limitations of existing methods that struggle to reconstruct defocused regions. This significantly enhances the quality and resolution of large aperture images. Specifically, when FBSDSR is integrated with Real-ESRGAN, the PSNR, LPIPS, NIQE, and hyperIQA metrics were improved by approximately 2.2 %, 45.1 %, 34.7 %, and 10.9 % respectively.

CT-based Intra-thrombus and Peri-thrombus Radiomics for Prediction of Prognosis After Endovascular Thrombectomy: A Retrospective Study Across Two Centers.

Complications from endovascular thrombectomy (EVT) can negatively affect clinical outcomes, making the development of a more precise and objective prediction model essential. This research aimed to assess the effectiveness of radiomic features derived from pre-surgical CT scans in predicting the prognosis post- EVT in acute ischemic stroke patients. This investigation included 336 acute ischemic stroke patients from two medical centers, spanning from March 2018 to March 2024. The participants were split into a training cohort of 161 patients and a validation cohort of 175 patients. Patient outcomes were rated with the mRS: 0-2 for good, 3-6 for poor. A total of 428 radiomic features were derived from intra-thrombus and peri-thrombus regions in non-contrast CT and CT angiography images. Feature selection was conducted using a least absolute shrinkage and selection operator regression model. The efficacy of eight different supervised learning models was assessed using the area under the curve (AUC) of the receiver operating characteristic curve. Among all models tested in the validation cohort, the logistic regression algorithm for combined model achieved the highest AUC (0.87, with a 95% confidence interval of 0.81 to 0.92), outperforming other algorithms. The combined use of radiomic features from both the intra-thrombus and peri-thrombus regions significantly enhanced diagnostic accuracy over models using features from a single region (0.81 vs 0.70, 0.77), highlighting the benefit of integrating data from both regions for improved prediction. The findings suggest that a combined radiomics model based on CT imaging serves as a potent approach to assessing the prognosis following EVT. The logistic regression model, in particular, proved to be both effective and stable, offering critical insights for the management of stroke. AUC=area under the curve; EVT=endovascular thrombectomy; KNN=k-nearest neighbors; LASSO=least absolute shrinkage and selection operator; LightGBM=Light Gradient Boosting Machine; LR=logistic regression; MLP=multi-layer perceptron; RF=random forest; SVM=support vector machine; XGBoost=extreme gradient boosting.

A high-quality visual image encryption algorithm utilizing the conservative chaotic system and adaptive embedding

Regarding the issue of encrypted images in the channel attracting the attention of attackers and making them vulnerable to attacks. This paper proposes a high-quality visual image encryption algorithm utilizing the conservative chaotic system, two-dimensional compressive sensing, optimization local of binary patterns, and adaptive embedding method. Firstly, a conservative chaotic system with excellent performance and resistance to reconstruction attacks is proposed. Secondly, the pseudo-random sequences generated by the chaotic system dynamically generate measurement matrices, which are optimized before compressing the image. Then, during the encryption process, by utilizing the newly proposed composite DNA computing rules, chaotic sequences dynamically encode and compute image information, which enriches the coding criteria and improves the security of encryption algorithms. Finally, in the information embedding stage, the newly proposed OLBP algorithm can identify important textured and non-textured regions of the host image, prioritize embedding non-textured regions, and then embed textured regions, which can improve the amount of information embedding and reduce damage to the host image. Experimental simulation and analysis exhibit that the encryption algorithm has high security, strong robustness and high efficiency. Meanwhile the imperceptible analysis of the steganographic images is exceed 52 dB, so the algorithm presents a high-quality visual effect of imperceptibility.

Reduction of speckle effect using galvanometer scanners in a laser-based uniform source for radiance responsivity measurement

Narrow linewidth lasers play an important role in high-spectral and high-precision radiometric calibration. In this paper, a standard uniform light source for radiometric calibration of imaging detectors and devices is designed and realized based on narrow linewidth laser and intergrating sphere. Because the speckle effect caused by laser has a great influence on the radiance transfer, galvanometer scanners were employed to reduce the speckle effect. CCD method was used to measure the nonuniformity of the middle row/column of the integrating sphere exit, and nonuniformity reduced from 2.6 % to 0.72 % when the galvanometer scanners were turned on. By superimposing 9 different regions of static speckle image, the non-uniformity is greatly reduced, which is consistent with the experimental results. The output radiance stability of the source is measured with an improvement from 8.4 × 10-5 to 2.7 × 10-5. Finally, the nonuniformity of the source is measured to be 0.15 % by a spot luminance meter.