Hybrid enhancement algorithm for nailfold images with large fields of view.

Collecting and analyzing human nailfold images is an important component of studying human microcirculation. However, the large-field-of-view and high-resolution nailfold images captured by research microscopes introduce issues such as uneven brightness, low imaging contrast, and unclear vascular contours. To overcome these issues, this paper proposes a hybrid enhancement algorithm for nailfold images with large fields of view. First, adaptive histogram equalization with limited contrast (Clahe) is used to redistribute gray levels to enhance the brightness and contrast of images. Next, nonlocal means denoising (NL-means) is used to remove the noise amplified by Clahe algorithm. Finally, unsharp masking (Usm) is used to enhance the edge contour information of nailfold blood vessels. Comparing the enhanced images reveals that the hybrid enhancement algorithm improves the brightness and contrast of the nailfold image, makes the nailfold vessel contour more obvious, and the image noise continues to remain small, and it obtains the best visual effect. It is superior to other algorithms in terms of objective indicators and subjective evaluation.

Heart rate estimation from color video sequences with high SNR.

We propose an absorption intensity heartbeat modulation-averaged shifted histogram (AIHM-ASH) method for estimating human heart rate (HR) using color videos of lip image sequences. When heartbeat occurs, AIHM is generated. Based on the AIHM, HR signals can be demodulated by computing the instantaneous HR modulation depth that presents the relative red blood cell (RBC) concentration from the green channel image of the RGB color video. In addition, the ASH algorithm further suppresses the background tissue and vein signals, and increases the signal-to-noise ratio (SNR). The experimental results for flow phantoms, chicken embryos, and human lips validated the proposed method's optimal estimation conditions and effectiveness, where the accuracy and root mean square error (RMSE) were 99.23% and 0.8 bpm, respectively. The proposed HR estimation method has significant potential to advance health monitoring and disease prevention via conventional color video cameras installed in public places.

Multi-Focus Image Fusion for Full-Field Optical Angiography.

Full-field optical angiography (FFOA) has considerable potential for clinical applications in the prevention and diagnosis of various diseases. However, owing to the limited depth of focus attainable using optical lenses, only information about blood flow in the plane within the depth of field can be acquired using existing FFOA imaging techniques, resulting in partially unclear images. To produce fully focused FFOA images, an FFOA image fusion method based on the nonsubsampled contourlet transform and contrast spatial frequency is proposed. Firstly, an imaging system is constructed, and the FFOA images are acquired by intensity-fluctuation modulation effect. Secondly, we decompose the source images into low-pass and bandpass images by performing nonsubsampled contourlet transform. A sparse representation-based rule is introduced to fuse the lowpass images to effectively retain the useful energy information. Meanwhile, a contrast spatial frequency rule is proposed to fuse bandpass images, which considers the neighborhood correlation and gradient relationships of pixels. Finally, the fully focused image is produced by reconstruction. The proposed method significantly expands the range of focus of optical angiography and can be effectively extended to public multi-focused datasets. Experimental results confirm that the proposed method outperformed some state-of-the-art methods in both qualitative and quantitative evaluations.

Fast and stable Fourier ptychographic microscopy based on improved phase recovery strategy.

Fourier ptychographic microscopy (FPM) imaging is a computational imaging technology that can reconstruct wide-field high-resolution (HR) images. It uses a series of low-resolution images captured by a camera under different illumination angles. The images are stitched in the Fourier domain to expand their spectral range. Under high-angle illumination, a dark-field image is noisy with a low signal-to-noise ratio, which significantly reduces the reconstruction quality of FPM. Conventional reconstruction algorithms often have low FPM imaging performance and efficiency due to optimization strategies. In response to these problems, this paper proposes an FPM imaging method based on an improved phase recovery strategy to optimize the alternating iterative algorithm. The technique uses an improved threshold method to reduce noise in the image preprocessing stage to maximize the retention of high-frequency sample information. Moreover, an adaptive control factor is added in the subsequent iterative update process to balance the sample spectrum function. This study verifies the effectiveness of the proposed method on both simulation and experimental images. The results show that the proposed method can effectively suppress image background noise and has a faster convergence speed and higher robustness. In addition, it can be used to reconstruct HR complex amplitude images of objects under wide field-of-view conditions.

Fano effect induced giant and robust enhancement of photon correlations in cavity QED systems.

The Fano effect arising from the interference between two dissipation channels of the radiation continuum enables tuning of the photon statistics. Understanding the role of the Fano effect and exploiting it to achieve strong photon correlations are of both fundamental and applied significance. We present an analytical description of Fano-enhanced photon correlations based on cavity quantum electrodynamics to show that the Fano effect in atom-cavity systems can improve the degree of antibunching by over four orders of magnitude. The enhancement factors and the optimal conditions are explicitly given, and found to relate to the Fano parameter q. Remarkably, the Fano enhancement manifests robustness against the decoherence processes and can survive in the weak coupling regime. We expect our work to provide insight to tuning the photon statistics through the Fano effect, which offers a new, to the best of our knowledge, route to enhance the photon correlations, as well as the possibility of generating nonclassical light in a wider diversity of systems without the need of a strong light-matter interaction.

Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning.

Automatic segmentation and measurement of the choroid layer is useful in studying of related fundus diseases, such as diabetic retinopathy and high myopia. However, most algorithms are not helpful for choroid layer segmentation due to its blurred boundaries and complex gradients. Therefore, this paper aimed to propose a novel choroid segmentation method that combines image enhancement and attention-based dense (AD) U-Net network. The choroidal images obtained from optical coherence tomography (OCT) are pre-enhanced by algorithms that include flattening, filtering, and exponential and linear enhancement to reduce choroid-independent information. Experimental results obtained from 800 OCT B-scans of the choroid layers from both normal eyes and high myopia showed that image enhancement significantly increased the performance of ADU-Net, with an AUC of 99.51% and a DSC of 97.91%. The accuracy of segmentation using the ADU-Net method with image enhancement is superior to that of the existing networks. In addition, we describe some algorithms that can measure automatically choroidal foveal thickness and the volume of adjacent areas. Statistical analyses of the choroidal parameters variation indicated that compared with normal eyes, high myopia has a reduction of 86.3% of the choroidal foveal thickness and 90% of the adjacent volume. It proved that high myopia is likely to cause choroid layer attenuation. These algorithms would have wide application in the diagnosis and precaution of related fundus lesions caused by choroid thinning from high myopia in future studies.

Multi-Focus Image Fusion Based on Hessian Matrix Decomposition and Salient Difference Focus Detection.

Multi-focus image fusion integrates images from multiple focus regions of the same scene in focus to produce a fully focused image. However, the accurate retention of the focused pixels to the fusion result remains a major challenge. This study proposes a multi-focus image fusion algorithm based on Hessian matrix decomposition and salient difference focus detection, which can effectively retain the sharp pixels in the focus region of a source image. First, the source image was decomposed using a Hessian matrix to obtain the feature map containing the structural information. A focus difference analysis scheme based on the improved sum of a modified Laplacian was designed to effectively determine the focusing information at the corresponding positions of the structural feature map and source image. In the process of the decision-map optimization, considering the variability of image size, an adaptive multiscale consistency verification algorithm was designed, which helped the final fused image to effectively retain the focusing information of the source image. Experimental results showed that our method performed better than some state-of-the-art methods in both subjective and quantitative evaluation.

A New Dictionary Construction Based Multimodal Medical Image Fusion Framework.

Training a good dictionary is the key to a successful image fusion method of sparse representation based models. In this paper, we propose a novel dictionary learning scheme for medical image fusion. First, we reinforce the weak information of images by extracting and adding their multi-layer details to generate the informative patches. Meanwhile, we introduce a simple and effective multi-scale sampling to implement a multi-scale representation of patches while reducing the computational cost. Second, we design a neighborhood energy metric and a multi-scale spatial frequency metric for clustering the image patches with a similar brightness and detail information into each respective patch group. Then, we train the energy sub-dictionary and detail sub-dictionary, respectively by K-SVD. Finally, we combine the sub-dictionaries to construct a final, complete, compact and informative dictionary. As a main contribution, the proposed online dictionary learning can not only obtain an informative as well as compact dictionary, but can also address the defects, such as superfluous patch issues and low computation efficiency, in traditional dictionary learning algorithms. The experimental results show that our algorithm is superior to some state-of-the-art dictionary learning based techniques in both subjective visual effects and objective evaluation criteria.

Real-time full-field optical angiography utilizing principal component analysis.

We develop a real-time full-field optical angiography method using principal component analysis (PCA). In our approach, an undersampled laser Doppler method is used to record the raw images. Considering the difference in the signal component contributions, PCA is used to separate the dynamic blood flow and static background signals. The principal advantage of the PCA method is that the choice of a high pixel number can aid in efficiently extracting the blood flow signal with finite frame raw images, which can greatly improve the temporal resolution. Our phantom experimental results validate our choice of the optimal frame number for reconstructing an angiographic image. A vascular occlusion test on a rabbit ear demonstrates that global and simultaneous hemodynamic processes of vessels can be monitored.

Full-field functional optical angiography.

We propose full-field functional optical angiography for a live biological specimen based on the absorption intensity fluctuation modulation (AIFM) effect. Because of the difference in absorption between red blood cells (RBCs) and the background tissue under low-coherence light illumination, the moving RBCs, which discontinuously pass though the capillary vessels, generate an AIFM effect. This effect offers high contrast of absorption imaging and sensitivity of low-coherence interference between RBCs and the background tissue. It is used to distinguish the signal of RBCs from that of the background tissue. The averaged and real-time modulation depths are computed to obtain full-field label-free optical angiography and measure blood flow velocity simultaneously. The AIFM method could potentially be applied to study the physiological mechanisms of blood circulation systems of near-transparent live biologic samples.

Double distortion correction method in a catadioptric vision system with a conic mirror.

Because of the low calibration precision caused by reflector distortion in catadioptric vision systems with conic mirrors, we proposed a double distortion correction model based on the panoramic image rectification. The lens distortion and the reflector distortion are analyzed independently in the mathematic model of the double distortion correction. Moreover, the modified model in this paper added the trigonometric functions into the polynomial model to solve the combined distortion. Additionally, extensive experiments, both simulative and real data, have been carried out and show that the proposed model achieves comparable measurement accuracy in contrast with the traditional distortion method.

Generative Adversarial Network for Trimodal Medical Image Fusion Using Primitive Relationship Reasoning.

Medical image fusion has become a hot biomedical image processing technology in recent years. The technology coalesces useful information from different modal medical images onto an informative single fused image to provide reasonable and effective medical assistance. Currently, research has mainly focused on dual-modal medical image fusion, and little attention has been paid on trimodal medical image fusion, which has greater application requirements and clinical significance. For this, the study proposes an end-to-end generative adversarial network for trimodal medical image fusion. Utilizing a multi-scale squeeze and excitation reasoning attention network, the proposed method generates an energy map for each source image, facilitating efficient trimodal medical image fusion under the guidance of an energy ratio fusion strategy. To obtain the global semantic information, we introduced squeeze and excitation reasoning attention blocks and enhanced the global feature by primitive relationship reasoning. Through extensive fusion experiments, we demonstrate that our method yields superior visual results and objective evaluation metric scores compared to state-of-the-art fusion methods. Furthermore, the proposed method also obtained the best accuracy in the glioma segmentation experiment.

Low-consumption stepper motor controller with real-time target position change responsiveness based on field programmable gate array.

In response to the demand for low resource consumption, parallel control, and real-time response to target position changes in precision measurement and manufacturing of multi-axis stepper motor controllers, this paper proposes a field programmable gate array-based method for generating trapezoidal velocity profiles and pulse generation, which can easily keep parallelism and independence during multi-axis control. By avoiding using multiplication and division, this controller not only reduces resource consumption but also enhances the pulse output frequency. To address the real-time responsiveness of the velocity profile generation algorithm to changes in the target position during the control process, the algorithm introduces a novel real-time comparative state transition logic for speed control, which makes it capable of adjusting the acceleration within a single clock cycle, enabling its application in scenarios that require higher levels of real-time performance. Finally, the designed controller is applied to a four-axis positioning system for performance validation.

Multicomponent Adversarial Domain Adaptation: A General Framework.

Domain adaptation (DA) aims to transfer knowledge from one source domain to another different but related target domain. The mainstream approach embeds adversarial learning into deep neural networks (DNNs) to either learn domain-invariant features to reduce the domain discrepancy or generate data to fill in the domain gap. However, these adversarial DA (ADA) approaches mainly consider the domain-level data distributions, while ignoring the differences among components contained in different domains. Therefore, components that are not related to the target domain are not filtered out. This can cause a negative transfer. In addition, it is difficult to make full use of the relevant components between the source and target domains to enhance DA. To address these limitations, we propose a general two-stage framework, named multicomponent ADA (MCADA). This framework trains the target model by first learning a domain-level model and then fine-tuning that model at the component-level. In particular, MCADA constructs a bipartite graph to find the most relevant component in the source domain for each component in the target domain. Since the nonrelevant components are filtered out for each target component, fine-tuning the domain-level model can enhance positive transfer. Extensive experiments on several real-world datasets demonstrate that MCADA has significant advantages over state-of-the-art methods.

Reduction in required volume of imaging data and image reconstruction time for adaptive-illumination Fourier ptychographic microscopy.

Fourier ptychographic microscopy (FPM) is a promising super-resolution computational imaging technology. It stitches a series of low-resolution (LR) images in the Fourier domain by an iterative method. Thus, it obtains a large field of view and high-resolution quantitative phase images. Owing to its capability to perform high-spatial bandwidth product imaging, FPM is widely used in the reconstruction of conventional static samples. However, the influence of the FPM imaging mechanism limits its application in high-speed dynamic imaging. To solve this problem, an adaptive-illumination FPM scheme using regional energy estimation is proposed. Starting with several captured real LR images, the energy distribution of all LR images is estimated, and select the measurement images with large information to perform FPM reconstruction. Simulation and experimental results show that the method produces efficient imaging performance and reduces the required volume of data to more than 65% while ensuring the quality of FPM reconstruction.

Multi-level optical angiography for photodynamic therapy.

Blood flow imaging is widely applied in photodynamic therapy (PDT) to provide vascular morphological and statistical parameters. This approach relies on the intensity of time-domain signal differences between blood vessels and background tissues; therefore, it often ignores differences within the vasculature and cannot accommodate abundant structural information. This study proposes a multi-level optical angiography (MOA) method for PDT. It can enhance capillaries and image vessels at different levels by measuring the signal frequency shift associated with red blood cell motion. The experimental results regarding the PDT-induced chorioallantoic membrane model showed that the proposed method could not only perform multi-level angiography but also provide more accurate quantitative information regarding various vascular parameters. This MOA method has potential applications in PDT studies.

Deep-learning visualization enhancement method for optical coherence tomography angiography in dermatology.

Optical coherence tomography angiography (OCTA) in dermatology usually suffers from low image quality due to the highly scattering property of the skin, the complexity of cutaneous vasculature, and limited acquisition time. Deep-learning methods have achieved great success in many applications. However, the deep learning approach to improve dermatological OCTA images has not been investigated due to the requirement of high-performance OCTA systems and difficulty of obtaining high-quality images as ground truth. This study aims to generate proper datasets and develop a robust deep learning method to enhance the skin OCTA images. A swept-source skin OCTA system was employed to create low-quality and high-quality OCTA images with different scanning protocols. We propose a model named vascular visualization enhancement generative adversarial network and adopt an optimized data augmentation strategy and perceptual content loss function to achieve better image enhancement effect with small amount of training data. We demonstrate the superiority of the proposed method in skin OCTA image enhancement by quantitative and qualitative comparisons.

High resolution imaging and quantification of the nailfold microvasculature using optical coherence tomography angiography (OCTA) and capillaroscopy: a preliminary study in healthy subjects.

Background: A wide range of diseases, such as systemic sclerosis, can be diagnosed by imaging the nailfold microcirculation, which is conventionally performed using capillaroscopy. This study applied optical coherence tomography angiography (OCTA) as a novel high resolution imaging method for the qualitative and quantitative assessment of the nailfold microvasculature, and compared OCTA imaging with capillaroscopy. Methods: For qualitative assessment, high resolution OCTA imaging was used to achieve images that contained a wide field of view of the nailfold microvasculature through mosaic scanning. OCTA imaging was also used to observe the characteristic changes in the microvasculature under external compression of the upper arm. For quantitative evaluation, the capillary density and the capillary diameter of the nailfold microvasculature were assessed with both OCTA and capillaroscopy by repeated measurements over 2 days in 13 normal subjects. The results were analyzed using the intraclass correlation coefficient (ICC). Results: OCTA imaging showed the typical nailfold microvasculature pattern, part of which was not directly seen with the capillaroscopy. OCTA imaging revealed significant changes in the nailfold microvasculature when a large external pressure was applied via arm compression, but no significant changes were observed using capillaroscopy. The capillary density measured by OCTA and capillaroscopy was 6.8±1.5 and 7.0±1.2 loops/mm, respectively, which was not significantly different (P=0.51). However, the capillary diameter measured by OCTA was significantly larger than that measured using capillaroscopy (19.1±2.5 vs. 13.3±2.3 µm, P<0.001). The capillary diameter measurements using OCTA and capillaroscopy were highly reproducible (ICC =0.926 and 0.973, respectively). While the capillary diameter measured with OCTA was significantly larger, it was rather consistent with the diameter measured using capillaroscopy (ICC =0.705). Conclusions: This study demonstrated that OCTA is a potentially viable and reproducible tool for the imaging and quantification of the capillaries in the nailfold microvasculature. The results of this study provide a solid basis for future applications of OCTA in qualitative and quantitative assessment of nailfold microcirculation in vivo.

Spatiotemporal absorption fluctuation imaging based on U-Net.

SIGNIFICANCE: Full-field optical angiography is critical for vascular disease research and clinical diagnosis. Existing methods struggle to improve the temporal and spatial resolutions simultaneously. AIM: Spatiotemporal absorption fluctuation imaging (ST-AFI) is proposed to achieve dynamic blood flow imaging with high spatial and temporal resolutions. APPROACH: ST-AFI is a dynamic optical angiography based on a low-coherence imaging system and U-Net. The system was used to acquire a series of dynamic red blood cell (RBC) signals and static background tissue signals, and U-Net is used to predict optical absorption properties and spatiotemporal fluctuation information. U-Net was generally used in two-dimensional blood flow segmentation as an image processing algorithm for biomedical imaging. In the proposed approach, the network simultaneously analyzes the spatial absorption coefficient differences and the temporal dynamic absorption fluctuation. RESULTS: The spatial resolution of ST-AFI is up to 4.33 µm, and the temporal resolution is up to 0.032 s. In vivo experiments on 2.5-day-old chicken embryos were conducted. The results demonstrate that intermittent RBCs flow in capillaries can be resolved, and the blood vessels without blood flow can be suppressed. CONCLUSIONS: Using ST-AFI to achieve convolutional neural network (CNN)-based dynamic angiography is a novel approach that may be useful for several clinical applications. Owing to their strong feature extraction ability, CNNs exhibit the potential to be expanded to other blood flow imaging methods for the prediction of the spatiotemporal optical properties with improved temporal and spatial resolutions.

Full-field optical multi-functional angiography based on endogenous hemodynamic characteristics.

Blood flow functional imaging is widely applied in biological research to provide vascular morphological and statistical parameters. It relies on the absorption difference and is, therefore, easily affected by complex biological structures, and it cannot accommodate abundant functional information. We propose a full-field multi-functional angiography method to classify arteriovenous vessels and to display flow velocity and vascular diameter distribution simultaneously. Unlike previous methods, an under-sampled laser Doppler acquisition mode is used to record the low-coherence speckle, and multi-functional angiography is achieved by modulating the endogenous hemodynamic characteristics from low-coherence speckle. To demonstrate the combination of classified angiography, blood flow velocity measurement, and vascular diameter measurement realized using our method, we performed experiments on the flow phantom and living chicken embryos and generated multi-functional angiograms. The proposed method can be used as a label-free multi-functional angiography technique in which red blood cells provide a strong endogenous source of naturally hemodynamic characteristics.