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Accelerating multi-emitter localization in super-resolution localization microscopy with FPGA-GPU cooperative computation

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OPTICS EXPRESS 2021年第22期29卷 35247-35260页

作者： Gui, Dan Chen, Yunjiu Kuang, Weibing Shang, Mingtao Wang, Zhengxia Huang, Zhen-Li Huazhong Univ Sci & Technol Britton Chance Ctr Sch Engn Sci Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Engn Sci MoE Key Lab Biomed Photon Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Wuhan Vocat Coll Software & Engn Sch Elect Engn Wuhan 430205 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Hainan Peoples R China Hainan Univ Sch Biomed Engn Key Lab Biomed Engn Hainan Prov Haikou 570228 Hainan Peoples R China

The real-time multi-emitter localization method is essential for advancing high-throughput super-resolution localization microscopy (HT-SRLM). In the past decade, the graphics processing unit (GPU) computation has been dominantly used to accelerate the execution speed of the multi-emitter localization method. However, if HT-SRLM is combined with a scientific complementary metal-oxide-semiconductor (sCMOS) camera working at full frame rate, real-time image processing is still difficult to achieve using this acceleration approach, thus resulting in a massive data storage challenge and even system crash. Here we take advantage of the cooperative acceleration power of field programming gate array (FPGA) computation and GPU computation, and propose a method called HCP-STORM to enable real-time multi-emitter localization. Using simulated images, we verified that HCP-STORM is capable of providing real-time image processing for raw images from a representative Hamamatsu Flash 4 V3 sCMOS camera working at full frame rate (that is, 2048x2048 pixels @ 10 ms exposure time). Using experimental images, we prove that HCP-STORM is 25 times faster than QC-STORM and 295 times faster than ThunderSTORM, with a small but acceptable degradation in image quality. This study shows the potential of FPGA-GPU cooperative computation in accelerating multi-emitter localization, and pushes a significant step toward the maturity of HT-SRLM technology. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： High throughput optics image processing image processing algorithms image quality Neural networks Spatial resolution

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Analysis and improvement of non-contact SpO2 extraction using an RGB webcam

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BIOMEDICAL OPTICS EXPRESS 2021年第8期12卷 5227-5245页

作者： Wei, Bing Wu, Xiaopei Zhang, Chao Lv, Zhao Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodel Cognit Computat Hefei 230601 Peoples R China Hefei Normal Coll Dept Comp Sci & Technol Hefei 230601 Peoples R China

Peripheral oxygen saturation (SpO2), a vital physiological sign employed in clinical care, is commonly obtained by using a contact pulse oximeter. With the rapid popularization of ordinary red-green-blue (RGB) webcams embedded in devices such as smartphones or laptops, there are broad application prospects for exploring techniques for non-contact SpO2 extraction using RGB webcams. However, many issues remain to be solved in the traditional webcam-based SpO2 extraction methods, such as the inherent low signal-to-noise ratio (SNR) of alternating current (AC) components of RGB signals and the potential defects in using RGB signals combination for SpO2 extraction. In this study, we conducted an in-depth examination of the existing research on webcam-based SpO2 extraction techniques, analyzed the practical problems in using them, and explored new ideas to solve the problems. Rather than roughly using the standard deviations (SD) of AC components for calculations, we performed blind source separation for AC components, and then used the energy coefficients retained in the mixed matrix to replace the variables required in the algorithm. Moreover, steady data was selected to compensate for the potential defects in using RGB signals combination. Through these efforts, the anti-noise capability of the algorithm was significantly enhanced, and the related defects were compensated for. The experimental results indicated that the proposed method produced reliable SpO2 estimation that could potentially-with further research-be used in real applications. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Absorption coefficient CCD cameras image analysis image processing algorithms Infrared radiation Light intensity

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Ultrathin arrayed camera for high-contrast near-infrared imaging

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OPTICS EXPRESS 2021年第2期29卷 1333-1339页

作者： Kim, Kisoo Jang, Kyung-Won Bae, Sang-In Kim, Hyun-Kyung Cha, Younggil Ryu, Jae-Kwan Jo, Yong-Jin Jeong, Ki-Hun Dept Bio & Brain Engn 291 Daehak Ro Daejeon 34141 South Korea KAIST Inst Hlth Sci & Technol 291 Daehak Ro Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KAIST 291 Daehak Ro Daejeon 34141 South Korea LIG Nexl Co Ltd Unmanned Robot Syst Lab Seongnam 13488 South Korea

We report an ultrathin arrayed camera (UAC) for high-contrast near infrared (NIR) imaging by using microlens arrays with a multilayered light absorber. The UAC consists of a multilayered composite light absorber, inverted microlenses, gap-alumina spacers and a planar CMOS image sensor. The multilayered light absorber was fabricated through lift-off and repeated photolithography processes. The experimental results demonstrate that the image contrast is increased by 4.48 times and the MTF 50 is increased by 2.03 times by eliminating optical noise between microlenses through the light absorber. The NIR imaging of UAC successfully allows distinguishing the security strip of authentic bill and the blood vessel of finger. The ultrathin camera offers a new route for diverse applications in biometric, surveillance, and biomedical imaging. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： image processing algorithms image quality Imaging systems Medical imaging Microlens arrays Scanning electron microscopy

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Deep learning provides high accuracy in automated chondrocyte viability assessment in articular cartilage using nonlinear optical microscopy

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BIOMEDICAL OPTICS EXPRESS 2021年第5期12卷 2759-2772页

作者： Chen, Xun Li, Yang Wyman, Nicole Zhang, Zheng Fan, Hongming Le, Michael Gannon, Steven Rose, Chelsea Zhang, Zhao Mercuri, Jeremy Yao, Hai Gao, Bruce Woolf, Shane Pecot, Thierry Ye, Tong Clemson Univ Dept Bioengn Clemson SC 29634 USA Beihang Univ Inst Med Photon Beijing Adv Innovat Ctr Biomed Engn Sch Biol Sci & Med Engn Beijing 100083 Peoples R China Med Univ South Carolina Dept Orthoped Charleston SC 29425 USA Med Univ South Carolina Hollings Canc Ctr Charleston SC 29425 USA Med Univ South Carolina Dept Regenerat Med & Cell Biol Charleston SC 29425 USA

Chondrocyte viability is a crucial factor in evaluating cartilage health. Most cell viability assays rely on dyes and are not applicable for in vivo or longitudinal studies. We previously demonstrated that two-photon excited autofluorescence and second harmonic generation microscopy provided high-resolution images of cells and collagen structure;those images allowed us to distinguish live from dead chondrocytes by visual assessment or by the normalized autofluorescence ratio. However, both methods require human involvement and have low throughputs. Methods for automated cell-based image processing can improve throughput. Conventional image processing algorithms do not perform well on autofluorescence images acquired by nonlinear microscopes due to low image contrast. In this study, we compared conventional, machine learning, and deep learning methods in chondrocyte segmentation and classification. We demonstrated that deep learning significantly improved the outcome of the chondrocyte segmentation and classification. With appropriate training, the deep learning method can achieve 90% accuracy in chondrocyte viability measurement. The significance of this work is that automated imaging analysis is possible and should not become a major hurdle for the use of nonlinear optical imaging methods in biological or clinical studies.& nbsp;(c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： image analysis image processing image processing algorithms Medical image processing Nonlinear microscopy Optical imaging

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Characterization and quantification of necrotic tissues and morphology in multicellular ovarian cancer tumor spheroids using optical coherence tomography

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BIOMEDICAL OPTICS EXPRESS 2021年第6期12卷 3352-3371页

作者： Yan, Feng Gunay, Gokhan Valerio, Trisha, I Wang, Chen Wilson, Jayla A. Haddad, Majood S. Watson, Maegan Connell, Michael O. Davidson, Noah Fung, Kar-Ming Acar, Handan Tang, Qinggong Univ Oklahoma Stephenson Sch Biomed Engn Norman OK 73019 USA Univ Oklahoma Dept Pathol Hlth Sci Ctr Oklahoma City OK 73104 USA Univ Oklahoma Stephenson Canc Ctr Hlth Sci Ctr Oklahoma City OK 73104 USA

The three-dimensional (3D) tumor spheroid model is a critical tool for high-throughput ovarian cancer research and anticancer drug development in vitro. However, the 3D structure prevents high-resolution imaging of the inner side of the spheroids. We aim to visualize and characterize 3D morphological and physiological information of the contact multicellular ovarian tumor spheroids growing over time. We intend to further evaluate the distinctive evolutions of the tumor spheroid and necrotic tissue volumes in different cell numbers and determine the most appropriate mathematical model for fitting the growth of tumor spheroids and necrotic tissues. A label-free and noninvasive swept-source optical coherence tomography (SS-OCT) imaging platform was applied to obtain two-dimensional (2D) and 3D morphologies of ovarian tumor spheroids over 18 days. Ovarian tumor spheroids of two different initial cell numbers (5,000-and 50,000-cells) were cultured and imaged (each day) over the time of growth in 18 days. Four mathematical models (Exponential-Linear, Gompertz, logistic, and Boltzmann) were employed to describe the growth kinetics of the tumor spheroids volume and necrotic tissues. Ovarian tumor spheroids have different growth curves with different initial cell numbers and their growths contain different stages with various growth rates over 18 days. The volumes of 50,000-cells spheroids and the corresponding necrotic tissues are larger than that of the 5,000-cells spheroids. The formation of necrotic tissue in 5,000-cells numbers is slower than that in the 50,000-cells ones. Moreover, the Boltzmann model exhibits the best fitting performance for the growth of tumor spheroids and necrotic tissues. Optical coherence tomography (OCT) can serve as a promising imaging modality to visualize and characterize morphological and physiological features of multicellular ovarian tumor spheroids. The Boltzmann model integrating with 3D OCT data of ovarian tumor spheroids provides great

关键词： image processing image processing algorithms Imaging techniques Optical coherence tomography Optical imaging Three dimensional imaging

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Efficient image-based object detection for floating weed collection with low cost unmanned floating vehicles

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SOFT COMPUTING 2021年第20期25卷 13093-13101页

作者： Sravanthi, Ravi Sarma, A. S., V Jawaharlal Nehru Technol Univ Ananthapuramu Anantapur Andhra Pradesh India PBR VITS Dept ECE Kavali Andhra Pradesh India

Unmanned floating vehicles (UFVs) can play a major role in the removal of invasive floating aquatic weeds in the water bodies. To provide automation to those UFVs, image or video processing techniques are helpful. Waterline detection from the sky-land-water region is the primary step for UFV autonomous navigation. After detecting the water region, object detection in the water region is the secondary step. In this paper, an object detection algorithm in water for low cost UFVs is proposed. Real-time and accurate waterline detection is done by using the K-means algorithm and Fast Marching method and it is compared with the Hough transform. Objects are detected accurately in the water region by a modified gradient-based image processing algorithm. In this algorithm, instead of Sobel Gradient Operator, Finite Difference Central Gradient Operator is used to achieve high accuracy in detecting objects in the water region. Floating green color weeds are also detected from objects using the green color of the weeds, and weed percentage is also calculated. Experiments were done on Own datasets, and OSF datasets containing freely floating plants, small cluster plants, and birds. Raspberry Pi 3 Model-B processor is used to implement this proposed algorithm. These performance metrics results show that the proposed detection approach is effective, more accurate, and suitable to different lake environments.

关键词： Autonomous floating vehicles Video acquisition image processing algorithms Water line detection Object detection Weed percentage calculation

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Gray consistency optical flow algorithm based on mask-R-CNN and a spatial filter for velocity calculation

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APPLIED OPTICS 2021年第34期60卷 10600-10609页

作者： Zhao, Donghua Wu, Yicheng Wang, Chenguang Shen, Chong Tang, Jun Liu, Jun Yu, Hua Lu, Zhumao North Univ China Sch Instrument & Elect Minist Educ Key Lab Instrumentat Sci & Dynam Measurement Taiyuan 030051 Peoples R China North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China State Grid Shanxi Elect Power Res Inst Taiyuan 030001 Peoples R China

The optical flow method has been widely used to measure the vehicle velocity by observing the stationary ground with a camera looking-down. However, when there are moving objects on the stationary ground, the interfering optical flow field will be generated, which decreases the velocity measurement accuracy of a vehicle relative to the ground. In order to reduce the effects caused by moving objects, this paper integrates pyramid Lucas-Kanade (LK) algorithm with the gray consistency method to use the information of color images thoroughly. First, a mask region with convolutional neural network (Mask-R-CNN) is used to recognize the objects that have motions relative to the ground, and it covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then images are decomposed into three channels, red, green, and blue (i.e., R, G, and B), and processed by median filter. Based on the gray consistency method, the optical flow can be obtained by the pyramidLKalgorithm. Finally, the velocity is calculated by the optical flowvalue. The prominent advantages of the proposed algorithm are: (i) increase the velocity measurement accuracy of a vehicle relative to the ground;(ii) use the information of color images acquired with cameras thoroughly and obtain velocity calculation outputs with less fluctuation;(iii) reduce wrong values caused by noises that are from the origin image and introduced by similar color masks. Four experiments are conducted to test the proposed algorithm and results with superior precision and reliability showthe feasibility and effectiveness of the proposed method for the velocity measurement accuracy of a vehicle relative to the ground. (C) 2021 Optical Society of America

关键词： image processing image processing algorithms Neural networks Nonlinear optical signal processing Optical flow Spatial filtering

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Aperture-type terahertz near-field imaging with a cylindrical frustum-shaped plastic probe

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APPLIED OPTICS 2021年第21期60卷 6308-6312页

作者： Wang, Nan Chang, Tianying Cui, Hong-Liang Jilin Univ Coll Instrumentat & Elect Engn Changchun 130012 Jilin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China

We designed an aluminum-coated plastic probe made from the combination of cylinder and a frustum of a cone for terahertz near-field imaging. The bunching width of the probe is 12 mu m and the electric field intensity enhancement factor just outside the probe tip reaches up to 25. An imaging resolution obtained through near-field imaging experiments on a variety of targets, including narrow metals and narrow media, is 6 mu m (lambda/450) at 0.11 THz. By adopting an adaptive threshold segmentation image processing algorithm that combines Hilbert scanning and wavelet transform, the influence of a large area of metal near the imaging area on the imaging result is successfully suppressed. Imaging of a scratch on the plastic substrate makes the new probe feasible for quality identification of nonmetallic cultural relics. (C) 2021 Optical Society of America

关键词： Electric fields image processing image processing algorithms Medical imaging Spatial resolution Wavelet transforms

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Exoscope-based videocapillaroscopy system for in vivo skin microcirculation imaging of various body areas

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BIOMEDICAL OPTICS EXPRESS 2021年第8期12卷 4627-4636页

作者： Machikhin, Alexander S. Volkov, Mikhail V. Khokhlov, Demid D. Lovchikova, Ekaterina D. Potemkin, Andrey, V Danilycheva, Inna, V Dorofeeva, Irina, V Shulzhenko, Andrey E. Russian Acad Sci Sci & Technol Ctr Unique Instrumentat Lab Acoustoopt Spect 15 Butlerova Moscow 117342 Russia ITMO Univ Fac Appl Opt 49 Kronverksky St Petersburg 197101 Russia Fed Med Biol Agcy Dept Allergol & Immunotherapy Inst Immunol 24 Kashirskoe Moscow 115522 Russia

The capillary system immediately responds to many pathologies and environmental conditions. Accurate monitoring of its functioning often enables early detection of various diseases related to disorders in skin microcirculation. To expand the scope of capillaroscopy application, it is reasonable to visualize and assess blood microcirculation exactly in the areas of inflamed skin. Body vibrations, breathing, non-flat skin surface and other factors hamper the application of conventional capillaroscopes outside the nailfold area. In this paper, we propose an exoscope-based optical system for high-quality non-invasive computational imaging of capillary network in various areas of the body. Accurate image matching and tracking temporal intensity variations allow detecting the presence of blood pulsations, precise mapping of capillaries and photoplethysmogram acquisition. We have demonstrated the efficiency of the proposed approach experimentally by in vivo mapping and analysis of microvessels in wrist, forearm, upper-arm, breast and hip areas. We believe that the developed system will increase the diagnostic value of video capillaroscopy in clinical practice. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Computational imaging image processing image processing algorithms image quality Optical coherence tomography Speckle imaging

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image fusion using a multi-level image decomposition and fusion method

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APPLIED OPTICS 2021年第24期60卷 7466-7479页

作者： Tian, Yu Yang, Wenjing Wang, Ji Natl Univ Def Technol Coll Comp Inst Quantum Informat Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp State Key Lab High Performance Comp Changsha 410073 Peoples R China

In recent years, image fusion has emerged as an important research field due to its various applications. images acquired by different sensors have significant differences in feature representation due to the different imaging principles. Taking visible and infrared image fusion as an example, visible images contain abundant texture details with high spatial resolution. In contrast, infrared images can obtain dear target contour information according to the principle of thermal radiation, and work well in all day/night and all weather conditions. Most existing methods employ the same feature extraction algorithm to get the feature information from visible and infrared images, ignoring the differences among these images. Thus, this paper proposes what we believe to be a novel fusion method based on a multi-level image decomposition method and deep learning fusion strategy for multi-type images. In image decomposition, we not only utilize a multi-level extended approximate low-rank projection matrix learning decomposition method to extract salient feature information from both visible and infrared images, but also apply a multi-level guide filter decomposition method to obtain texture information in visible images. In image fusion, a novel fusion strategy based on a pretrained ResNet50 network is presented to fuse multi-level feature information from both visible and infrared images into corresponding multi-level fused feature information, so as to improve the quality of the final fused image. The proposed method is evaluated subjectively and objectively in a large number of experiments. The experimental results demonstrate that the proposed method exhibits better fusion performance than other existing methods. (C) 2021 Optical Society of America

关键词： Feature extraction image fusion image processing image processing algorithms image quality Spatial resolution

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