Spherical-matching hyperbolic-array photoacoustic computed tomography

Linear-array photoacoustic computed tomography (LA-PACT), for its flexibility and simplicity, has great potential in providing anatomical and functional information of tissues. However, the limited coverage view impedes the LA-PACT obtaining high-quality images. In this study, a photoacoustic tomographic system with a hyperbolic-array transducer was developed for stereoscopic PA imaging of carotid artery. The hyperbolic-array PACT increases the receiving sensitivity for PA signal detection due to its transducer's geometric structure matching with the spherical wave. The control phantom experiment shows that the proposed system can expand the angular coverage of ∼1/3 more than that of the LA-PACT system, and the volumetric PA images of rat's carotid artery demonstrates the potential of the system for carotid artery imaging. Furthermore, volumetric imaging of the human forearm verifies that the system has significant capability in human imaging, which indicates that it has bright prospect for assisting diagnosis in the vascular disease.     

Quantitative measurement of blood glucose influenced by multiple factors via photoacoustic technique combined with optimized wavelet neural networks

In this work, the photoacoustic (PA) quantitative measurement of blood glucose concentration (BGC) influenced by multiple factors was firstly investigated. A set of PA detection system of blood glucose considering the comprehensive influence of five factors was established. The PA signals and peak-to-peak values (PPVs) of 625 rabbit whole blood were obtained under 625 influence combinations. Due to the accurate measurement of BGC limited by the overlap PA signals, wavelet neural network (WNN) was utilized to train the PPVs of blood glucose for 500 rabbit blood. The mean square error (MSE) of BGC for 125 testing blood was approximately 6.5782 mmol/L. To decrease the MSE, the parameters of WNN were optimized by particle swarm optimization (PSO), that is, PSO-WNN algorithm was employed. Under the optimal parameters, MSE of BGC was decreased to approximately 0.48005 mmol/L. To further improve the prediction accuracy of BGC, an improved nonlinear dynamic inertia weight (NDIW) strategy of PSO was proposed, and compared with other two kinds of dynamic inertia weight strategies. Under the optimal parameters, the MSE of BGC was decreased to approximately 0.2635 mmol/L. The comparison of nine algorithms demonstrate that the PA technique combined with PSO-WNN and the improved NDIW strategy is significant in the quantitative measurement of blood glucose influenced by multiple factors.     

Deep learning protocol for improved photoacoustic brain imaging

One of the key limitations for the clinical translation of photoacoustic imaging is penetration depth that is linked to the tissue maximum permissible exposures (MPE) recommended by the American National Standards Institute (ANSI). Here, we propose a method based on deep learning to virtually increase the MPE in order to enhance the signal‐to‐noise ratio of deep structures in the brain tissue. The proposed method is evaluated in an in vivo sheep brain imaging experiment. We believe this method can facilitate clinical translation of photoacoustic technique in brain imaging, especially in transfontanelle brain imaging in neonates.     

基于Mel倒谱系数和矢量量化的昆虫声音自动鉴别

为了给生产单位害虫管理的普通技术人员提供简便易操作的昆虫种类鉴别方法, 本研究把人类语音识别领域的先进技术应用于昆虫识别, 提出了一种新颖的昆虫声音自动鉴别方法, 用声音参数化技术为昆虫声纹识别设计了一种简单易行的方案。声音信号经过预处理、分段得到一系列的声音样本, 从声音样本提取Mel倒谱系数（MFCC）, 并用Linde-Buzo-Gray（LBG）算法对提取的MFCC进行矢量量化(VQ), 所得码字作为声音样本的特征模型。特征参数之间的匹配用搜索最近邻的方法实现。本文方法在包含70种昆虫声音的库中进行了试验, 取得了超过96%的识别率和理想的时间性能。试验结果证明了该方法的有效性。     

Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography

Optical coherence tomography (OCT) has shown potential in differentiating normal colonic mucosa from neoplasia. In this study of 33 fresh human colon specimens, we report the first use of texture features and computer vision-based imaging features acquired from en face scattering coefficient maps to characterize colorectal tissue. En face scattering coefficient maps were generated automatically using a new fast integral imaging algorithm. From these maps, a gray-level cooccurrence matrix algorithm was used to extract texture features, and a scale-invariant feature transform algorithm was used to derive novel computer vision-based features. In total, 25 features were obtained, and the importance of each feature in diagnosis was evaluated using a random forest model. Two classifiers were assessed on two different classification tasks. A support vector machine model was found to be optimal for distinguishing normal from abnormal tissue, with 94.7% sensitivity and 94.0% specificity, while a random forest model performed optimally in further differentiating abnormal tissues (i.e., cancerous tissue and adenomatous polyp) with 86.9% sensitivity and 85.0% specificity. These results demonstrated the potential of using OCT to aid the diagnosis of human colorectal disease.     

Label-free high frame rate imaging of circulating blood clots using a dual modal ultrasound and photoacoustic system

Deep vein thrombosis (DVT) is a disorder when a blood clot (thrombus) is formed in one of the deep veins. These clots detach from the original sites and circulate in the blood stream at high velocities. Diagnosing these blood clots at an early stage is necessary to decide the treatment strategy. For label-free, in vivo, and real-time detection, high framerate photoacoustic imaging can be used. In this work, a dual modal clinical ultrasound and photoacoustic (PA) system is used for the high framerate PA imaging of circulating blood clots in blood at linear velocities up to 107 cm/sec. Blood clot had 1.4 times higher signal-to-noise ratio (SNR) in the static mode and 1.3 times higher SNR compared to blood PA signal in the flow experiments. This work demonstrates that fast-moving circulating blood clots are easy to recognize against the background PA signal and may aid in early diagnosis.     

A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.

     

Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector

Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. In this paper, a new algorithm is developed for the automatic recognition of the fetal facial standard planes (FFSPs) such as the axial, coronal, and sagittal planes. Specifically, densely sampled root scale invariant feature transform (RootSIFT) features are extracted and then encoded by Fisher vector (FV). The Fisher network with multi-layer design is also developed to extract spatial information to boost the classification performance. Finally, automatic recognition of the FFSPs is implemented by support vector machine (SVM) classifier based on the stochastic dual coordinate ascent (SDCA) algorithm. Experimental results using our dataset demonstrate that the proposed method achieves an accuracy of 93.27% and a mean average precision (mAP) of 99.19% in recognizing different FFSPs. Furthermore, the comparative analyses reveal the superiority of the proposed method based on FV over the traditional methods.     

Automatic target recognition based on cross-plot

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository.     

Photoacoustic imaging of the human placental vasculature

Minimally invasive fetal interventions require accurate imaging from inside the uterine cavity. Twin‐to‐twin transfusion syndrome (TTTS), a condition considered in this study, occurs from abnormal vascular anastomoses in the placenta that allow blood to flow unevenly between the fetuses. Currently, TTTS is treated fetoscopically by identifying the anastomosing vessels, and then performing laser photocoagulation. However, white light fetoscopy provides limited visibility of placental vasculature, which can lead to missed anastomoses or incomplete photocoagulation. Photoacoustic (PA) imaging is an alternative imaging method that provides contrast for hemoglobin, and in this study, two PA systems were used to visualize chorionic (fetal) superficial and subsurface vasculature in human placentas. The first system comprised an optical parametric oscillator for PA excitation and a 2D Fabry‐Pérot cavity ultrasound sensor; the second, light emitting diode arrays and a 1D clinical linear‐array ultrasound imaging probe. Volumetric photoacoustic images were acquired from ex vivo normal term and TTTS‐treated placentas. It was shown that superficial and subsurface branching blood vessels could be visualized to depths of approximately 7 mm, and that ablated tissue yielded negative image contrast. This study demonstrated the strong potential of PA imaging to guide minimally invasive fetal therapies.      

基于多特征融合的多分类运动想象脑电信号识别研究

针对目前多分类运动想象脑电识别存在特征提取单一、分类准确率低等问题,提出一种多特征融合的四分类运动想象脑电识别方法来提高识别率。对预处理后的脑电信号分别使用希尔伯特-黄变换、一对多共空间模式、近似熵、模糊熵、样本熵提取结合时频—空域—非线性动力学的初始特征向量,用主成分分析降维,最后使用粒子群优化支持向量机分类。该算法通过对国际标准数据集BCI2005 Data set IIIa中的k3b受试者数据经MATLAB仿真处理后获得93.30%的识别率,均高于单一特征和其它组合特征下的识别率。分别对四名实验者实验采集运动想象脑电数据,使用本研究提出的方法处理获得了72.96%的平均识别率。结果表明多特征融合的特征提取方法能更好的表征运动想象脑电信号,使用粒子群支持向量机可取得较高的识别准确率,为人脑的认知活动提供了一种新的识别方法。     

MAD-YOLO: A quantitative detection algorithm for dense small-scale marine benthos

Marine biological resources are abundant, and the reasonable development, research and protection of marine biological resources are of great significance to marine ecological health and economic development. At present, underwater object quantitative detection plays a very important role in marine biological science research, marine species richness survey, and rare species conservation. However, the problems of a large amount of noise in the underwater environment, small object scale, dense biological distribution, and occlusion all increase the detection difficulty. In this paper, a detection algorithm MAD-YOLO (Multiscale Feature Extraction and Attention Feature Fusion Reinforced YOLO for Marine Benthos Detection) is proposed, which is based on improved YOLOv5 is proposed to solve the above problems. To improve the adaptability of the network to the underwater environment, VOVDarkNet is designed as the feature extraction backbone. It uses the intermediate features with different receptive fields to reinforce the ability to extract feature. AFC-PAN is proposed as the feature fusion network so that the network can learn correct feature information and location information of objects at various scales, improving the network's ability to perceive small objects. Label assignment strategy SimOTA and decoupled head are introduced to help the model better handles occlusion and dense distribution problems. Experiments show the MAD-YOLO algorithm increases mAP_0.5:0.95 on the URPC2020 dataset from 49.8% to 53.4% compared to the original YOLOv5. Moreover, the advantages of the model are visualized and analyzed by the method of controlling variables in the experimental part. The experiments show that MAD-YOLO is suitable for detecting blurred, dense, and small-scale objects. The model performs well in marine benthos detection tasks and can effectively promote marine life science research and marine engineering implementation. The source code is publicly available at https://github.com/JoeNan1/MAD-YOLO.     

Photoacoustic and ultrasound (PAUS) dermoscope with high sensitivity and penetration depth by using a bimorph transducer

A bimorph transducer was proposed to improve the detection sensitivity and imaging depth of photoacoustic and ultrasound (PAUS) dermoscope. By applying the bimorph transducer, the imaging depth and sensitivity of PAUS dermoscope were enhanced by simultaneously improving excitation efficiency and reception bandwidth. The integrated design of the imaging head of the dermoscope makes it highly convenient for detecting human skin. The PAUS imaging performance was demonstrated via visualizing subcutaneous tumor and depicting full structures of different skin layers from epidermis to subcutaneous tissue. The results confirm that the dermoscope with the bimorph transducer is well suited for PA and US dual‐modality imaging, which can provide multi‐information for skin disease.     

Fingerprint matching using the onion peeling approach and turning function

Fingerprint, as one of the most popular and robust biometric traits, can be used in automatic identification and verification systems to identify individuals. Fingerprint matching is a vital and challenging issue in fingerprint recognition systems. Most fingerprint matching algorithms are minutiae-based. The minutiae points are the ways that the fingerprint ridges can be discontinuous. Ridge ending and ridge bifurcation are two frequently used minutiae in most fingerprint matching algorithms. This article presents a new minutiae-based fingerprint matching using the onion peeling approach. In the proposed method, fingerprints are aligned to find the matched minutiae points. Then, the nested convex polygons of matched minutiae points are constructed and the comparison between peer-to-peer polygons is performed by the turning function distance. Simplicity, accuracy, and low time complexity of the onion peeling approach are three important factors that make it a standard method for fingerprint matching purposes. The performance of the proposed algorithm is evaluated on the database FVC2002. Since the fingerprints that the difference between the number of their layers is more than 2 and the a minutiae matching score lower than 0.15 are ignored, better results are obtained.KeywordsFingerprint Matching, Minutiae, Convex Layers, Turning Function, Computational Geometry.     

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html     

Nonlinear iterative perturbation scheme with simplified spherical harmonics (SP3) light propagation model for quantitative photoacoustic tomography

When using quantitative photoacoustic tomography (q-PAT) reconstruction to recover the optical absorption coefficients of tissue, the commonly used diffusion equation has several limitations in the case of the objects that have small geometries and high-absorption or low-scattering areas. Furthermore, the conventional perturbation reconstruction strategy is unsatisfactory when the target tissue containing large heterogeneous features. We herein present a modified q-PAT implementation that employs the higher-order photon migration model achieving the tradeoff between mathematical rigidity and computational efficiency. Besides, a nonlinear iterative method is proposed to obtain the perturbations of optical absorption considering the updating of the sensitivity matrix in calculating the fluence perturbations. Consequently, the distribution of tissue optical properties can be recovered in a robust way even if the targets with high absorption are included. The proposed approach has been validated by simulation, phantom and in vivo experiments, exhibiting promising performances in image fidelity and quantitative feasibility for practical applications.     

Role of blood oxygenation saturation in ovarian cancer diagnosis using multi-spectral photoacoustic tomography

In photoacoustic tomography (PAT), a tunable laser typically illuminates the tissue at multiple wavelengths, and the received photoacoustic waves are used to form functional images of relative total haemoglobin (rHbT) and blood oxygenation saturation (%sO₂). Due to measurement errors, the estimation of these parameters can be challenging, especially in clinical studies. In this study, we use a multi-pixel method to smooth the measurements before calculating rHbT and %sO₂. We first perform phantom studies using blood tubes of calibrated %sO₂ to evaluate the accuracy of our %sO₂ estimation. We conclude by presenting diagnostic results from PAT of 33 patients with 51 ovarian masses imaged by our co-registered PAT and ultrasound system. The ovarian masses were divided into malignant and benign/normal groups. Functional maps of rHbT and %sO₂ and their histograms as well as spectral features were calculated using the PAT data from all ovaries in these two groups. Support vector machine models were trained on different combinations of the significant features. The area under ROC (AUC) of 0.93 (0.95%CI: 0.90-0.96) on the testing data set was achieved by combining mean %sO₂, a spectral feature, and the score of the study radiologist.     

Facial recognition — Part 1

Out of all the biometric technologies, the facial recognition sector has received the most attention in recent months. Although the technology is not as accurate as some others, such as fingerprint or iris recognition, it does have unique features that elude other biometric modalities, and that make it an attractive biometric to use in a multitude of scenarios. The first part of this two-part survey looks at the issues surrounding this technology, predicted market sizes and the types of technology on offer. Part two will look in depth at the companies and individual products in this exciting market, as well as listing full contact details for the main players.     

High-resolution volumetric information fusion for depth of field enhancement in photoacoustic microscopy

Optical-resolution photoacoustic microscopy suffers from limited depth of field due to the strongly focused laser beam. Here, a novel volumetric information fusion is proposed to achieve large volumetric and high-resolution imaging. First, three-dimensional stationary wavelet transform was performed on the multi-focus data to obtain eight wavelet coefficients. Differential evolution based on joint weighted evaluation was then employed to optimize the block size of division for each wavelet coefficient. The proposed fusion rule using standard deviation for focus detection was used to fuse the corresponding sub-coefficients. Finally, photoacoustic imaging with large depth of field can be achieved by the inverse stationary wavelet transform. Performance test shows that the depth of field of photoacoustic imaging can be doubled without sacrificing lateral resolution. The proposed volumetric information fusion can further promote the capability of volumetric imaging of optical-resolution photoacoustic microscopy and will be helpful in the acquisition of physiological and pathological process.     

Deep self-supervised transformation learning for leukocyte classification

The scarcity of training annotation is one of the major challenges for the application of deep learning technology in medical image analysis. Recently, self-supervised learning provides a powerful solution to alleviate this challenge by extracting useful features from a large number of unlabeled training data. In this article, we propose a simple and effective self-supervised learning method for leukocyte classification by identifying the different transformations of leukocyte images, without requiring a large batch of negative sampling or specialized architectures. Specifically, a convolutional neural network backbone takes different transformations of leukocyte image as input for feature extraction. Then, a pretext task of self-supervised transformation recognition on the extracted feature is conducted by a classifier, which helps the backbone learn useful representations that generalize well across different leukocyte types and datasets. In the experiment, we systematically study the effect of different transformation compositions on useful leukocyte feature extraction. Compared with five typical baselines of self-supervised image classification, experimental results demonstrate that our method performs better in different evaluation protocols including linear evaluation, domain transfer, and finetuning, which proves the effectiveness of the proposed method.