Binary Thresholding Research Articles

Determination of the parameters of suspended particles of arbitrary shape by optoelectronic method

Data on the parameters of suspended particles are required in the electronic, optical, chemical and mining industries, powder metallurgy, atmospheric physics, medicine, etc. We present the results of determining the parameters of suspended particles of arbitrary shape using a combined optoelectronic approach, including a photoelectric method and a digital method of image processing. The results of both methods were used to correct the particle parameters and operational control of the laser emitter radiation. The operation of an optoelectronic device used to determine the parameters is based on the analysis of particle images in four projections, as well as on the registration of radiation scattered by particles and collected by an elliptical mirror. Spectrometry of the disperse composition of aerosols was carried out using a photomultiplier tube. The images of suspended particles for digital processing were obtained using a CCD array. It is shown that the applied image processing algorithms make it possible to remove noise, correct a background, improve the boundaries, determine the binarization threshold, exclude small particles, select closed areas and boundaries, split the image into the parts and count the number of particles in each of them, determine the parameters and classify the particles. Moreover, it becomes possible to restore the volumetric shape of a suspended particle and display the main characteristics on a digital indicator with an accuracy of 1%, which is significantly better than that of known similar devices. The results obtained can be used to control air pollution and improve the accuracy of identification of suspended particles of arbitrary shape.

Improvement of line image reproduction in the system of prepress engineer work

In order to optimise the process of prepress engineer work, it is necessary to provide a high-quality display of the line image, which ensures the most accurate transfer of the geometric dimensions of individual elements. The purpose of the article was to determine the degree of influence of the binarization threshold on the resolution value of raster line images. The experiments were based on the use of general scientific methods of analysis, generalisation, classification, deduction. To assess the quality of line image reproduction in this paper, photoforms were employed using a line test object which was designed as an accurate photograph with the use of an optical density distribution profile. The paper examines the influence of various parameters of raster structures on the playback quality of reproductions. The specifics of using a photo output device as the main link, which ensures the reproduction quality of image details, have been determined. The geometry of the raster structure when using rotation angles with rational tangents has been analysed. Features of the Accurate Screening technology have been systematised. The difference between “rational” and “irrational” rasterization methods has been considered. The main aspects of the use of line details in the reproduction process have been considered. The proposed method for assessing the quality of a line image reproduction with an uneven edge has been called the “signal-to-noise ratio” method, and it has been concluded that the scanning stage affects the quality of image reproduction to a greater extent than the photo output. The practical result of the work is the development of recommendations that can find practical application in reproduction processes. The developed binarization algorithms allow processing of images with significant zonal brightness unevenness, with monotonous brightness areas, and highly noisy images

Rapid non-destructive detection of foreign bodies in fish based on terahertz imaging and spectroscopy

Food safety is a matter of social stability and healthy development of the national economy. The processing technology of fish complicated and tedious and it is inclined to have fish bone residues or mixed with various foreign bodies during the processing. It is difficult to detect these foreign bodies by traditional manual and visual detection methods, which poses a serious threat to the health of people. In this case, this paper aims to explore the feasibility of rapid and non-destructive detection of endogenous foreign bodies (fish bones) and exogenous foreign bodies (metal, plastic, wood toothpicks) in fish based on terahertz imaging and spectroscopy. Methods: Firstly, the transmission terahertz spectral images of fish samples are acquired, and the terahertz spectral information of the region of interest is extracted. Secondly, In order to improve the accuracy of the model and reduce the computational effort, three algorithms including Competitive Adaptive Reweighting Sampling (CARS), Uninformative Variable Elimination (UVE) and Successive Projections Algorithm (SPA) are used to extract the spectral features. Thirdly, three qualitative discriminant models including Partial Least Squares Discriminant Analysis (PLS-DA), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) are established to detect and identify foreign bodies in fish. Finally, in order to realize the visual expression of Foreign Bodies, the terahertz images of fish are segmented by binarization threshold. Results: The model is optimized to obtain the best SVM model after CARS feature extraction with the accuracy rate of 99.56%. Meanwhile, the terahertz images of the samples at 0.1-4.0 THz are visualized and analyzed. It is found that the foreign bodies in fish are clearly outlined, and the shape and size of foreign bodies in fish can be well distinguished from terahertz images. Conclusion: This study has proved that the terahertz imaging and spectroscopy technology can achieve rapid and non-destructive detection of endogenous foreign bodies (fish bones) and exogenous foreign bodies (metal, plastic, wood toothpicks) in fish. It provides relevant basis and theoretical reference for rapid non-destructive detection of foreign bodies in food, which is of great significance for food process monitoring and quality control.

One-bit block sparse signal recovery via nonconvex ℓ2/ℓp(0<p<1)-minimization

One-bit compressed sensing (1-bit CS) shows that a sparse signal can exactly be recovered from extremely quantized linear measurements which just catch their signs. While in many applications, real-world signals also exhibit additional structures aside from standard sparsity, such as block-sparsity. We proposed a nonconvex ℓ2 / ℓp ( 0 < p < 1 ) -minimization model. Using the best approximation, covering number, and packing number, we obtain a weak condition to reconstruct block-sparse signals with high probability. And the lower bound of the required number of measurements is lower than some existing 1-bit CS methods. At last, we propose a block adaptive binary iteration thresholding algorithm to recover ℓ2 / ℓp effectively block sparse signals. The algorithm can be used without knowing the sparsity of the underlying signal. Several simulations are conducted to reveal the superiority of our methods to existing approaches, which expose the advantage of 1-bit CS in the reconstruction of block-sparse signals.

Study on the Identification and Detection of Walnut Quality Based on Terahertz Imaging.

Walnuts have rich nutritional value and are favored by the majority of consumers. As walnuts are shelled nuts, they are prone to suffer from defects such as mildew during storage. The fullness and mildew of the fruit impose effects on the quality of the walnuts. Therefore, it is of great economic significance to carry out a study on the rapid, non-destructive detection of walnut quality. Terahertz spectroscopy, with wavelengths between infrared and electromagnetic waves, has unique detection advantages. In this paper, the rapid and nondestructive detection of walnut mildew and fullness based on terahertz spectroscopy is carried out using the emerging terahertz transmission spectroscopy imaging technology. First, the normal walnuts and mildewed walnuts are identified and analyzed. At the same time, the image processing is carried out on the physical samples with different kernel sizes to calculate the fullness of the walnut kernels. The THz image of the walnuts is collected to extract the spectral information in different regions of interest. Four kinds of time domain signals in different regions of interest are extracted, and three qualitative discrimination models are established, including the support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) algorithms. In addition, in order to realize the visual expression of walnut fullness, the terahertz images of the walnut are segmented with a binarization threshold, and the walnut fullness is calculated by the proportion of the shell and kernel pixels. In the frequency domain signal, the amplitude intensity from high to low is the mildew sample, walnut kernel, and walnut shell, and the distinction between walnut kernel, shell samples, and mildew samples is high. The overall identification accuracy of the aforementioned three models is 90.83%, 97.38%, and 97.87%, respectively. Among them, KNN has the best qualitative discrimination effect. In a single category, the recognition accuracy of the model for the walnut kernel, walnut shell, mildew sample, and reference group (background) reaches 94%, 100%, 97.43%, and 100%, respectively. The terahertz transmission images of the five categories of walnut samples with different kernel sizes are processed to visualize the detection of kernel fullness inside walnuts, and the errors are less than 5% compared to the actual fullness of walnuts. This study illustrates that terahertz spectroscopy detection can achieve the detection of walnut mildew, and terahertz imaging technology can realize the visual expression and fullness calculation of walnut kernels. Terahertz spectroscopy and imaging provides a non-destructive detection method for walnut quality, which can provide a reference for the quality detection of other dried nuts with shells, thus having significant practical value.

Automatic Localization of License Plate for Car in Wolfram Mathematica

Modern imaging devices make it possible to solve a complex of technical applied problems that require the synthesis and analysis of computer processing methods using threshold binarization, image classification, clustering, and the use of machine learning to determine areas of interest. Thus, segmentation algorithms are widely used for processing medical images. Computer technologies are used for the functioning of the intellectual environment, which allows to analyze the state of human health. The development of microelectronics makes it possible to increase the complexity of the applied image processing algorithms used to solve applied engineering problems. The issues of segmentation, pattern recognition, description and presentation of details, morphological analysis of images obtained by industrial equipment are widely discussed in the literature. For example, theories of optical signal processing taking into account interference, issues of image perception and analysis are presented in detail in domestic and foreign literature. The paper describes the developed algorithm for localizing a car license plate, implemented in the Wolfram Mathematica system. First, the region of interest is determined, isolated from the rest of the image for its subsequent processing. An image representation is implemented using an affine transformation. Further segmentation of the characters on the license plate allows the characters to be identified. In the Mathematica system, a program code for the car license plate localization algorithm for its further recognition has been developed. The solution to the problem was obtained using the step-by-step application of the built-in and user-defined functions of the Wolfram Mathematica system. The algorithm has been tested on a representative sample of images. The average error did not exceed 10 %, which is in line with modern industrial image processing algorithms. The resulting car license plate identification algorithm can be used in digital devices to automatically determine and further image processing.

IMPACT OF PENETRATION AND IMAGE ANALYSIS IN OPTICAL COHERENCE TOMOGRAPHY ON THE MEASUREMENT OF CHOROIDAL VASCULARITY PARAMETERS.

To investigate the impact of penetration and image analysis in different optical coherence tomography (OCT) instruments on the measurement of choroidal vascularity parameters. Twenty-three healthy volunteers were imaged using two swept-source OCTs and one spectral-domain OCT. A fully automatic segmentation method based on ResNet-UNet and Niblack local threshold binarization was performed to quantify the relevant choroidal vascular parameters, including choroidal vascularity index, total choroidal volume, and luminal volume. The intraclass correlation coefficient (ICC) and coefficient of repeatability (COR) were used to analyze the repeatability and consistency of automatic and manual segmentation, respectively. Both swept-source OCT devices showed good consistency of luminal volume and total choroidal volume measurements (all ICC value >0.98 with COR% < 8.53%) based on manual segmentation, whereas the consistency of the spectral-domain OCT was lower (ICC value <0.60 with COR% > 40%), which was greatly improved after using the automatic algorithm (ICC value >0.99 with COR% < 4%). The repeatability of choroidal vascularity index obtained from different OCT images using manual or automatic segmentation showed good agreement (all ICC values >0.85), whereas the choroidal vascularity index measurement from the spectral-domain OCT was larger than the other two swept-source OCT devices (ICC value <0.65). For healthy youngsters, the penetration of OCT plays a role in the measurement precision for choroidal vascularity parameters, and automatic segmentation can improve the ability of choroidal boundary identification with deficient penetration, suggesting these factors need to be considered in clinical work.

Performance of Sentinel-1 and 2 imagery in detecting aquaculture waterbodies in Bangladesh

In this study, we evaluated the use of synthetic aperture radar (SAR) and multispectral data to detect aquaculture waterbodies in Southern Bangladesh to quantify fish production on a national scale. For this purpose, we developed an object-based framework comprised of three sequential stages: 1) water detection, 2) feature segmentation, and 3) feature classification. Techniques such as Edge-Otsu for binary thresholding, edge detection with convolution filters, and various supervised and unsupervised machine learning methods were used as part of a workflow. We found that ensemble products combining individual subproducts resulted in higher overall accuracy for water detection (overall detection rate around 60%) and waterbodies classification (overall accuracies up to 79%). Moreover, we showed that SAR data and shape indices played important roles in better-discriminating waterbodies. However, limitations in edge detection outcomes affected the identification of small and isolated aquaculture waterbodies, especially those integrated into rice fields, or in areas with trees. • Information about rapidly growing aquaculture in developing countries is limited. • Synthetic aperture radar and multispectral data were used for detecting aquaculture waterbodies. • Ensemble products have overall high accuracy for water detection/waterbodies classification. • Limitations in edge detection affected the identification of small and isolated waterbodies.

Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images.

Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies, such as diabetic retinopathy and glaucoma. In this article, we propose an efficient method based on a B-COSFIRE filter to tackle two challenging problems in fundus vessel segmentation: (i) difficulties in improving segmentation performance and time efficiency together and (ii) difficulties in distinguishing the thin vessel from the vessel-like noise. In the proposed method, first, we used contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, then excerpted region of interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary thresholding was used for vessel segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE, and CHASEDB1) of manually labeled images. The proposed method requires little processing time (around 12 s for each image) and results in the average accuracy, sensitivity, and specificity of 0.9604, 0.7339, and 0.9847 for the DRIVE database, and 0.9558, 0.8003, and 0.9705 for the STARE database, respectively. The results demonstrate that the proposed method has potential for use in computer-aided diagnosis.

Nondestructive Evaluation of Coating Defects and Uniformity Based on Terahertz Time-Domain Spectroscopy

Structural coatings are widely used because of their excellent mechanical and thermal properties. To evaluate defects and uniformity in coatings, both qualitatively and quantitatively, a terahertz time-domain spectroscopy (THz-TDS) detection technique is proposed. The thermal barrier coating is selected as a typical single-layer coating structure for quantitative defect detection. A wavelet noise reduction method is used on the acquired raw signals to eliminate noise while retaining detailed information. The peak value of the preprocessed signal is used as a feature parameter for imaging, and the automatic binarization threshold segmentation technique is used to describe the defects quantitatively. The automotive coating is selected as a typical multilayer coating structure for uniformity detection. The time-frequency characteristics of a strongly superimposed signal are analyzed; the peak-to-peak value is used as a feature parameter for imaging, and the peak-to-peak 3D imaging is then used to characterize the coating uniformity, enabling fast and intuitive acquisition of the coating state. The statistical characteristics of the standard deviation and range are used to evaluate the uniformity of each layer of the automotive coating. The results show that the uniformity of the clean coating is optimal. The results of a subsequent thickness inspection using an eddy current gauge are consistent with those of the terahertz technique. The results demonstrate that THz-TDS can effectively detect defects and uniformity in coatings.

Nondestructive Evaluation of Coating Defects and Uniformity Based on Terahertz Time-Domain Spectroscopy

Structural coatings are widely used because of their excellent mechanical and thermal properties. To evaluate defects and uniformity in coatings, both qualitatively and quantitatively, a terahertz time-domain spectroscopy (THz-TDS) detection technique is proposed. The thermal barrier coating is selected as a typical single-layer coating structure for quantitative defect detection. A wavelet noise reduction method is used on the acquired raw signals to eliminate noise while retaining detailed information. The peak value of the preprocessed signal is used as a feature parameter for imaging, and the automatic binarization threshold segmentation technique is used to describe the defects quantitatively. The automotive coating is selected as a typical multilayer coating structure for uniformity detection. The time-frequency characteristics of a strongly superimposed signal are analyzed; the peak-to-peak value is used as a feature parameter for imaging, and the peak-to-peak 3D imaging is then used to characterize the coating uniformity, enabling fast and intuitive acquisition of the coating state. The statistical characteristics of the standard deviation and range are used to evaluate the uniformity of each layer of the automotive coating. The results show that the uniformity of the clean coating is optimal. The results of a subsequent thickness inspection using an eddy current gauge are consistent with those of the terahertz technique. The results demonstrate that THz-TDS can effectively detect defects and uniformity in coatings.

Optimization of Sorting Processes of Partially Rotten Mango for Food Processing with Image Processing

This research aims to develop a method for improving the process of separating partially rotten fruit of ripe mango to the process of creating new creative Mango Stir-Fry. The partial rotten approximation process was based on image processing using Gaussian Mixture Model, HSV color model, dilation, erosion, Otsu’s binarization thresholding, and image color proportions techniques for mobile application development. The results showed that the techniques presented in this study could sort and approximate partially rotten mangoes with an accuracy of 99.06%. Moreover, the developed mobile application can approximate the net weight of ripe mango pulp and the net weight of Mango Stir-Fry to be obtained. These approximation errors were 0.68% and 1.91% for the net weight of net ripe mango pulp and Mango Stir-Fry, respectively. Therefore, this work helps the cooking process to classify partially rotten ripe mangoes and estimate the volume of processed products more conveniently.

Research on a Vehicle Recognition Method Based on Radar and Camera Information Fusion

To improve the accuracy and real-time performance of vehicle recognition in an advanced driving-assistance system (ADAS), a vehicle recognition method based on radar and camera information fusion is proposed. Firstly, the millimeter-wave radar and camera are calibrated jointly, the radar recognition information is mapped on the camera image, and the region of interest is established. Then, based on operator edge detection, global threshold binarization is performed on the image of the region of interest (ROI) to obtain the contour information of the vehicle in front, and Hough transform is used to fit the vehicle contour edge straight line. Finally, a sliding window is established according to the symmetry characteristics of the fitting line, which can find the vehicle region with the highest symmetry and complete the identification of the vehicle. The experimental results show that compared to the original recognition region of the radar, the mean square error of this algorithm is reduced by 13.4 and the single frame detection time is reduced to 28 ms. It is proven that the algorithm has better accuracy and a faster detection rate, and it can solve the problem of an inaccurate recognition region caused by radar error.

Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision.

The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. However, the tobacco shred types, especially expanded tobacco silk and tobacco silk, have no apparent differences in macro-scale characteristics. The tobacco shreds have small size and irregular shape characteristics, creating significant challenges in their recognition and classification based on machine vision. This study provides a complete set of solutions aimed at this problem for screening tobacco shred samples, taking images, image preprocessing, establishing datasets, and identifying types. A block threshold binarization method is used for image preprocessing. Parameter setting and method performance are researched to obtain the maximum number of complete samples with acceptable execution time. ResNet50 is used as the primary classification and recognition network structure. By increasing the multi-scale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. Specifically, the MS-ResNet network is obtained by fusing the multi-scale Stage 3 low-dimensional and Stage 4 high-dimensional features to reduce the overfitting risk. The number of blocks in Stages 1–4 are adjusted from the original 3:4:6:3 to 3:4:N:3 (A-ResNet) and 3:3:N:3 (B-ResNet) to obtain the X-ResNet network, which improves the model’s classification performance with lower complexity. The focal loss function is selected to reduce the impact of identification difficulty for different sample types on the network and improve its performance. The experimental results show that the final classification accuracy of the network on a tobacco shred dataset is 96.56%. The image recognition of a single tobacco shred requires 103 ms, achieving high classification accuracy and efficiency. The image preprocessing and deep learning algorithms for tobacco shred classification and identification proposed in this study provide a new implementation approach for the actual production and quality detection of tobacco and a new way for online real-time type identification of other agricultural products.

Binarization Algorithm Based on Side Window Multidimensional Convolution Classification.

Uneven illumination and space radiation can cause inhomogeneous grayscale distribution, low contrast, and noisy images in in-orbit cameras. A binarization algorithm based on morphological classification is proposed to solve the problem of inaccurate image binarization caused by space image degradation. Traditional local binarization algorithms generally calculate thresholds based on statistical information of gray dimensions within the local window, often ignoring the morphological distribution information, leading to poor results in degraded images. The algorithm presented in this paper demonstrates the property of the side window filtering (SWF) kernel on morphological clustering. First, the eight-dimensional SWF convolution kernel is used to describe the morphological properties of the pixels. Then, the positive and negative types of each pixel in the local window are identified, and the local threshold is calculated according to the difference between the two types. Finally, the positive pixel is used to filter the threshold of each pixel, with the binarization threshold satisfying the morphologically smooth and continuous property. A self-built dataset is used to evaluate the algorithm quantitatively and the results are compared with the three existing classical techniques using the quantitative measures FM, PSNR, and DRD. The experimental results show that the algorithm in this paper yields good binarization results for different degraded images, outperforms the comparison algorithm in terms of accuracy and robustness, and is insensitive to noise.

Robust binary fringe generation method with defocus adaptability

Current binary defocus technology mainly focuses on fringe generation suitable for specific frequencies without considering fringe adaptability for defocus-degree variation, which decreases the measuring accuracy for this scenario. To achieve a high-quality measurement, we propose a robust binary fringe generation method to minimize the phase error caused by changes in defocus and the random error in measurement. In the method, we establish a complete phase error model and construct a novel objective function, to the best of our knowledge, to optimize the binarization threshold of each pixel. Through derivation of the threshold gradient calculation formula, we quickly obtain optimal binary fringes that can adapt to different fringe pitches and various degrees of defocus. The experimental results verify that the proposed method can generate robust binary fringes adaptive to different fringe pitches and defocus degree variation, and thus achieve high-quality 3D measurements.

Fractal and multifractal spatiotemporal patterns of land surface temperatures in a coastal city

ABSTRACT In order to discover the fractal and multifractal nature of land surface temperature (LST) spatiotemporal patterns, this study computed the LST in thecoastal city, Xiamen, southeastern China using Landsat TM/OLI/TIRS images from 1994 to 2015, and introduced two-dimensional box-counting and two-dimensional multifractal box-counting methods to quantitatively characterize the LST pattern. Results suggest that increasing the binarization threshold decreased the box-counting dimensions with values from 1.86 to 1.65 in 1994, 1.67 in 2000, 1.56 in 2004, 1.57 in 2010, and 1.56 in 2015, respectively. The two-dimensional multifractal approach employs a set of intertwined nonfractal subsets over different spatial scales to describe the LST patterns. LST pattern possesses a increased multifractality and the similar multifractal spectra of left-hook shapes throughout the study period, suggesting the increasing trends of the spatial heterogeneity in LST distribution. The probability of a given pixel having a high LST value is consistently high in the study area, as indicated by the positive ratio between the regions in which the probability measure appears most concentrated and those in which it is most sparse. Rapid urbanization and the large-scale urban land surface changes in the coastal city determine the variation in the multifractal parameters.

Contrast Normalization Filtering Modules for Segmentations of Retinal Blood Vessels from Color Retinal Fundus Images

Vision loss is one of the main complications of eye disease, especially diabetic retinopathy (DR), because DR is a silent disease affecting the retina of the eye and resulting in loss of vision. Manual observation of eye disease takes time and delays effective treatment, so computerized methods are used to diagnose eye disease by extracting their features such as blood vessels, optic disc, and other abnormalities. Many computerized methods are proposed but it is still lacking to obtain small vessels. To overcome this problem, we have proposed methods based on image processing techniques for detection of retinal vessels. The proposed method is based on the elimination of uneven illumination using morphological tactics and principal component analysis. These initial steps are known as the preprocessing module, and our post-processing module contains the vessel coherence and the double threshold binarization method to obtain an image of the segmented vessels. Our proposed method obtained comparable results (average results (sensitivity: 0.78, specificity: 0.95 and precision: 0.951)) compared to the existing methods on the DRIVE and STARE databases.

Automated counting of cerebral penetrating vessels using optical coherence tomography images of a mouse brain in vivo.

Penetrating blood vessels emanating from cortical surface vasculature and lying deep in the cortex are essential vascular conduits for the shuttling of blood from superficial pial vessels to the capillary beds in parenchyma for the nourishment of neuronal brain tissues. Locating and counting the penetrating vessels is beneficial for the quantification of a course of ischemia in blood occlusive events such as stroke. This paper seeks to demonstrate and validate a method for automated penetrating vessel counting that uses optical coherence tomography (OCT). This paper proposes an OCT method that effectively identifies and grades the cortical penetrating vessels in perfusion. The key to the proposed method is the harnessing of vascular features found in the penetrating vessels, which are distinctive from those of other vessels. In particular, with an increase in the light attenuation and flow turbulence, the contrast in the mean projection of the OCT datacube decreases, whereas that in the maximum projection of the Doppler frequency variance datacube increases. By multiplying the inversion of the former with the latter, its binary thresholding is sufficient to highlight the penetrating vessels and allows for their counting over the projection image. A computational method that leverages the decrease in mean OCT projection intensity and the increase in Doppler frequency variance at the penetrating vessel is developed. It successfully identifies and counts penetrating vessels with a high accuracy of over 87%. The penetrating vessel density is observed to be significantly reduced in the mouse model of focal ischemic stroke. The OCT analysis is effective for counting penetrating blood vessels in mice brains and may be applied to the rapid diagnosis and treatment of stroke in stroke models of small animals.

Murine cardiac fibrosis localization using adaptive Bayesian cardiac strain imaging in vivo

An adaptive Bayesian regularized cardiac strain imaging (ABR-CSI) algorithm for in vivo murine myocardial function assessment is presented. We report on 31 BALB/CJ mice (n = 17 females, n = 14 males), randomly stratified into three surgical groups: myocardial infarction (MI, n = 10), ischemia–reperfusion (IR, n = 13) and control (sham, n = 8) imaged pre-surgery (baseline- BL), and 1, 2, 7 and 14 days post-surgery using a high frequency ultrasound imaging system (Vevo 2100). End-systole (ES) radial and longitudinal strain images were used to generate cardiac fibrosis maps using binary thresholding. Percentage fibrotic myocardium (PFM) computed from regional fibrosis maps demonstrated statistically significant differences post-surgery in scar regions. For example, the MI group had significantly higher PFMRadial (%) values in the anterior mid region (p = 0.006) at Day 14 (n = 8, 42.30 ± 14.57) compared to BL (n = 12, 1.32 ± 0.85). A random forest classifier automatically detected fibrotic regions from ground truth Masson’s trichrome stained histopathology whole slide images. Both PFMRadial (r = 0.70) and PFMLongitudinal (r = 0.60) results demonstrated strong, positive correlation with PFMHistopathology (p < 0.001).