Pixel Intensity Distribution Research Articles

Machined Surface Image Analysis for Process Instability Identification in Micromilling of Ti6Al4V

The low flexural rigidity of micro-end mill used in micromilling process undergoes catastrophic failure at low amplitude chatter and hence it is critical to identify the low amplitude chatter to produce the chatter free machined surfaces. An unfixing of the workpiece to identify the chatter by analyzing the power spectral density of machined surface topography alters the process set-up and even a minute change in workpiece surface flatness during re-fixing the workpiece lead to variation in depth of cut, which changes the precision of machined surface in micromilling process. Consequently, in the present work, an image analysis method has been proposed for machined surface analysis in micromilling of Ti6Al4V. Different statistical parameters like arithmetic gray level average value of the distribution of pixel intensities and optical parameter have been analyzed to detect the process instability. The estimated statistical parameters of the machined surface images for different process parameters has been compared with the measured surface roughness of the machined surfaces. The frequency spectrum analysis of the machined surface image analysis has also been carried out, which shows the distribution of pixel intensities at different frequencies. Arithmetic gray level average of pixel intensity of the machined surface image has been found to be decreasing with an increases of the roughness of the machined surface. The optical parameter of the machined surface increases with the increases of the roughness. The critical value of arithmetic gray level average of pixel intensity and optical parameter was found to be 148 and 0.36, respectively for chatter onset. The distribution of pixel intensity at different frequencies shows the diffused pattern of brightness of the pixel away from the center location in chatter dominant machining while the distribution of brightness of pixel was found to be circular in nature around the central spot in stable machining. The chatter and stable machining condition has also been verified with the vibration spectrum of the workpiece, where the vibration of the workpiece has been captured using accelerometer. The proposed image analysis method can further be used for online monitoring of machining process.

Detection of early decayed oranges by structured-illumination reflectance imaging coupling with texture feature classification models.

Citrus fruits are susceptible to fungal infection after harvest. To reduce the economic loss, it is necessary to reject the infected citrus fruit before storage and transportation. However, the infected area in the early stage of decay is almost invisible on the fruit surface, so the detection of early decayed citrus is very challenging. In this study, a structured-illumination reflectance imaging (SIRI) system combined with a visible light-emitting diode (LED) lamp and a monochrome camera was developed to detect early fungal infection in oranges. Under sinusoidal modulation illumination with spatial frequencies of 0.05, 0.15, and 0.25 cycles mm–1, three-phase-shifted images with phase offsets of − 2π/3, 0, and 2π/3 were acquired for each spatial frequency. The direct component (DC) and alternating component (AC) images were then recovered by image demodulation using a three-phase-shifting approach. Compared with the DC image, the decayed area can be clearly identified in the AC image and RT image (AC/DC). The optimal spatial frequency was determined by analyzing the AC image and pixel intensity distribution. Based on the texture features extracted from DC, AC, and RT images, four kinds of classification models including partial least square discriminant analysis (PLS-DA), support vector machine (SVM), least squares-support vector machine (LS-SVM), and k-nearest neighbor (KNN) were established to detect the infected oranges, respectively. Model optimization was also performed by extracting important texture features. Compared to all models, the PLS-DA model developed based on eight texture features of RT images achieved the optimal classification accuracy of 96.4%. This study showed for the first time that the proposed SIRI system combined with appropriate texture features and classification model can realize the early detection of decayed oranges.

Attention-Guided Global-Local Adversarial Learning for Detail-Preserving Multi-Exposure Image Fusion

Deep learning networks have recently demonstrated yielded impressive progress for multi-exposure image fusion. However, how to restore realistic texture details while correcting color distortion is still a challenging problem to be solved. To alleviate the aforementioned issues, in this paper, we propose an attention-guided global-local adversarial learning network for fusing extreme exposure images in a coarse-to-fine manner. Firstly, the coarse fusion result is generated under the guidance of attention weight maps, which acquires the essential region of interest from both sides. Secondly, we formulate an edge loss function, along with a spatial feature transform layer, for refining the fusion process. So that it can take full use of the edge information to deal with blurry edges. Moreover, by incorporating global-local learning, our method can balance pixel intensity distribution and correct the color distortion on spatially varying source images from both image/patch perspectives. Such a global-local discriminator ensures all the local patches of the fused images align with realistic normal-exposure ones. Extensive experimental results on two publicly available datasets show that our method drastically outperforms state-of-the-art methods in visual inspection and objective analysis. Furthermore, sufficient ablation experiments prove that our method has significant advantages in generating high-quality fused results with appealing details, clear targets, and faithful color. Source code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JinyuanLiu-CV/AGAL</uri> .

Texture analysis of myometrium-derived T2WI in the evaluation of placenta increta: An observational retrospective study

IntroductionMRI has demonstrated its potential in the diagnosis of placenta percreta. Texture analysis is a novel technique to quantify tissue heterogeneity. The study aimed to evaluate the feasibility of using texture analysis based on myometrium-derived T2WI to differentiate placenta accreta from increta. MethodsParticipants with MRI and clinical or histopathological diagnosis of placenta increta were retrospectively enrolled. Texture analysis of T2WI was implemented on normal myometrium and placenta increta by MaZda software. With the Fisher discriminant method, parameter selection and reduction were done automatically. Multivariate analysis was used for the comparison of response variables between two groups. The contours of multivariable average vectors were compared using profile analysis. Two-step clustering was performed to assess the importance of parameters. ResultsThere were a total of 23 participants (median age 29 years, range 22–43 years). The pixel intensity distribution was narrow and wide in two first-order histograms taken from normal myometrium and placenta increta, respectively. Multivariate analysis showed nine second-order parameters derived from the histogram were statistically significant (P < 0.05). The results of two-step clustering indicated that three second-order parameters (Mean, Percentile 90%, and Percentile 99%) were important (predictor importance > 0.8). Multivariate analysis of three second-order parameters further showed they were different between normal myometrium and placenta increta. DiscussionTexture analysis based on myometrium-derived T2WI may be a useful add-on to MRI in diagnosing placenta increta. Trial registrationRegistration number: ChiCTR2000038604 and name of registry: Evaluation of diagnostic accuracy of MRI multi-parameter imaging combined with texture analysis for placenta accreta spectrum disorders (PAD).

Using global information to refine local patterns for texture representation and classification

Local binary pattern (LBP) and its variants have been successfully applied in texture feature extraction. However, it is hard for most LBP-based methods to effectively describe and distinguish the local neighborhoods with similar structures (that is, the calculated feature patterns are identical) but different contrasts or grayscales. To alleviate such problems, we propose a novel global refined local binary pattern (GRLBP) by analyzing the nature of pixel intensity distribution in local neighborhoods. GRLBP consists of two descriptors called magnitude refined local sign binary pattern (MRLBP_S) and center refined local magnitude binary pattern (CRLBP_M). MRLBP_S distinguishes local neighborhoods with contrast differences by using global magnitude anchors to refine local sign patterns. And CRLBP_M identifies local neighborhoods with grayscale differences by employing global central grayscale anchors to refine local magnitude patterns. Finally, frequency histograms of MRLBP_S and CRLBP_M from each image are cascaded to generate the GRLBP. Extensive experimental results on seven benchmark texture databases: Outex, CUReT, KTH-TIPS, UMD, UIUC, KTH-T2b, and DTD demonstrate that the proposed GRLBP can represent the detailed information of texture images. Furthermore, compared with state-of-the-art LBP variants, GRLBP has competitive advantages in classification accuracy, feature dimension, and computational complexity, respectively.

An optimization method for motion blur image restoration and ringing suppression via texture mapping

Since the image sensor will produce blur problems in the process of collecting data of moving objects, the image needs to be restored. Ringing is one of the most common artifacts in deblurred images. This paper proposes a non-blind image deconvolution method based on texture mapping segmentation, named texture-Richardson–Lucy (TRL) algorithm, which suppresses ringing while deblurring the image. TRL is based on a novel ringing removal deconvolution algorithm, which adds a ringing detection term as regularization in the iterative process of the Richardson–Lucy algorithm. Taking into account the structural difference between the texture and the flat area, the image is segmented into several blocks and restored through adaptive iterative texture maps based on the pixel intensity and texture features of the image. In order to obtain a reasonable texture map, a Gaussian mixture model is used to fit the pixel intensity distribution, and use the expectation maximization algorithm and local binary mode to estimate. Experimental results and quantitative evaluations show that TRL can effectively reduce ringing artifacts while retaining details and achieving robustness to suppress ringing of different blur kernels. The processing time of a single 1 million pixel image in an 8-core CPU environment is about 3.5 s. And the PSNR and SSIM parameters are above 30 dB and 0.92, respectively. In conclusion, TRL is superior to the current popular algorithms.

Plenoptic 2.0 Intra Coding Using Imaging Principle

Plenoptic 2.0 videos that are captured by focused plenoptic cameras outperform the traditional plenoptic videos with higher spatial resolution of rendered subaperture images and more effective light field sampling. However, its distinct imaging principle causes different pixel intensity distribution and complex macropixel structures, which results in ineffectiveness in coding them using the existing plenoptic video compression methods. Thus, a novel compression method is proposed in this paper to dramatically improve the intra prediction efficiency for multi-focus plenoptic 2.0 video coding. It consists of two new intra prediction modes: imaging-principle-guided static prediction (ISP) and imaging-principle-guided zoomed prediction (IZP). In ISP, the prediction candidates spatially positioned by plenoptic 2.0 imaging principle are weighted by minimizing boundary error to generate the predicted samples. In IZP, the spatial positions and scales of the prediction candidates vary according to the focusing status and the focal length of the microlens that generates the pixels in the prediction candidates. The combination of two modes generalizes plenoptic 2.0 video coding for multi-focus plenoptic cameras. The proposed approach outperforms high efficiency video coding (HEVC) standard by an average of 15.37% bitrate reduction under All Intra configuration. It also outperforms other state-of-the-art methods with obvious compression efficiency improvements.

Initial observation of contrast profiles for 3D and 2D MRI sequences in MR-guided radiation therapy for locally advanced pancreatic cancer.

541 Background: MR-guided stereotactic body radiation therapy (MR-SBRT) is a novel method of treating mobile tumors with soft-tissue gating and on-table adaptive planning. In our experience using the ViewRay MRIdian system (VR) for treating locally advanced pancreatic cancer (PA) with MR-SBRT, the true-fast imaging with steady-state free precession (TRUFI) sequences on the VR impart differing intensities for relevant structures seen on the pre-treatment high resolution 3D MRI (3D MRI) versus the real-time 2D cine MRI (2D cine) used for target tracking. Since these variations can confound target tracking selection, we propose that an understanding of the differing contrast profiles could improve selection of tracking structures and optimize treatment delivery. Methods: We retrospectively reviewed both 3D MRI and 2D cine images for patients (pts) with PA (n =20) treated on the VR. At simulation, an appropriate tracking target was identified and contoured on a single 3mm sagittal slice of the 3D MRI. This sagittal slice was directly compared to the registered 7mm 2D cine to identify structures with notable discrepancies in signal intensity. The 3D MRI was then explored in additional planes to confirm structure identities. For quantitative verification of the clinically observed differences, the pixel intensity distributions of 3D MRI and 2D cine DICOM image datasets were statistically compared. Results: In all pts reviewed, arteries (aorta, celiac, SMA) appeared with similar contrast profiles on both images. However, veins (portal vein, SMV) appeared hypointense on 3D MRI but hyperintense on 2D cine. Biliary structures appeared hyperintense on 3D MRI but only mildly hyperintense on 2D cine. The pixel intensity distributions extracted from 3D MRI and 2D cine images were confirmed to differ significantly (two sample Kolmogorov-Smirnov test; test statistic =0.40; p &lt; 0.001). Conclusions: There are significant variations in image intensity between the initial treatment planning 3D MRI and the immediate pre-treatment 2D cine obtained with the VR. Understanding these discrepancies can guide radiation oncologists in choosing optimal tracking targets. Future work will focus on identifying the particular causes and frequencies of target tracking failures and exploring alternative tracking algorithms using artificial intelligence which could ultimately allow for VMAT on the ViewRay system.

Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network

Infrared and visible image fusion aims to synthesize a single fused image that not only contains salient targets and abundant texture details but also facilitates high-level vision tasks. However, the existing fusion algorithms unilaterally focus on the visual quality and statistical metrics of fused images but ignore the demands of high-level vision tasks. To address these challenges, this paper bridges the gap between image fusion and high-level vision tasks and proposes a semantic-aware real-time image fusion network (SeAFusion). On the one hand, we cascade the image fusion module and semantic segmentation module and leverage the semantic loss to guide high-level semantic information to flow back to the image fusion module, which effectively boosts the performance of high-level vision tasks on fused images. On the other hand, we design a gradient residual dense block (GRDB) to enhance the description ability of the fusion network for fine-grained spatial details. Extensive comparative and generalization experiments demonstrate the superiority of our SeAFusion over state-of-the-art alternatives in terms of maintaining pixel intensity distribution and preserving texture detail. More importantly, the performance comparison of various fusion algorithms in task-driven evaluation reveals the natural advantages of our framework in facilitating high-level vision tasks. In addition, the superior running efficiency allows our algorithm to be effortlessly deployed as a real-time pre-processing module for high-level vision tasks. The source code will be released at https://github.com/Linfeng-Tang/SeAFusion.

Suspect glaucoma detection from corneal densitometry supported by machine learning

PurposeTo discriminate suspect glaucomatous from control eyes using corneal densitometry based on the statistical modeling of the pixel intensity distribution of Scheimpflug images. MethodsTwenty-four participants (10 suspect glaucomatous and 14 control eyes) were included in this retrospective study. Corneal biomechanics was assessed with the commercial Scheimpflug camera Corvis ST (Oculus). Sets of 140 images acquired per measurement were exported for further analysis. After corneal segmentation, pixel intensities corresponding to different corneal depths were statistically modeled for each image, from which corneal densitometry in the form of parameters α (brightness) and β (homogeneity) was derived. After data pre-processing, parameters α and β were input to six supervised machine learning algorithms that were trained, tested, and compared. ResultsThere exists a statistically significant difference in α and β parameters between suspect glaucomatous and control eyes (both, P < 0.05/N, Bonferroni). From the implemented supervised machine learning algorithms, the K-nearest neighbors (K-NN) algorithm reached 83.93% accuracy to discriminate between groups only using corneal densitometry parameters (α and β). ConclusionDensitometry of the anterior cornea including epithelium on its own has the potential to serve as a clinical tool for early glaucoma risk assessment.

Detection of Subclinical Keratoconus With a Validated Alternative Method to Corneal Densitometry.

PurposeTo enhance the current standards of subclinical keratoconus screening based on the statistical modeling of the pixel intensity distribution of Scheimpflug images.MethodsScheimpflug corneal tomographies corresponding to 25 corneal meridians of 60 participants were retrospectively collected and divided into three groups: controls (20 eyes), subclinical keratoconus (20 eyes), and clinical keratoconus (20 eyes). Only right eyes were selected. After corneal segmentation, pixel intensities of the stromal tissue were statistically modeled using a Weibull probability density function from which parameter α (pixel brightness) was derived. Further, data were transformed to polar coordinates, smoothed, and interpolated to build a map of the corneal α parameter. The discriminative power of the method was analyzed using receiver operating characteristic curves.ResultsThe proposed platform-independent method achieved a higher performance in discriminating subclinical keratoconus from control eyes (90.0% sensitivity, 95.0% specificity, 0.97 area under the curve [AUC]) than the standard method (Belin–Ambrósio enhanced ectasia display), which uses only corneal morphometry (85.0% sensitivity, 85.0% specificity, 0.80 AUC).ConclusionsAnalysis of light backscatter at the cornea successfully discriminates subclinical keratoconus from control eyes, upgrading the results previously reported in the literature.Translational RelevanceThe proposed methodology has the potential to support clinicians in the detection of keratoconus before showing clinical signs.

Pixel frequency based railroad surface flaw detection using active infrared thermography for Structural Health Monitoring

With rapid increase in operation and development of high-speed trains, inspection of railroad surface flaws has become an important aspect for safe and reliable operation of rail network. Non-destructive testing using active infrared thermography has been useful in determining the structural health of different structures with additional benefit of robustness in overall inspection system. This study is based on detection of artificial surface flaws on an in-service railroad. Transverse and longitudinal flaws of various dimensions were machined on rough and smooth rail surface. The railroad surface was thermally stimulated to a temperature equivalent to practical conditions. Emitted radiations from rail surface were captured by an infrared camera to detect cracks. Results show a comparison between the surface flaws on rough and smooth rail surface. Subsequently, raw infrared images were post-processed by statistical image improvement to quantitatively analyse the results. Significant change in the frequency distribution of pixel intensity is observed as the flaw size and depth changes giving a clear quantification of crack topology. A comprehensive and inexpensive solution for damage diagnosis will be offered to railway authorities for Structural Health Monitoring (SHM) and NDT by the proposed framework.

Efficient 3D Junction Detection in Biomedical Images Based on a Circular Sampling Model and Reverse Mapping.

Detection and localization of terminations and junctions is a key step in the morphological reconstruction of tree-like structures in images. Previously, a ray-shooting model was proposed to detect termination points automatically. In this paper, we propose an automatic method for 3D junction points detection in biomedical images, relying on a circular sampling model and a 2D-to-3D reverse mapping approach. First, the existing ray-shooting model is improved to a circular sampling model to extract the pixel intensity distribution feature across the potential branches around the point of interest. The computation cost can be reduced dramatically compared to the existing ray-shooting model. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed to detect 2D junction points in maximum intensity projections (MIPs) of sub-volume images in a given 3D image, by determining the number of branches in the candidate junction region. Further, a 2D-to-3D reverse mapping approach is used to map these detected 2D junction points in MIPs to the 3D junction points in the original 3D images. The proposed 3D junction point detection method is implemented as a build-in tool in the Vaa3D platform. Experiments on multiple 2D images and 3D images show average precision and recall rates of 87.11% and 88.33% respectively. In addition, the proposed algorithm is dozens of times faster than the existing deep-learning based model. The proposed method has excellent performance in both detection precision and computation efficiency for junction detection even in large-scale biomedical images.

Improving the Contrast of Pseudothermal Ghost Images Based on the Measured Signal Distribution of Speckle Fields

In this study, we examine the quality of microscale ghost images as a function of the measured histographic signal distribution of the speckle fields from a nonuniform pseudothermal light source. This research shows that the distribution of the detected signal level on each pixel of the camera plays a significant role in improving the contrast-to-noise ratio (CNR) of pseudothermal ghost imaging. To our knowledge, the scaling of CNR with different pixel intensity distributions of the speckle fields is observed for the first time in the field of pseudothermal microscale ghost imaging. The experimental observations are in very good agreement with numerical analysis. Based on these findings, we can predict the settings for light sources that will maximize the CNR of microscale ghost images.

Accurate Liver Border Identification Model in CT Scan Images

The liver is an important organ in human body with certain variations in its edges, color, shape and pixel intensity distribution. These uncertainties may be because of various liver pathologies, hereditary or both. Along with it, liver has close proximity to its nearby organs. Hence, identifying liver in scanned images is a challenging step in image processing. This task becomes more imprecise when liver diseases are present at the edges. The liver segmentation is prerequisite for liver volumetry, computer-based surgery planning, liver surgery modelling, surgery training, 3D view generation, etc. The proposed hybrid segmentation method overcomes the problems and identifies liver boundary in Computed-Tomography (CT) scan images accurately. In this paper, the first step is to study statistics of pixel intensity distribution within liver image, and novel methodology is designed to obtain thresholds. Then, threshold-based segmentation is applied which separates the liver from abdominal CT scan images. In the second step, liver edge is corrected using improved chain code and Bresenham pixel interconnection methods. This provides a precise liver image. The initial points are located inside the liver region without user interventions. These initial points evolve outwardly using Fast Marching Method (FMM), identifying the liver boundary accurately in CT abdominal scan images.

Probability Distribution of Pixel Intensities of EBT3 Films and its Application in the Correction of Uncertainty Budget.

Background and Aim:Modern radiotherapy modalities, such as Intensity-Modulated Radiotherapy and Volumetric Modulated Arc Therapy involve complex dose delivery. The dose delivery is complex as it involves beam modulation, hence, manual dose calculations for these techniques are not possible. Film dosimetry is commonly used method of dose verification for these modalities because of the advantages associated with it. The quantification of uncertainty associated with a film dosimetry system under clinical use becomes important for accurate dosimetry. The spread in the distribution of the pixel values (PV) of the irradiated film contributes to the uncertainty. The probability distribution (PD) of the PV was studied for the clinical photon beam energies of 6, 10, and 15 MV.Methods and Materials:Gafchromic EBT3 film and EPSON 10000XL flatbed scanner were used for this purpose and using the resulting PD, the uncertainty budgets for these energies in the red, green and blue color channels were estimated.Results:The PV of exposed films for the energies studied follows t-distribution, the sum of the squares of the deviation of the measured data from the fitted value was of the order of 10−7, this indicates the goodness of fit. The “t” value corrected combined standard uncertainty (CSU) at 1σ confidence level for exposed film and dose measurement at 200 cGy were 1.42%, 1.48%, and 1.63% and 1.99%, 3.23%, and 5.08% for 6, 10, and 15 MV energies, respectively, in the red colour channel.Conclusion:In the case of the limited number of measurements of a quantity, the SU values must be corrected using the “t” value to get the correct CSU.

Coefficient of variation as an image-intensity metric for cytoskeleton bundling

The evaluation of cytoskeletal bundling is a fundamental experimental method in the field of cell biology. Although the skewness of the pixel intensity distribution derived from fluorescently-labeled cytoskeletons has been widely used as a metric to evaluate the degree of bundling in digital microscopy images, its versatility has not been fully validated. Here, we applied the coefficient of variation (CV) of intensity values as an alternative metric, and compared its performance with skewness. In synthetic images representing extremely bundled conditions, the CV successfully detected degrees of bundling that could not be distinguished by skewness. On actual microscopy images, CV was better than skewness, especially on variable-angle epifluorescence microscopic images or stimulated emission depletion and confocal microscopy images of very small areas of around 1 μm2. When blur or noise was added to synthetic images, CV was found to be robust to blur but deleteriously affected by noise, whereas skewness was robust to noise but deleteriously affected by blur. For confocal images, CV and skewness showed similar sensitivity to noise, possibly because optical blurring is often present in microscopy images. Therefore, in practical use with actual microscopy images, CV may be more appropriate than skewness, unless the image is extremely noisy.

Moth Swarm Algorithm for Image Contrast Enhancement

Image Contrast Enhancement (ICE) is a crucial step in several image processing and computer vision applications. Its main objective is to improve the quality of the visual information contained in the processed images. The presence of noise and small sets of pixels in images are not only irrelevant for their visualization. It also negatively affects the improvement process of ICE schemes since the inclusion of irrelevant information avoids the appropriate distribution of significant pixel intensities in the enhanced image. As a consequence of this effect, most of the proposed ICE methods present different associated problems such as the production of undesirable artifacts, noise amplification, over saturation and bad human visual perception. In this paper, an Image Contrast Enhancement (ICE) method for grayscale and color images is presented. The proposed approach has the propriety of eliminating noisy and irrelevant information in order to improve the distribution capacity of significant pixel intensities in the enhanced image. Our method eliminates multiple groups of a very small number of pixels that, according to their characteristics, do not represents any object or important detail of the image. This process is done by the Mean-shift algorithm, which is used to replace such sets of irrelevant pixels in the original histogram by significant pixel densities represented by local maxima. Then, the Moth Swarm Algorithm (MSA) is used to redistribute the pixel intensities of the reduced histogram so that the value from Kullback–Leibler entropy (KL-entropy) has been maximized. The proposed approach has been tested considering different public datasets commonly used in the literature. Its results are also compared with those produced by other well-known ICE techniques. Evaluation of the experimental results demonstrates that the proposed approach highlights the important details of the image also improving its human visual appearance.

Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

Efficient Critical Point Detection for Curvilinear Structures Using a Ring-Like Ray-Shooting Model

The correct location and identification of critical points, including terminations, three-branch points, and four-branch points, for curvilinear structures in images is a crucial task in many curvilinear structure reconstruction processes. Few methods have been proposed to detect all three types of critical points simultaneously. In this article, we upgraded the existing ray-shooting model to a multifunctional ring-like ray-shooting model that can efficiently detect all types of critical points for curvilinear structures at the same time. First, we measured the minimum and maximum radii of the region of interest based on a sphere-growing method and the modified ray-burst model, respectively. Second, the estimated minimum and maximum radii are used to determine the inner and outer radii of the ring-like ray-shooting model, allowing the proposed model to adaptively extract the pixel intensity distribution feature along the potential branches. Finally, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to detect critical points of curvilinear structures by analyzing the number of clusters of pixel intensity distribution extracted from the ring-like ray-shooting model. The experimental results on multiple data sets confirmed that the proposed method can achieve very accurate detection results efficiently and provide the reconstruction of curvilinear structures with beneficial information.