Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools.
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ABSTRACT:
SUBMITTER: Bulgarevich DS
PROVIDER: S-EPMC6567074 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeThis study aims to provide an automatic strabismus screening method for people who live in remote areas with poor medical accessibility.Materials and methodsThe proposed method first utilizes a pretrained convolutional neural network-based face-detection model and a detector for 68 facial landmarks to extract the eye region for a frontal facial image. Second, Otsu's binarization and the HSV color model are applied to the image to eliminate the influence of eyelashes and canthi. Then, the method samples all of the pixel points on the limbus and applies the least square method to obtain the coordinate of the pupil center. Lastly, we calculated the distances from the pupil center to the medial and lateral canthus to measure the deviation of the positional similarity of two eyes for strabismus screening.ResultWe used a total of 60 frontal facial images (30 strabismus images, 30 normal images) to validate the proposed method. The average value of the iris positional similarity of normal images was smaller than one of the strabismus images via the method (p-value<0.001). The sample mean and sample standard deviation of the positional similarity of the normal and strabismus images were 1.073 ± 0.014 and 0.039, as well as 1.924 ± 0.169 and 0.472, respectively.ConclusionThe experimental results of 60 images show that the proposed method is a promising automatic strabismus screening method for people living in remote areas with poor medical accessibility.
| S-EPMC8330949 | biostudies-literature
Project description:The lifetime of steam pipelines in long-term operation in coal-fired power plants are limited due to material damage that resulted from creep exposure. In the present study, the authors comparatively assess the damage of ex-service 12% Cr piping steel with varying degrees of exposure while using accelerated creep tests that employ digital image correlation (DIC) as well as microstructural investigation that is based on electron microscopy. The DIC technique, which allows multiple creep curves to be measured at temperatures ranging from 550-600 °C from a single specimen, revealed higher Zener-Hollomon parameters for a high damage material with a high void density when compared to a material with lower damage and lower void density. Both of the material states showed similar hardness values, subgrain sizes, and boundary character, despite the difference in void densities. Slightly higher inter-particle spacing of MX precipitates results in a lower threshold stress of 79 MPa for the high damage steel when compared to 97 MPa for the low damage material. Besides large Laves phase particles (>0.2 µm) that are found in the higher damaged materials that result in solid solution depletion, the most prominent microstructural damage indicator was a lower density of M23C6 precipitates. Therefore, the observations indicate that the Zener-Hollomon parameter and M23C6 particles are good damage assessment indicators between the most extreme damage states and they predict a lower damage level for a medium void density material.
| S-EPMC6804267 | biostudies-literature
Project description:The purpose of this study was to develop a computer-aided diagnosis (CAD) system for automatic classification of histopathological images of lung tissues. Two datasets (private and public datasets) were obtained and used for developing and validating CAD. The private dataset consists of 94 histopathological images that were obtained for the following five categories: normal, emphysema, atypical adenomatous hyperplasia, lepidic pattern of adenocarcinoma, and invasive adenocarcinoma. The public dataset consists of 15,000 histopathological images that were obtained for the following three categories: lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. These images were automatically classified using machine learning and two types of image feature extraction: conventional texture analysis (TA) and homology-based image processing (HI). Multiscale analysis was used in the image feature extraction, after which automatic classification was performed using the image features and eight machine learning algorithms. The multicategory accuracy of our CAD system was evaluated in the two datasets. In both the public and private datasets, the CAD system with HI was better than that with TA. It was possible to build an accurate CAD system for lung tissues. HI was more useful for the CAD systems than TA.
| S-EPMC8001245 | biostudies-literature
Project description:Fruit fly courtship behaviors composed of a series of actions have always been an important model for behavioral research. While most related studies have focused only on total courtship behaviors, specific courtship elements have often been underestimated. Identifying these courtship element details is extremely labor intensive and would largely benefit from an automatic recognition system. To address this issue, in this study, we established a vision-based fly courtship behavior recognition system. The system based on the proposed image processing methods can precisely distinguish body parts such as the head, thorax, and abdomen and automatically recognize specific courtship elements, including orientation, singing, attempted copulation, copulation and tapping, which was not detectable in previous studies. This system, which has high identity tracking accuracy (99.99%) and high behavioral element recognition rates (> 97.35%), can ensure correct identification even when flies completely overlap. Using this newly developed system, we investigated the total courtship time, and proportion, and transition of courtship elements in flies across different ages and found that male flies adjusted their courtship strategy in response to their physical condition. We also identified differences in courtship patterns between males with and without successful copulation. Our study therefore demonstrated how image processing methods can be applied to automatically recognize complex animal behaviors. The newly developed system will largely help us investigate the details of fly courtship in future research.
| S-EPMC10943763 | biostudies-literature
Project description:BackgroundCerebral vessel diameter changes objectively and automatically derived from longitudinal magnetic resonance angiography (MRA) facilitate quantification of vessel changes and further modeling.PurposeTo characterize longitudinal changes in intracranial vessel diameter using time-of-flight (TOF) MRA.Study typeRetrospective longitudinal study.Subject populationIN all, 112 pediatric patients, aged 9.96 ± 4.59 years, with craniopharyngioma from 2006-2011 scanned annually.Field strength/sequence1.5T and 3T TOF MRA.Statistical testsChi-square and Wilcoxon-Mann-Whitney tests.AssessmentManual measurements using interventional angiography was established as a reference standard for diameter measurements. Constant and linear quantile regression with absolute difference, percentage difference, and relative difference was used for outlier detection.ResultsMajor vessels surrounding the circle of Willis were successfully segmented except for posterior communicating arteries, mostly due to disease-related hypoplasia. Diameter measurements were calculated at 1-mm segments with a median computed vessel diameter of 1.25 mm. Diameter distortion due to registration was within 0.04 mm for 99% of vessel segments. Outlier detection using quantile regression detected less than 4.34% as being outliers. Outliers were more frequent in smaller vessels and proximity to bifurcations (P < 0.001).Data conclusionUsing the proposed method, objective changes in vessel diameter can be acquired noninvasively from routine longitudinal imaging. High-throughput analyses of imaging-derived vascular trees combined with clinical and treatment parameters will allow rigorous modeling of vessel diameter changes.Level of evidence2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1063-1074.
| S-EPMC8720418 | biostudies-literature
Project description:BackgroundDNA gel electrophoresis is a molecular biology technique for separating different sizes of DNA fragments. Applications of DNA gel electrophoresis include DNA fingerprinting (genetic diagnosis), size estimation of DNA, and DNA separation for Southern blotting. Accurate interpretation of DNA banding patterns from electrophoretic images can be laborious and error prone when a large number of bands are interrogated manually. Although many bio-imaging techniques have been proposed, none of them can fully automate the typing of DNA owing to the complexities of migration patterns typically obtained.ResultsWe developed an image-processing tool that automatically calls genotypes from DNA gel electrophoresis images. The image processing workflow comprises three main steps: 1) lane segmentation, 2) extraction of DNA bands and 3) band genotyping classification. The tool was originally intended to facilitate large-scale genotyping analysis of sugarcane cultivars. We tested the proposed tool on 10 gel images (433 cultivars) obtained from polyacrylamide gel electrophoresis (PAGE) of PCR amplicons for detecting intron length polymorphisms (ILP) on one locus of the sugarcanes. These gel images demonstrated many challenges in automated lane/band segmentation in image processing including lane distortion, band deformity, high degree of noise in the background, and bands that are very close together (doublets). Using the proposed bio-imaging workflow, lanes and DNA bands contained within are properly segmented, even for adjacent bands with aberrant migration that cannot be separated by conventional techniques. The software, called GELect, automatically performs genotype calling on each lane by comparing with an all-banding reference, which was created by clustering the existing bands into the non-redundant set of reference bands. The automated genotype calling results were verified by independent manual typing by molecular biologists.ConclusionsThis work presents an automated genotyping tool from DNA gel electrophoresis images, called GELect, which was written in Java and made available through the imageJ framework. With a novel automated image processing workflow, the tool can accurately segment lanes from a gel matrix, intelligently extract distorted and even doublet bands that are difficult to identify by existing image processing tools. Consequently, genotyping from DNA gel electrophoresis can be performed automatically allowing users to efficiently conduct large scale DNA fingerprinting via DNA gel electrophoresis. The software is freely available from http://www.biotec.or.th/gi/tools/gelect.
| S-EPMC4682448 | biostudies-literature
Project description:BackgroundEndoscopic ultrasound (EBUS) strain elastography can diagnose intrathoracic benign and malignant lymph nodes (LNs) by reflecting the relative stiffness of tissues. Due to strong subjectivity, it is difficult to give full play to the diagnostic efficiency of strain elastography. This study aims to use machine learning to automatically select high-quality and stable representative images from EBUS strain elastography videos.MethodsLNs with qualified strain elastography videos from June 2019 to November 2019 were enrolled in the training and validation sets randomly at a quantity ratio of 3:1 to train an automatic image selection model using machine learning algorithm. The strain elastography videos in December 2019 were used as the test set, from which three representative images were selected for each LN by the model. Meanwhile, three experts and three trainees selected one representative image severally for each LN on the test set. Qualitative grading score and four quantitative methods were used to evaluate images above to assess the performance of the automatic image selection model.ResultsA total of 415 LNs were included in the training and validation sets and 91 LNs in the test set. Result of the qualitative grading score showed that there was no statistical difference between the three images selected by the machine learning model. Coefficient of variation (CV) values of the four quantitative methods in the machine learning group were all lower than the corresponding CV values in the expert and trainee groups, which demonstrated great stability of the machine learning model. Diagnostic performance analysis on the four quantitative methods showed that the diagnostic accuracies were range from 70.33% to 73.63% in the trainee group, 78.02% to 83.52% in the machine learning group, and 80.22% to 82.42% in the expert group. Moreover, there were no statistical differences in corresponding mean values of the four quantitative methods between the machine learning and expert groups (p >0.05).ConclusionThe automatic image selection model established in this study can help select stable and high-quality representative images from EBUS strain elastography videos, which has great potential in the diagnosis of intrathoracic LNs.
| S-EPMC8201408 | biostudies-literature
Project description:Macromolecular structural determination by Electron Microscopy under cryogenic conditions is revolutionizing the field of structural biology, interesting a large community of potential users. Still, the path from raw images to density maps is complex, and sophisticated image processing suites are required in this process, often demanding the installation and understanding of different software packages. Here, we present Scipion Web Tools, a web-based set of tools/workflows derived from the Scipion image processing framework, specially tailored to nonexpert users in need of very precise answers at several key stages of the structural elucidation process.
| S-EPMC5734268 | biostudies-literature
Project description:Automated colony counting methods have long been known in Microbiology. Numerous methods for automated image analysis have been described and a wide range of commercial products exists. Known advantages are saving cost by reducing enumeration time, automatic documentation, reproducibility, and operator independence. Still, even today the realization of all advantages of automated image analysis makes it necessary to either invest in an expensive, high performance commercial system, or to acquire expert knowledge in image processing. This is a considerable obstacle for many laboratories, and the reason why manual colony counting is still done frequently. This article describes an easy to apply automatic colony counting system-including suggestions for sample preparation-that can be put into operation with basic knowledge of image processing and low budget.
| S-EPMC7313745 | biostudies-literature
Project description:BackgroundMicrofluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool.ResultsWe present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks.ConclusionPresented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso.
| S-EPMC5035088 | biostudies-literature