Статті в журналах: "Image analysis – Software"

1

Smith, Tony. "Image Measurement Analysis Software." Anti-Corrosion Methods and Materials 41, no. 4 (April 1994): 19. http://dx.doi.org/10.1108/eb007345.

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2

Costa, Mirian Cristina Gomes, Isabela Maria de Lima Cunha, Lúcio André de Castro Jorge, and Isabel Cristina da Silva Araújo. "Public-domain software for root image analysis." Revista Brasileira de Ciência do Solo 38, no. 5 (October 2014): 1359–66. http://dx.doi.org/10.1590/s0100-06832014000500001.

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In the search for high efficiency in root studies, computational systems have been developed to analyze digital images. ImageJ and Safira are public-domain systems that may be used for image analysis of washed roots. However, differences in root properties measured using ImageJ and Safira are supposed. This study compared values of root length and surface area obtained with public-domain systems with values obtained by a reference method. Root samples were collected in a banana plantation in an area of a shallower Typic Carbonatic Haplic Cambisol (CXk), and an area of a deeper Typic Haplic Ta Eutrophic Cambisol (CXve), at six depths in five replications. Root images were digitized and the systems ImageJ and Safira used to determine root length and surface area. The line-intersect method modified by Tennant was used as reference; values of root length and surface area measured with the different systems were analyzed by Pearson's correlation coefficient and compared by the confidence interval and t-test. Both systems ImageJ and Safira had positive correlation coefficients with the reference method for root length and surface area data in CXk and CXve. The correlation coefficient ranged from 0.54 to 0.80, with lowest value observed for ImageJ in the measurement of surface area of roots sampled in CXve. The IC (95 %) revealed that root length measurements with Safira did not differ from that with the reference method in CXk (-77.3 to 244.0 mm). Regarding surface area measurements, Safira did not differ from the reference method for samples collected in CXk (-530.6 to 565.8 mm²) as well as in CXve (-4231 to 612.1 mm²). However, measurements with ImageJ were different from those obtained by the reference method, underestimating length and surface area in samples collected in CXk and CXve. Both ImageJ and Safira allow an identification of increases or decreases in root length and surface area. However, Safira results for root length and surface area are closer to the results obtained with the reference method.

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3

Price, David H. "Software: Image Analysis Is Everything." Analytical Chemistry 68, no. 9 (May 1996): 318A—319A. http://dx.doi.org/10.1021/ac961916k.

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4

Legland, David, and Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab." F1000Research 10 (April 30, 2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

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Passoni, Sabrina, Fernando da Silva Borges, Luiz Fernando Pires, Sérgio da Costa Saab, and Miguel Cooper. "Software Image J to study soil pore distribution." Ciência e Agrotecnologia 38, no. 2 (April 2014): 122–28. http://dx.doi.org/10.1590/s1413-70542014000200003.

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In the soil science, a direct method that allows the study of soil pore distribution is the bi-dimensional (2D) digital image analysis. Such technique provides quantitative results of soil pore shape, number and size. The use of specific softwares for the treatment and processing of images allows a fast and efficient method to quantify the soil porous system. However, due to the high cost of commercial softwares, public ones can be an interesting alternative for soil structure analysis. The objective of this work was to evaluate the quality of data provided by the Image J software (public domain) used to characterize the voids of two soils, characterized as Geric Ferralsol and Rhodic Ferralsol, from the southeast region of Brazil. The pore distribution analysis technique from impregnated soil blocks was utilized for this purpose. The 2D image acquisition was carried out by using a CCD camera coupled to a conventional optical microscope. After acquisition and treatment of images, they were processed and analyzed by the software Noesis Visilog 5.4® (chosen as the reference program) and ImageJ. The parameters chosen to characterize the soil voids were: shape, number and pore size distribution. For both soils, the results obtained for the image total porosity (%), the total number of pores and the pore size distribution showed that the Image J is a suitable software to be applied in the characterization of the soil sample voids impregnated with resin.

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6

Brown, Daniel G. "Image and Spatial Analysis Software Tools." Journal of Forestry 98, no. 6 (June 1, 2000): 53–57. http://dx.doi.org/10.1093/jof/98.6.53.

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Abstract To make the most of remotely sensed images and apply them in natural resource management, foresters and other land managers need computer software systems. The ultimate goal is to extract meaningful information from raw images and communicate it to support effective decisionmaking. The following description of the state-of-the-art capabilities available in selected contemporary software systems is a starting point, but readers should compare all available systems that perform a particular function. In many cases there exist low-cost or free alternatives, some of which are mentioned here.

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7

Lee, Hye-One, Jinsun Kim, and Kibum Kim. "Carbon electrode surface analysis using image analysis software." Journal of Industrial Science and Technology Institute 35, no. 1 (June 30, 2021): 25–29. http://dx.doi.org/10.54726/jisti.35.1.5.

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8

Vokes, Martha S., and Anne E. Carpenter. "CellProfiler: Open-Source Software to Automatically Quantify Images." Microscopy Today 16, no. 5 (September 2008): 38–39. http://dx.doi.org/10.1017/s1551929500061757.

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Researchers often examine samples by eye on the microscope — qualitatively scoring each sample for a particular feature of interest. This approach, while suitable for many experiments, sacrifices quantitative results and a permanent record of the experiment. By contrast, if digital images are collected of each sample, software can be used to quantify features of interest. For small experiments, quantitative analysis is often done manually using interactive programs like Adobe Photoshop©. For the large number of images that can be easily collected with automated microscopes, this approach is tedious and time-consuming. NIH Image/ImageJ (http://rsb.info.nih.gov/ij) allows users comfortable writing in a macro language to automate quantitative image analysis. We have developed Cell- Profiler, a free, open-source software package, designed to enable scientists without prior programming experience to quantify relevant features of samples in large numbers of images automatically, in a modular system suitable for processing hundreds of thousands of images.

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9

Tosi, Sébastien, Lídia Bardia, Maria Jose Filgueira, Alexandre Calon, and Julien Colombelli. "LOBSTER: an environment to design bioimage analysis workflows for large and complex fluorescence microscopy data." Bioinformatics 36, no. 8 (December 20, 2019): 2634–35. http://dx.doi.org/10.1093/bioinformatics/btz945.

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Abstract Summary Open source software such as ImageJ and CellProfiler greatly simplified the quantitative analysis of microscopy images but their applicability is limited by the size, dimensionality and complexity of the images under study. In contrast, software optimized for the needs of specific research projects can overcome these limitations, but they may be harder to find, set up and customize to different needs. Overall, the analysis of large, complex, microscopy images is hence still a critical bottleneck for many Life Scientists. We introduce LOBSTER (Little Objects Segmentation and Tracking Environment), an environment designed to help scientists design and customize image analysis workflows to accurately characterize biological objects from a broad range of fluorescence microscopy images, including large images exceeding workstation main memory. LOBSTER comes with a starting set of over 75 sample image analysis workflows and associated images stemming from state-of-the-art image-based research projects. Availability and implementation LOBSTER requires MATLAB (version ≥ 2015a), MATLAB Image processing toolbox, and MATLAB statistics and machine learning toolbox. Code source, online tutorials, video demonstrations, documentation and sample images are freely available from: https://sebastients.github.io. Supplementary information Supplementary data are available at Bioinformatics online.

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10

Rueff, M., and K. Melchior. "“BILDLIB:” the image analysis software at IPA." Journal of Robotic Systems 2, no. 2 (1985): 179–98. http://dx.doi.org/10.1002/rob.4620020203.

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11

Fernandes, Lorena Cunha, Maira Ribeiro dos Santos, Leonardo Peres da Silva, Thiago Viana Miranda Lima, and Rafael Figueiredo Pohlmann Simões. "Evaluation of Various Free Software Options for Catphan 504 Phantom Analysis." Brazilian Journal of Radiation Sciences 12, no. 1 (March 20, 2024): e2335. http://dx.doi.org/10.15392/2319-0612.2024.2335.

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In computed tomography, image quality tests are important to guarantee a correct medical diagnosis and a better cost and benefit for the patient. Purpose: the purpose of this study is to analyse the images reconstructed with different thorax and bone convolution filters using popular free-use software in the field of medical physics, for the Catphan 504 phantom. Methods: a total of 14 scans were performed using the chest protocol, with convolution filters (FC) FC30, FC35, FC50, FC51, FC52, FC53, FC55, FC56, FC80, FC81, FC83, FC84, FC85, FC86, on the 16-channel Canon Aquilion Lightning CT scanner using the Catphan 504 phantom. Image quality parameters evaluated were: noise, uniformity, linearity of CT numbers, and high spatial resolution with MTF 50% and MTF 10%. The images were evaluated using software such as ImageJ, Script Python, and free software for automatic evaluation of the Catphan 504 phantom, CTQA-cp, SPICE-CT, and Pylinac. Results: the tests carried out with the Catphan 504 phantom were analysed by the software and agreed with each other (with p>0.05), except for Pylinac. The results obtained with Pylinac had a significant difference for the uniformity, slice thickness, and MTF10% tests, this being the code that was the furthest away from the results obtained by the other codes. Conclusions: the ImageJ, Spice-CT, and CTQA-cp software showed consistent results for the tests performed, while Pylinac had limitations in calculating the standard deviation for the noise test and showed significant differences in some tests when compared to the other software.

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12

Gehan, Malia A., Noah Fahlgren, Arash Abbasi, Jeffrey C. Berry, Steven T. Callen, Leonardo Chavez, Andrew N. Doust, et al. "PlantCV v2: Image analysis software for high-throughput plant phenotyping." PeerJ 5 (December 1, 2017): e4088. http://dx.doi.org/10.7717/peerj.4088.

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Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

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13

Stutte, G. W. "Analysis of Video Images Using an Interactive Image Capture and Analysis System." HortScience 25, no. 6 (June 1990): 695–97. http://dx.doi.org/10.21273/hortsci.25.6.695.

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Interactive Image Capture and Analysis System (ICAS) provides for real-time capture of video images using an imaging board and software in a personal computer. Through the use of selective filters on the video input source, images of specific reflective wavelengths are obtained and then analyzed for intensity distribution using interactive software designed for scientific agriculture. Conversion of video cameras into two-dimensional near real-time visual and near infrared (NIR) spectral sensors through the use of filters provides information on the physiological status of the tissue following ICAS analysis. However, caution must be observed to minimize equipment-induced artifacts during image acquisition and analysis.

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14

Thomas, Laurent S. V., Franz Schaefer, and Jochen Gehrig. "Fiji plugins for qualitative image annotations: routine analysis and application to image classification." F1000Research 9 (October 15, 2020): 1248. http://dx.doi.org/10.12688/f1000research.26872.1.

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Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To facilitate and spread the usage of analysis tools, we provide examples of such pipelines, including a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

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15

V, Srujana, Chaithanya P, Ramesh B, Manoranjan S, and Mahesh V. "Crop Analysis Using Image Processing." International Journal of Engineering Technology and Management Sciences 4, no. 3 (May 28, 2020): 9–15. http://dx.doi.org/10.46647/ijetms.2020.v04i03.002.

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To detect the uniqueness and quantities of agriculture product images a new method is proposed using MATLAB software .In this paper we propose a method to increase the contrast level of a image with exponential low pass filter and histogram equalization technique. Next by using region props function we extract the binary features of the image, and then we calculated the number of targets in gray level image. This method can be easily applied in modern agriculture.

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Thomas, Laurent S. V., Franz Schaefer, and Jochen Gehrig. "Fiji plugins for qualitative image annotations: routine analysis and application to image classification." F1000Research 9 (February 12, 2021): 1248. http://dx.doi.org/10.12688/f1000research.26872.2.

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Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To illustrate possible use case of the annotations, and facilitate the analysis of the generated annotations, we provide examples of such pipelines, including data-visualization solutions in Fiji and KNIME, as well as a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

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Sundell, Veli-Matti, Teemu Mäkelä, Alexander Meaney, Touko Kaasalainen, and Sauli Savolainen. "Automated daily quality control analysis for mammography in a multi-unit imaging center." Acta Radiologica 60, no. 2 (May 16, 2018): 140–48. http://dx.doi.org/10.1177/0284185118776502.

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Background The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users. Purpose To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center. Material and Methods An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated. Results The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center. Conclusion Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

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Martin, Darrin P., and Edward P. Rybicki. "Microcomputer-Based Quantification of Maize Streak Virus Symptoms in Zea mays." Phytopathology® 88, no. 5 (May 1998): 422–27. http://dx.doi.org/10.1094/phyto.1998.88.5.422.

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We investigated the use of computer-assisted image analysis techniques for the objective quantification of maize streak virus (MSV) symptoms in Zea mays. We compared independent duplicate evaluations of chlorotic lesion areas occurring on MSV-infected leaves using visual assessment, a commercial image analysis system, and a custom image analysis system employing software developed in our laboratory. Relative to visual assessments of disease severity, computer-assisted image analysis employing both the commercial and custom systems provided significant enhancements in the accuracy and precision of chlorotic area estimations. The commercial image analysis system afforded no significant improvement in precision or accuracy over the custom system. An important advantage of examining images using the custom-written software was that the software permitted a high degree of analysis automation. Digitized images of maize leaves could be automatically analyzed by the custom software five times faster than, and with the same precision and accuracy as, when the same images were analyzed with the commercial software. Because of the flexibility of the image analysis techniques described, they should be applicable to the measurement of symptom severity in other plant host-pathogen combinations.

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Gurevich, Igor, and Vera Yashina. "Descriptive Image Analysis. Foundations and Descriptive Image Algebras." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 11 (October 2019): 1940018. http://dx.doi.org/10.1142/s0218001419400184.

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The paper is devoted to Descriptive Image Analysis (DA) — a leading line of the modern mathematical theory of image analysis. DA is a logically organized set of descriptive methods, mathematical objects, and models and representations aimed at analyzing and evaluating the information represented in the form of images, as well as for automating the extraction from images of knowledge and data needed for intelligent decision-making. The basic idea of DA consists of embedding all processes of analysis (processing, recognition, understanding) of images into an image formalization space and reducing it to (1) construction of models/representations/formalized descriptions of images; (2) construction of models/representations/formalized descriptions of transformations over models and representations of images. We briefly discuss the basic ideas, methodological principles, mathematical methods, objects, and components of DA and the basic results determining the current state of the art in the field. Image algebras (IA) are considered in the context of a unified language for describing mathematical objects and operations used in image analysis (the standard IA by Ritter and the descriptive IA by Gurevich).

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20

Rahayu, Andriyati, Asril pramutadi Andi mustari, and Baliana Amir. "Analisis Image Processing pada Prasasti Teroksidasi Ayam Téas I." PURBAWIDYA: Jurnal Penelitian dan Pengembangan Arkeologi 12, no. 2 (November 29, 2023): 206–15. http://dx.doi.org/10.55981/purbawidya.2023.741.

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The Ayam Téas I inscription is one of the ancient inscriptions in Indonesia. Currently, the condition of the inscription has undergone natural degradation, causing the letters and the written message to become more difficult to read. Among the natural forms of degradation are corrosion and erosion. One method that can be used to address this problem is by utilizing image processing technology in the form of imageJ software. The analysis process involves capturing images using a camera and then processing the images using imageJ software. This software provides a mode that can remove unnecessary colors due to lighting, allowing some of the writings on the Ayam Téas I inscription to become more visible. Keywords: imageJ; prasasti; Ayam Téas I; histogram; grayscale

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21

Chou, Szu-Yuan, Cindy Chan, Yu-Chieh Lee, Tzu-Ning Yu, Chii-Ruey Tzeng, and Chi-Huang Chen. "Evaluation of adenomyosis after gonadotrophin-releasing hormone agonist therapy using ultrasound post-processing imaging: a pilot study." Journal of International Medical Research 48, no. 6 (June 2020): 030006052092005. http://dx.doi.org/10.1177/0300060520920056.

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Objective We explored a method for the quantitative sonographic analysis of myometrial texture using computer-aided image analysis software to assess outcomes following treatment with gonadotrophin-releasing hormone (GnRH) agonist for adenomyosis in women with infertility. Method Data for patients with ultrasound images of the myometrium obtained at Taipei Medical University Hospital from 1 September 2018 to 5 April 5 2019 were analyzed. Only 10 patients with 20 ultrasound images matched the eligibility criteria. The images were divided into pre-treatment (n = 10) and post-treatment images (n = 10) and quantitative grayscale histograms were obtained from the ultrasound images using publicly available ImageJ computer-aided image analysis software. We analyzed the differences between the pre- and post-treatment images using the Mann–Whitney test and compared the results with outcomes assessed by serum CA-125 levels. Results Image analysis of the grayscale histograms revealed significant differences between before and after treatment. The classification of the myometrium pre-treatment and post-treatment was similar using CA-125 and histogram grayscale analysis. Conclusion Computer-aided image analysis of grayscale histograms of the myometrium obtained from ultrasound images is an alternative method for assessing myometrial conditions after GnRH agonist treatment in patients with adenomyosis.

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Pereyra Irujo, Gustavo. "IRimage: open source software for processing images from infrared thermal cameras." PeerJ Computer Science 8 (May 10, 2022): e977. http://dx.doi.org/10.7717/peerj-cs.977.

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IRimage aims at increasing throughput, accuracy and reproducibility of results obtained from thermal images, especially those produced with affordable, consumer-oriented cameras. IRimage processes thermal images, extracting raw data and calculating temperature values with an open and fully documented algorithm, making this data available for further processing using image analysis software. It also allows the making of reproducible measurements of the temperature of objects in a series of images, and produce visual outputs (images and videos) suitable for scientific reporting. IRimage is implemented in a scripting language of the scientific image analysis software ImageJ, allowing its use through a graphical user interface and also allowing for an easy modification or expansion of its functionality. IRimage’s results were consistent with those of standard software for 15 camera models of the most widely used brand. An example use case is also presented, in which IRimage was used to efficiently process hundreds of thermal images to reveal subtle differences in the daily pattern of leaf temperature of plants subjected to different soil water contents. IRimage’s functionalities make it better suited for research purposes than many currently available alternatives, and could contribute to making affordable consumer-grade thermal cameras useful for reproducible research.

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YOSHIMOTO, Motonobu, and Tomomasa UEMURA. "High Performance PTV Software for Sequential Image Analysis." Journal of the Visualization Society of Japan 16, Supplement2 (1996): 31–34. http://dx.doi.org/10.3154/jvs.16.supplement2_31.

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Hedde, Per Niklas, Jochen Fuchs, Franz Oswald, Jörg Wiedenmann, and Gerd Ulrich Nienhaus. "Online image analysis software for photoactivation localization microscopy." Nature Methods 6, no. 10 (October 2009): 689–90. http://dx.doi.org/10.1038/nmeth1009-689.

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Gavoille, A., E. Kahn, J. Bosq, and M. B. Malki. "A User-Oriented Software for Cytological Image Analysis." Pathology - Research and Practice 185, no. 5 (December 1989): 821–24. http://dx.doi.org/10.1016/s0344-0338(89)80247-x.

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Novikov, E., and E. Barillot. "Software package for automatic microarray image analysis (MAIA)." Bioinformatics 23, no. 5 (January 19, 2007): 639–40. http://dx.doi.org/10.1093/bioinformatics/btl644.

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Szczypiński, Piotr M., Michał Strzelecki, Andrzej Materka, and Artur Klepaczko. "MaZda—A software package for image texture analysis." Computer Methods and Programs in Biomedicine 94, no. 1 (April 2009): 66–76. http://dx.doi.org/10.1016/j.cmpb.2008.08.005.

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Mazer, Alan S., Miki Martin, Meemong Lee, and Jerry E. Solomon. "Image processing software for imaging spectrometry data analysis." Remote Sensing of Environment 24, no. 1 (February 1988): 201–10. http://dx.doi.org/10.1016/0034-4257(88)90012-0.

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Tollemar, V., N. Tudzarovski, E. Boberg, A. Törnqvist Andrén, A. Al-Adili, K. Le Blanc, K. Garming Legert, M. Bottai, G. Warfvinge, and R. V. Sugars. "Quantitative chromogenic immunohistochemical image analysis in cellprofiler software." Cytometry Part A 93, no. 10 (August 8, 2018): 1051–59. http://dx.doi.org/10.1002/cyto.a.23575.

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Amin, Ibrahim, and Saad Salman. "Fragmentation Analysis of Blasted Rock using WipFrag Image Analysis Software." Journal of Mines, Metals and Fuels 70, no. 5 (July 22, 2022): 263. http://dx.doi.org/10.18311/jmmf/2022/28875.

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Blasting is an essential and the very first activity of hard rock mining. It is considered the cheapest source of energy for loosening/extracting hard rock. Improper planning and design can make basting a costly operation. Furthermore, as downstream processes are affected by properties of muckpile a blast should be designed properly to yield the desired muckpile and fragmentation. Among fragmentation and muckpile, fragmentation is the most important, and is the main parameter used to evaluate the efficiency of a blast. This paper analyzes the use of WipFrag software to evaluate the fragment size distribution of blasting with current blasting parameters of Cherat Cement quarry.

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Seethepalli, Anand, Haichao Guo, Xiuwei Liu, Marcus Griffiths, Hussien Almtarfi, Zenglu Li, Shuyu Liu, et al. "RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping." Plant Phenomics 2020 (February 15, 2020): 1–15. http://dx.doi.org/10.34133/2020/3074916.

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Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are not optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns. The hardware platform utilizes a backlight and a monochrome machine vision camera to capture root crown silhouettes. The RhizoVision Imager and RhizoVision Analyzer are free, open-source software that streamline image capture and image analysis with intuitive graphical user interfaces. The RhizoVision Analyzer was physically validated using copper wire, and features were extensively validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean (Glycine max) root crowns of 187 lines and 1,753 wheat (Triticum aestivum) root crowns of 186 lines were phenotyped. Principal component analysis indicated similar correlations among features in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating that differences in species and populations need to be considered. The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns and sets a standard by which open plant phenotyping platforms can be benchmarked.

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32

Jarčuška, B., S. Kucbel, and P. Jaloviar. "Comparison of output results from two programmes for hemispherical image analysis: Gap Light Analyser and WinScanopy." Journal of Forest Science 56, No. 4 (May 3, 2010): 147–53. http://dx.doi.org/10.17221/76/2009-jfs.

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We compare the results of the analysis of hemispherical images (of a broadleaved and a coniferous forest) obtained using the Gap Light Analyser (GLA) software and the results obtained by analyzing the same images with the aid of WinScanopy. The two packages were used to calculate relative total, relative diffuse and relative direct transmittance, canopy openness, and leaf area index. Our aim was to find out whether it is possible to compare the studies using different software packages for determining light conditions. The binary pixel classification of images of canopy and sky was performed automatically (in the case of Gap Light Analyser, using the SideLook programme). The threshold values determined by the SideLook programme were lower compared to the WinScanopy, which was also reflected in the evaluated output results. There was a strong positive correlation between the results obtained with the two software packages (R2 ranges from 0.814 to 0.999). However, when the Gap Light Analyser analysis was applied to the threshold values obtained with the SideLook, the output results mostly manifested systematic differences in comparison with the output results obtained using the WinScanopy. Using the same threshold value in both programmes, the differences between the output values were quite small (a minimum of 0.038 m2.m–2 for LAI in the spruce forest and a maximum of 0.738% for total relative transmittance also in the spruce forest). The differences in some characteristics were statistically significant, on the other hand, both the photo series had only the identical direct transmittance values. The observed differences can be explained by differences in the calibration of the used camera-lens pair, different image registration techniques and different theoretical background and models used in the two software packages. It follows that it is also necessary to be aware of possible differences when comparing the outputs of the two compared software packages analyzing photos obtained applying the same methodical approach.

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Gamarra Acosta, Margarita, Evert De los Ríos Trujillo, and José Escorcia-Gutierrez. "A Spiral-Based Methodology Applied to Cell Image Analysis." Revista SEXTANTE 20 (October 31, 2019): 4–11. http://dx.doi.org/10.54606/sextante2019.v20.01.

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The advances in technology, microscopy and computing have allowed the development of new fields in cell image analysis. However, the usability of these platforms is adequate to expert users only. Many software tools are oriented to expert users in image processing, likewise the use of bioinformatics require a basic knowledge in programming. The development of research in cell imaging requires the joint work of computer Scientifics and biologist. In this paper we present a methodology to develop a software solution applied to the analysis of cell images.

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Schroeder, Alexandra B., Ellen T. A. Dobson, Curtis T. Rueden, Pavel Tomancak, Florian Jug, and Kevin W. Eliceiri. "The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis." Protein Science 30, no. 1 (November 20, 2020): 234–49. http://dx.doi.org/10.1002/pro.3993.

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35

Barlow, Andrew L., and Christopher J. Guerin. "Quantization of widefield fluorescence images using structured illumination and image analysis software." Microscopy Research and Technique 70, no. 1 (2006): 76–84. http://dx.doi.org/10.1002/jemt.20389.

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36

Bagni, T., H. Haldi, D. Mauro, and C. Senatore. "Tomography analysis tool: an application for image analysis based on unsupervised machine learning." IOP SciNotes 3, no. 1 (February 24, 2022): 015201. http://dx.doi.org/10.1088/2633-1357/ac54bf.

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Abstract We developed a graphical user interface (GUI) to analyse tomographic images of superconducting Nb3Sn wires designed for the next generation accelerator magnets. The Tomography Analysis Tool (TAT) relies on the k-means algorithm, an unsupervised machine learning technique which is widely used to partition images into separated clusters. The GUI is compatible with both Linux and Windows operating systems. The software reliability was tested by optical inspecting the tomographic images superimposed on the clustered image obtained by the k-means algorithm. TAT was proven to correctly segment the various components of the Nb3Sn superconducting wires with single pixel precision. Finally, this software can be a useful tool for the scientific community to segment and analyse quickly and reproducibly tomographic images.

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37

KITAJIMA, Itaru. "Roles of Software for Profile Measurement. Software Analysis of SPM Topographic Image." Journal of the Japan Society for Precision Engineering 61, no. 8 (1995): 1082–85. http://dx.doi.org/10.2493/jjspe.61.1082.

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Noble, J. Alison, Nassir Navab, and H. Becher. "Ultrasonic image analysis and image-guided interventions." Interface Focus 1, no. 4 (June 15, 2011): 673–85. http://dx.doi.org/10.1098/rsfs.2011.0025.

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The fields of medical image analysis and computer-aided interventions deal with reducing the large volume of digital images (X-ray, computed tomography, magnetic resonance imaging (MRI), positron emission tomography and ultrasound (US)) to more meaningful clinical information using software algorithms. US is a core imaging modality employed in these areas, both in its own right and used in conjunction with the other imaging modalities. It is receiving increased interest owing to the recent introduction of three-dimensional US, significant improvements in US image quality, and better understanding of how to design algorithms which exploit the unique strengths and properties of this real-time imaging modality. This article reviews the current state of art in US image analysis and its application in image-guided interventions. The article concludes by giving a perspective from clinical cardiology which is one of the most advanced areas of clinical application of US image analysis and describing some probable future trends in this important area of ultrasonic imaging research.

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Badanyuk, Igor, Igor Nevliudov, and Dmytro Nikitin. "TOPOLOGICAL IMAGE PROCESSING FOR COMPREHENSIVE DEFECT AND DEVIATION ANALYSIS USING ADAPTIVE BINARISATION." Innovative Technologies and Scientific Solutions for Industries, no. 1 (23) (April 20, 2023): 164–73. http://dx.doi.org/10.30837/itssi.2023.23.164.

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The subject of this article is the preparation for recognition and comparison of real topological images of printed circuit boards (PCBs) using adaptive image binarisation with an "automatic window" (the area for scanning the image "Block size"). The aim of the work is to improve the method of adaptive binarisation for images obtained by technical vision systems by developing an automatic algorithm for detecting the required value of the image binarisation window. Objectives: to analyse the subject area for the analysis of technical images of the topology of the SOE; to describe the finding of the global binarisation threshold using the "Otsu" method; to perform global image binarisation; to calculate the standard deviation of binarisation; to process the results obtained to find the required value of the Block size; to test the developed algorithm in software. Results: an image processing algorithm with automatic adjustment of the "Block size" binarisation window was implemented and tested; software was developed using the proposed algorithm and the performance of global binarisation with an improved method of finding the "Block size" values for scanning an image in processing small elements of the SE topology was compared. This will allow solving the following issues: noise removal – removing noise from the image (noise can occur due to poor scan or photo quality, as well as due to the presence of small spots on the surface of the PCB); image segmentation – dividing the image into separate elements such as contours, zones and text (this process can be automated using image processing software); element detection – finding and separating individual elements such as resistors, capacitors and other components depicted on the topology. Conclusions: according to the results of the work, an algorithm for automatically adjusting the size of the scanning area "Block size" for binarisation of technological images of the SE topology has been developed. The following advantages of this algorithm can be distinguished: automatic finding of the optimal scanning area Block Size; resistance to image noise without the use of smoothing filters; finding details in areas of contrast difference.

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Ross, Brian J., Anthony G. Gualtieri, Frank Fueten, and Paul Budkewitsch. "Hyperspectral image analysis using genetic programming." Applied Soft Computing 5, no. 2 (January 2005): 147–56. http://dx.doi.org/10.1016/j.asoc.2004.06.003.

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Sabarish, R. T., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Median Filtering over Histogram Equalization Technique." CARDIOMETRY, no. 25 (February 14, 2023): 818–24. http://dx.doi.org/10.18137/cardiometry.2022.25.818824.

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Aim: The main goal of this project is image enhancement to improve interpretability or perception of information in images for human viewers and also to provide better input for other automated image processing techniques. Materials and Methods: In this research different sources of lung images collected from Kaggle website were used. Samples were considered as (N=30) for median filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of sample was calculated to be 60.Using SPSS Software and a standard data set,the PSNR was obtained. Both median filter and novel histogram technique image enhancement were implemented on Lung images through Matlab coding and also extracting PSNR values of each image. Then through SPSS software comparison and analysis has been made Results: In an image enhancement of the image processing pathway, novel histogram equalization technique shows the best performance by removing noise to improve PSNR of lung images than median filtering. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and median filtering. The novel histogram equalization technique showed higher results of PSNR (69.6557dB) with (p=0.04) in comparison with median filtering (37.6427dB). Conclusion: Histogram equalization image enhancement technique provides high PSNR values for different sources of lung images than median filtering Technique.

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Song, Hongchang, and Tengfei Li. "Image Data Fusion Algorithm Based on Virtual Reality Technology and Nuke Software and Its Application." Journal of Sensors 2022 (March 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/1569197.

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As an important branch of multisensor information fusion, image fusion is widely used in various fields. As a hot research technology, virtual reality technology can bring different levels of experience to image fusion. At the same time, with the development of existing image processing software, it is conducive to the further analysis and processing of images. In today’s market, image processing technology still faces many problems, and with the advancement of technology, virtual technology is widely used in various fields, so combining virtual technology with images is conducive to improving image processing technology. This article mainly introduces the image fusion algorithm and its application research based on virtual reality technology and Nuke software. This paper first proposes a picture fusion model and a picture fusion system through the analysis of virtual technology and Nuke software and, on this basis, proposes a particle algorithm and a picture edge algorithm. Secondly, the optimal fusion of images is studied on Nuke software, and finally the experimental results are analyzed through image fusion algorithm. Studies have shown that the best image fusion greatly improves the security and privacy of the image, and the difficulty of cracking is as high as 80%. The data in the experimental analysis of the graphic fusion algorithm shows that the execution efficiency and time consumption of the algorithm are greatly shortened, and the time consumption is greatly reduced. The rate is reduced by about 50%, and a good image fusion effect has been achieved.

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Loening, Andreas Markus, and Sanjiv Sam Gambhir. "AMIDE: A Free Software Tool for Multimodality Medical Image Analysis." Molecular Imaging 2, no. 3 (July 1, 2003): 153535002003031. http://dx.doi.org/10.1162/15353500200303133.

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Amide's a Medical Image Data Examiner (AMIDE) has been developed as a user-friendly, open-source software tool for displaying and analyzing multimodality volumetric medical images. Central to the package's abilities to simultaneously display multiple data sets (e.g., PET, CT, MRI) and regions of interest is the on-demand data reslicing implemented within the program. Data sets can be freely shifted, rotated, viewed, and analyzed with the program automatically handling interpolation as needed from the original data. Validation has been performed by comparing the output of AMIDE with that of several existing software packages. AMIDE runs on UNIX, Macintosh OS X, and Microsoft Windows platforms, and it is freely available with source code under the terms of the GNU General Public License.

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Lin, Yujia, and Xin Wang. "Analysis and Influence of Media Degradation Image Propagation Path Based on Image Vision." Scientific Programming 2021 (November 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/6903255.

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With the rapid development of the information age, the efficiency of image information dissemination has been improved and the way of information dissemination has gradually moved from text information to image information. In the process of using equipment to take pictures, because of some objective reasons, the images taken are different from the ideal images taken by the equipment, so the interference brought by these objective factors to the images is eliminated, thus presenting a more realistic image process. In the process of network propagation, degraded images show different characteristics in the network. In the process of propagation, images degenerate again, which makes it difficult for images to be authentic or restored. In this paper, an SIR model is selected from three classical infectious disease models to simulate and reflect the propagation path and influence of degraded images and the influence of degraded images on propagation is evaluated by extracting the moderate and degree distribution of undirected network. In addition, the distribution and integration between nodes are evaluated to distinguish the average road sources. Based on the SIR propagation model, a propagation model of information timeliness is constructed. By describing the update of subjective attitude values of nodes and then defining the probability function of state transition between different nodes, the model has higher fitting and adaptability. Finally, using BA, WS, Facebook, and Sina Weibo as the base map and setting the network environment parameters, based on the SIR model, the propagation of degraded images in different network environments is analyzed and the influence results of degraded images in network propagation are obtained.

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Reyes, Juan Pablo, Marcela Hernández Hoyos, and Dominique Fouchez. "SNIa DETECTION ANALYSIS RESULTS FROM REAL AND SIMULATED IMAGES USING SPECIALIZED SOFTWARE." Revista Mexicana de Astronomía y Astrofísica 60, no. 1 (April 1, 2024): 125–40. http://dx.doi.org/10.22201/ia.01851101p.2024.60.01.10.

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The detection of transient events, Type Ia supernovae in particular, has become an important research subject in today's astronomy. We use as a base tool the software suite for astronomical image processing called LSSTsp and adapt it to assemble a type Ia supernova detection pipe. We study some straightforward changes on the overall pipeline by selecting better quality inputs to perform a coaddition of reference images, we analyze the different residual sources detected on the difference images and, lastly, we build light curves by taking into account the features of detected difference image analysis sources. Finally, we build a catalog of supernova candidates by using a random forest classification, and check the relevance these additions. We reduced the overall source detection density with our changes while finding between 82% and 85% of the present Type Ia supernovae.

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Kurrant, Douglas, Nasim Abdollahi, Muhammad Omer, Pedram Mojabi, Elise Fear, and Joe LoVetri. "MWSegEval—An image analysis toolbox for microwave breast images." SoftwareX 15 (July 2021): 100728. http://dx.doi.org/10.1016/j.softx.2021.100728.

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47

Bessa, Guilherme M., Yeganeh Saffar, Reza Sabbagh, and David S. Nobes. "A Software Tool For Automated Analysis And Characterization Of Raw PIV Images." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 21 (July 8, 2024): 1–6. http://dx.doi.org/10.55037/lxlaser.21st.98.

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The time to process large PIV data sets can be expensive and the resultant velocity fields are a strong function of the quality of the particle images used. For new and even experienced users of PIV, determination of the quality of a particle image data set can be challenging and time consuming especially for a large number of data sets. This is compounded with new high-speed camera systems that are capable of collecting terabytes of data quickly. A software tool is described here that allows the user to make informed decisions on the general quality of the data sets and what pre-processing image data steps are needed. The software provides statistical feedback on such parameters as particle count, particle size, particle intensity for not only a single image but also a complete data set. Using this software allows the user to make informed decisions and generate and document the quality of the data collected. Based on this, a robust PIV processing algorithm can be developed.

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Verma, Sneha. "Comparative Analysis of Image Classification Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (December 31, 2023): 1513–20. http://dx.doi.org/10.22214/ijraset.2023.57662.

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Abstract: Image classification is a hot research topic in today's society and an important direction in the field of image processing research. Image classification is a supervised learning method used to classify images. Paper analyses four common image classification algorithms: convolution neural network, support vector machine, artificial neural network and logistic regression. In the research work, both theoretical and empirical approaches were followed. For the theoretical approach a review of both secondary data as well as data based on results obtained by application on the tools is studied. Secondary data was acquired from the research articles, text books, journals, technical reports, published thesis, websites, e-journals, software tool manuals, conference proceedings and any other research articles published in the related domain. The empirical study was carried out on the set of experiments, using software tools. The results obtained from the experiments were analyzed for the finding of the research. The paper compares the results of these four algorithms when tested on same dataset, in same environment and on same system. Research paper proves that results obtained from theoretical analysis are same as results obtained from experiments. The study found out that best results were given by convolution neural network, followed by support vector machine, artificial neural network and at last logistic regression.

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Wan Anis Mardhiah Wan Mohd Fadlah, Mohd Zulfaezal Che Azemin, Mohd Izzuddin Mohd Tamrin, and Mohamed Jalaldeen Mohamed Razi. "Modelling Intraocular Lens Design Based on Image Analysis." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 1 (October 14, 2023): 67–74. http://dx.doi.org/10.37934/araset.33.1.6774.

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Intraocular lenses (IOLs) are commonly used to treat ocular diseases such as cataracts. The International Organization for Standardization (ISO 11979) recommends examining IOLs using real model eyes with the lenses in situ, although this process can also be simulated in silico with an accurate IOL haptic design. One way to create this design is by using software such as Abaqus, which generates a finite element mesh configuration with a linear element set up and directly tests it in the model eye using the same program. While this process is like those reported in other studies and includes most of the software necessary for generating the result automatically, there is an alternative method for generating the IOL haptic design — microscopic image analysis. The purpose of this study was to evaluate the image quality of IOL using microscopic analysis. Several IOL images were captured using a microscope and the converted into vector images and the steps were repeated after two weeks. The structural changes in the images were measured using the Structural Similarity Index Measure (SSIM). All the tested IOLs have SSIM values greater than 0.7. The greatest value was extracted from the simplest IOL shape design. Our result suggests that image analysis for IOL modelling is a reliable method and best for simple IOL shape design.

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Maurya, Dharmendra Kumar. "HaloJ." International Journal of Toxicology 33, no. 5 (September 2014): 362–66. http://dx.doi.org/10.1177/1091581814549961.

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Although Halo assay is a fast and more economic technique, it is not popular compared to comet assay for the measurement of DNA damage. One of the reasons behind this was nonavailability of suitable user-friendly program. Currently, most of the researchers were analyzing halo images manually using image analysis software (Scion Image or ImageJ). To address this problem, I have developed a semiautomatic halo analysis ImageJ program, HaloJ, and applied in the assessment of DNA damage at the single-cell level. In this article, we have shown that data obtained from the HaloJ program have a very good correlation with the data obtained using comet assay analysis program such as Comet Assay Software Project. To the best of our knowledge, this will be the first program to quantify DNA damage of halo images. This program will be of great use for researchers working on the DNA damage and repair, radiation biology, toxicology, cancer biology, and so on.

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