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Panda AK, Verma V, Srivastav A, Badola R, Hussain SA. Digital image processing: A new tool for morphological measurements of freshwater turtles under rehabilitation. PLoS One 2024; 19:e0300253. [PMID: 38484004 PMCID: PMC10939246 DOI: 10.1371/journal.pone.0300253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 02/23/2024] [Indexed: 03/17/2024] Open
Abstract
Freshwater fauna is facing an uphill task for survival in the Ganga Basin, India, due to a range of factors causing habitat degradation and fragmentation, necessitating conservation interventions. As part of the ongoing efforts to conserve the freshwater fauna of the Basin, we are working on rehabilitating rescued freshwater chelonians. We carry out various interventions to restore rescued individuals to an apparent state of fitness for their release in suitable natural habitats. Morphometric measurements are crucial to managing captive wild animals for assessing their growth and well-being. Measurements are made using manual methods like vernier caliper that are prone to observer error experience and require handling the specimens for extended periods. Digital imaging technology is rapidly progressing at a fast pace and with the advancement of technology. We acquired images of turtles using smartphones along with manual morphometric measurements using vernier calipers of the straight carapace length and straight carapace width. The images were subsequently processed using ImageJ, a freeware and compared with manual morphometric measurements. A significant decrease in the time spent in carrying out morphometric measurements was observed in our study. The difference in error in measurements was, however, not significant. A probable cause for this may have been the extensive experience of the personnel carrying out the measurements using vernier caliper. Digital image processing technology can cause a significant reduction in the stress of the animals exposed to handling during measurements, thereby improving their welfare. Additionally, this can be used in the field to carry out morphometric measurements of free-ranging individuals, where it is often difficult to capture individuals, and challenges are faced in obtaining permission to capture specimens.
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Affiliation(s)
- Ashish Kumar Panda
- Ganga Aqualife Conservation and Monitoring Centre, Wildlife Institute of India, Chandrabani, Dehra Dun, Uttarakhand, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Vikas Verma
- Ganga Aqualife Conservation and Monitoring Centre, Wildlife Institute of India, Chandrabani, Dehra Dun, Uttarakhand, India
| | - Anupam Srivastav
- Ganga Aqualife Conservation and Monitoring Centre, Wildlife Institute of India, Chandrabani, Dehra Dun, Uttarakhand, India
| | - Ruchi Badola
- Ganga Aqualife Conservation and Monitoring Centre, Wildlife Institute of India, Chandrabani, Dehra Dun, Uttarakhand, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Syed Ainul Hussain
- Ganga Aqualife Conservation and Monitoring Centre, Wildlife Institute of India, Chandrabani, Dehra Dun, Uttarakhand, India
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Martins GG, Cordelières FP, Colombelli J, D'Antuono R, Golani O, Guiet R, Haase R, Klemm AH, Louveaux M, Paul-Gilloteaux P, Tinevez JY, Miura K. Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists. F1000Res 2021; 10:334. [PMID: 34164115 PMCID: PMC8215561 DOI: 10.12688/f1000research.25485.1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/15/2021] [Indexed: 11/20/2022] Open
Abstract
NEUBIAS, the European Network of Bioimage Analysts, was created in 2016 with the goal of improving the communication and the knowledge transfer among the various stakeholders involved in the acquisition, processing and analysis of biological image data, and to promote the establishment and recognition of the profession of Bioimage Analyst. One of the most successful initiatives of the NEUBIAS programme was its series of 15 training schools, which trained over 400 new Bioimage Analysts, coming from over 40 countries. Here we outline the rationale behind the innovative three-level program of the schools, the curriculum, the trainer recruitment and turnover strategy, the outcomes for the community and the career path of analysts, including some success stories. We discuss the future of the materials created during this programme and some of the new initiatives emanating from the community of NEUBIAS-trained analysts, such as the NEUBIAS Academy. Overall, we elaborate on how this training programme played a key role in collectively leveraging Bioimaging and Life Science research by bringing the latest innovations into structured, frequent and intensive training activities, and on why we believe this should become a model to further develop in Life Sciences.
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Affiliation(s)
| | - Fabrice P Cordelières
- Bordeaux Imaging Center (BIC), Université de Bordeaux - US4 INSERM, Bordeaux, France
| | - Julien Colombelli
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Rocco D'Antuono
- Crick Advanced Light Microscopy STP (CALM), The Francis Crick Institute, London, UK
| | - Ofra Golani
- The department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Romain Guiet
- BioImaging and Optics Platform (BIOP), Faculty of Life Sciences (SV), École Polytechnique Fédérale (EPFL), Lausanne, Switzerland
| | - Robert Haase
- DFG Cluster of Excellence "Physics of Life", University of Technology TU, Dresden, Germany
| | - Anna H Klemm
- Science for Life Laboratory BioImage Informatics Facility and Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Marion Louveaux
- BioImage Analysis Unit, Institut Pasteur, Paris, France.,Image Analysis Hub, C2RT Institut Pasteur, Paris, France
| | - Perrine Paul-Gilloteaux
- Université de Nantes, CNRS, INSERM, Nantes, France.,Université de Nantes, CHU Nantes, Inserm, CNRS, SFR Sante, Inserm UMS 016, CNRS UMS3556, Nantes, France
| | | | - Kota Miura
- Nikon Imaging Center, University of Heidelberg, Heidelberg, Germany.,Bioimage Analysis & Research, Heidelberg, Germany
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Schroeder AB, Dobson ETA, Rueden CT, Tomancak P, Jug F, Eliceiri KW. The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis. Protein Sci 2021; 30:234-249. [PMID: 33166005 PMCID: PMC7737784 DOI: 10.1002/pro.3993] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 11/04/2020] [Accepted: 11/06/2020] [Indexed: 12/31/2022]
Abstract
For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.
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Affiliation(s)
- Alexandra B. Schroeder
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell ImagingUniversity of Wisconsin at MadisonMadisonWisconsinUSA
- Morgridge Institute for ResearchMadisonWisconsinUSA
- Department of Medical PhysicsUniversity of Wisconsin at MadisonMadisonWisconsinUSA
| | - Ellen T. A. Dobson
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell ImagingUniversity of Wisconsin at MadisonMadisonWisconsinUSA
| | - Curtis T. Rueden
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell ImagingUniversity of Wisconsin at MadisonMadisonWisconsinUSA
| | - Pavel Tomancak
- Max Planck Institute of Molecular Cell Biology and GeneticsDresdenGermany
- IT4Innovations, VŠB – Technical University of OstravaOstravaCzech Republic
| | - Florian Jug
- Max Planck Institute of Molecular Cell Biology and GeneticsDresdenGermany
- Center for Systems Biology DresdenDresdenGermany
- Fondazione Human TechnopoleMilanItaly
| | - Kevin W. Eliceiri
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell ImagingUniversity of Wisconsin at MadisonMadisonWisconsinUSA
- Morgridge Institute for ResearchMadisonWisconsinUSA
- Department of Medical PhysicsUniversity of Wisconsin at MadisonMadisonWisconsinUSA
- Department of Biomedical EngineeringUniversity of Wisconsin at MadisonMadisonWisconsinUSA
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Domínguez C, Heras J, Pascual V. IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine. Comput Biol Med 2017; 84:189-194. [PMID: 28390286 DOI: 10.1016/j.compbiomed.2017.03.027] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 03/28/2017] [Accepted: 03/28/2017] [Indexed: 12/27/2022]
Abstract
BACKGROUND AND OBJECTIVE The effective processing of biomedical images usually requires the interoperability of diverse software tools that have different aims but are complementary. The goal of this work is to develop a bridge to connect two of those tools: ImageJ, a program for image analysis in life sciences, and OpenCV, a computer vision and machine learning library. METHODS Based on a thorough analysis of ImageJ and OpenCV, we detected the features of these systems that could be enhanced, and developed a library to combine both tools, taking advantage of the strengths of each system. The library was implemented on top of the SciJava converter framework. We also provide a methodology to use this library. RESULTS We have developed the publicly available library IJ-OpenCV that can be employed to create applications combining features from both ImageJ and OpenCV. From the perspective of ImageJ developers, they can use IJ-OpenCV to easily create plugins that use any functionality provided by the OpenCV library and explore different alternatives. From the perspective of OpenCV developers, this library provides a link to the ImageJ graphical user interface and all its features to handle regions of interest. CONCLUSIONS The IJ-OpenCV library bridges the gap between ImageJ and OpenCV, allowing the connection and the cooperation of these two systems.
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Affiliation(s)
- César Domínguez
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain
| | - Jónathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain.
| | - Vico Pascual
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain.
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Holzinger A, Malle B, Bloice M, Wiltgen M, Ferri M, Stanganelli I, Hofmann-Wellenhof R. On the Generation of Point Cloud Data Sets: Step One in the Knowledge Discovery Process. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/978-3-662-43968-5_4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
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Stoma S, Fröhlich M, Gerber S, Klipp E. STSE: Spatio-Temporal Simulation Environment Dedicated to Biology. BMC Bioinformatics 2011; 12:126. [PMID: 21527030 PMCID: PMC3114743 DOI: 10.1186/1471-2105-12-126] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2010] [Accepted: 04/28/2011] [Indexed: 11/17/2022] Open
Abstract
Background Recently, the availability of high-resolution microscopy together with the advancements in the development of biomarkers as reporters of biomolecular interactions increased the importance of imaging methods in molecular cell biology. These techniques enable the investigation of cellular characteristics like volume, size and geometry as well as volume and geometry of intracellular compartments, and the amount of existing proteins in a spatially resolved manner. Such detailed investigations opened up many new areas of research in the study of spatial, complex and dynamic cellular systems. One of the crucial challenges for the study of such systems is the design of a well stuctured and optimized workflow to provide a systematic and efficient hypothesis verification. Computer Science can efficiently address this task by providing software that facilitates handling, analysis, and evaluation of biological data to the benefit of experimenters and modelers. Results The Spatio-Temporal Simulation Environment (STSE) is a set of open-source tools provided to conduct spatio-temporal simulations in discrete structures based on microscopy images. The framework contains modules to digitize, represent, analyze, and mathematically model spatial distributions of biochemical species. Graphical user interface (GUI) tools provided with the software enable meshing of the simulation space based on the Voronoi concept. In addition, it supports to automatically acquire spatial information to the mesh from the images based on pixel luminosity (e.g. corresponding to molecular levels from microscopy images). STSE is freely available either as a stand-alone version or included in the linux live distribution Systems Biology Operational Software (SB.OS) and can be downloaded from http://www.stse-software.org/. The Python source code as well as a comprehensive user manual and video tutorials are also offered to the research community. We discuss main concepts of the STSE design and workflow. We demonstrate it's usefulness using the example of a signaling cascade leading to formation of a morphological gradient of Fus3 within the cytoplasm of the mating yeast cell Saccharomyces cerevisiae. Conclusions STSE is an efficient and powerful novel platform, designed for computational handling and evaluation of microscopic images. It allows for an uninterrupted workflow including digitization, representation, analysis, and mathematical modeling. By providing the means to relate the simulation to the image data it allows for systematic, image driven model validation or rejection. STSE can be scripted and extended using the Python language. STSE should be considered rather as an API together with workflow guidelines and a collection of GUI tools than a stand alone application. The priority of the project is to provide an easy and intuitive way of extending and customizing software using the Python language.
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Affiliation(s)
- Szymon Stoma
- Humboldt-Universität zu Berlin, Department of Theoretical Biophysics, Berlin, Germany.
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Smal I, Loog M, Niessen W, Meijering E. Quantitative comparison of spot detection methods in fluorescence microscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2010; 29:282-301. [PMID: 19556194 DOI: 10.1109/tmi.2009.2025127] [Citation(s) in RCA: 114] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Quantitative analysis of biological image data generally involves the detection of many subresolution spots. Especially in live cell imaging, for which fluorescence microscopy is often used, the signal-to-noise ratio (SNR) can be extremely low, making automated spot detection a very challenging task. In the past, many methods have been proposed to perform this task, but a thorough quantitative evaluation and comparison of these methods is lacking in the literature. In this paper, we evaluate the performance of the most frequently used detection methods for this purpose. These include seven unsupervised and two supervised methods. We perform experiments on synthetic images of three different types, for which the ground truth was available, as well as on real image data sets acquired for two different biological studies, for which we obtained expert manual annotations to compare with. The results from both types of experiments suggest that for very low SNRs ( approximately 2), the supervised (machine learning) methods perform best overall. Of the unsupervised methods, the detectors based on the so-called h -dome transform from mathematical morphology or the multiscale variance-stabilizing transform perform comparably, and have the advantage that they do not require a cumbersome learning stage. At high SNRs ( > 5), the difference in performance of all considered detectors becomes negligible.
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Affiliation(s)
- Ihor Smal
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands.
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Díaz-Visurraga J, García A, Cárdenas G. Lethal effect of chitosan-Ag (I) films on Staphylococcus aureus as evaluated by electron microscopy. J Appl Microbiol 2009; 108:633-46. [PMID: 19664066 DOI: 10.1111/j.1365-2672.2009.04447.x] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
AIM This article investigated the lethal effect and morphological changes on Staphylococcus aureus strains ATCC 25923 and ATCC 6538P produced by chitosan-Ag (I) films as observed by electron microscopy. METHODS AND RESULTS The antimicrobial activity of films against staphylococci was determined using the broth dilution method and agar diffusion test. Killing curves, transmission and scanning electron microscopy (TEM and SEM) techniques were employed to evaluate the bacterial death and morphological changes in bacterial cells after exposure to chitosan-Ag (I) films. Films affected the cell structure of Staph. aureus, causing elongation of cells, disaggregation of grape-like cluster, contraction of bacterial cytoplasm, thickening of cell wall, increase in cell wall roughness, cell disruption with loss of intracellular material, filamentation and bacteriolysis, as seen in the micrographs following 3, 6, 12 and 16 h of incubation. CONCLUSIONS Obtained images clearly show that chitosan-Ag (I) films have a notable antistaphylococcal activity. SIGNIFICANCE AND IMPACT OF THE STUDY Information from this study can be employed in guiding future strategies to improve the design of materials for the food industry packaging.
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Affiliation(s)
- J Díaz-Visurraga
- CIPA-Chile, Faculty of Chemistry Sciences, Department of Polymers, Advanced Materials Laboratory, University of Concepcion, Concepcion, Chile
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Gebäck T, Koumoutsakos P. Edge detection in microscopy images using curvelets. BMC Bioinformatics 2009; 10:75. [PMID: 19257905 PMCID: PMC2663783 DOI: 10.1186/1471-2105-10-75] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2008] [Accepted: 03/03/2009] [Indexed: 11/22/2022] Open
Abstract
Background Despite significant progress in imaging technologies, the efficient detection of edges and elongated features in images of intracellular and multicellular structures acquired using light or electron microscopy is a challenging and time consuming task in many laboratories. Results We present a novel method, based on the discrete curvelet transform, to extract a directional field from the image that indicates the location and direction of the edges. This directional field is then processed using the non-maximal suppression and thresholding steps of the Canny algorithm to trace along the edges and mark them. Optionally, the edges may then be extended along the directions given by the curvelets to provide a more connected edge map. We compare our scheme to the Canny edge detector and an edge detector based on Gabor filters, and show that our scheme performs better in detecting larger, elongated structures possibly composed of several step or ridge edges. Conclusion The proposed curvelet based edge detection is a novel and competitive approach for imaging problems. We expect that the methodology and the accompanying software will facilitate and improve edge detection in images available using light or electron microscopy.
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Affiliation(s)
- Tobias Gebäck
- Computational Science, ETH Zürich, Universitätstrasse 6, CAB H69,2, ETH Zürich, CH-8092 Zürich, Switzerland.
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