biblio.bib

% Encoding: UTF-8

% Foundational field calculus and Protelis aggregate programming


@inproceedings{10.1007/978-3-540-88693-8_8,
  author    = {Agrawal, Motilal
               and Konolige, Kurt
               and Blas, Morten Rufus},
  editor    = {Forsyth, David
               and Torr, Philip
               and Zisserman, Andrew},
  title     = {CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching},
  booktitle = {Computer Vision -- ECCV 2008},
  year      = {2008},
  publisher = {Springer Berlin Heidelberg},
  address   = {Berlin, Heidelberg},
  pages     = {102--115},
  abstract  = {We explore the suitability of different feature detectors for the task of image registration, and in particular for visual odometry, using two criteria: stability (persistence across viewpoint change) and accuracy (consistent localization across viewpoint change). In addition to the now-standard SIFT, SURF, FAST, and Harris detectors, we introduce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation.},
  isbn      = {978-3-540-88693-8}
}
@inproceedings{10.1007/978-3-642-15552-9_14,
  author    = {Mair, Elmar
               and Hager, Gregory D.
               and Burschka, Darius
               and Suppa, Michael
               and Hirzinger, Gerhard},
  editor    = {Daniilidis, Kostas
               and Maragos, Petros
               and Paragios, Nikos},
  title     = {Adaptive and Generic Corner Detection Based on the Accelerated Segment Test},
  booktitle = {Computer Vision -- ECCV 2010},
  year      = {2010},
  publisher = {Springer Berlin Heidelberg},
  address   = {Berlin, Heidelberg},
  pages     = {183--196},
  abstract  = {The efficient detection of interesting features is a crucial step for various tasks in Computer Vision. Corners are favored cues due to their two dimensional constraint and fast algorithms to detect them. Recently, a novel corner detection approach, FAST, has been presented which outperforms previous algorithms in both computational performance and repeatability. We will show how the accelerated segment test, which underlies FAST, can be significantly improved by making it more generic while increasing its performance.We do so by finding the optimal decision tree in an extended configuration space, and demonstrating how specialized trees can be combined to yield an adaptive and generic accelerated segment test. The resulting method provides high performance for arbitrary environments and so unlike FAST does not have to be adapted to a specific scene structure. We will also discuss how different test patterns affect the corner response of the accelerated segment test.},
  isbn      = {978-3-642-15552-9}
}

@article{10.1083/jcb.201004104,
  author   = {Linkert, Melissa and Rueden, Curtis T. and Allan, Chris and Burel, Jean-Marie and Moore, Will and Patterson, Andrew and Loranger, Brian and Moore, Josh and Neves, Carlos and MacDonald, Donald and Tarkowska, Aleksandra and Sticco, Caitlin and Hill, Emma and Rossner, Mike and Eliceiri, Kevin W. and Swedlow, Jason R.},
  title    = {{Metadata matters: access to image data in the real world}},
  journal  = {Journal of Cell Biology},
  volume   = {189},
  number   = {5},
  pages    = {777-782},
  year     = {2010},
  month    = {05},
  abstract = {{Data sharing is important in the biological sciences to prevent duplication of effort, to promote scientific integrity, and to facilitate and disseminate scientific discovery. Sharing requires centralized repositories, and submission to and utility of these resources require common data formats. This is particularly challenging for multidimensional microscopy image data, which are acquired from a variety of platforms with a myriad of proprietary file formats (PFFs). In this paper, we describe an open standard format that we have developed for microscopy image data. We call on the community to use open image data standards and to insist that all imaging platforms support these file formats. This will build the foundation for an open image data repository.}},
  issn     = {0021-9525},
  doi      = {10.1083/jcb.201004104},
  url      = {https://doi.org/10.1083/jcb.201004104},
  eprint   = {https://rupress.org/jcb/article-pdf/189/5/777/1346115/jcb\_201004104.pdf}
}

@article{10.1093/bioinformatics/bts543,
  author   = {Pietzsch, Tobias and Preibisch, Stephan and Tomančák, Pavel and Saalfeld, Stephan},
  title    = {{ImgLib2—generic image processing in Java}},
  journal  = {Bioinformatics},
  volume   = {28},
  number   = {22},
  pages    = {3009-3011},
  year     = {2012},
  month    = {09},
  abstract = {{Summary: ImgLib2 is an open-source Java library for n-dimensional data representation and manipulation with focus on image processing. It aims at minimizing code duplication by cleanly separating pixel-algebra, data access and data representation in memory. Algorithms can be implemented for classes of pixel types and generic access patterns by which they become independent of the specific dimensionality, pixel type and data representation. ImgLib2 illustrates that an elegant high-level programming interface can be achieved without sacrificing performance. It provides efficient implementations of common data types, storage layouts and algorithms. It is the data model underlying ImageJ2, the KNIME Image Processing toolbox and an increasing number of Fiji-Plugins.Availability: ImgLib2 is licensed under BSD. Documentation and source code are available at http://imglib2.net and in a public repository at https://github.com/imagej/imglib.Supplementary Information:Supplementary data are available at Bioinformatics Online.Contact: saalfeld@mpi-cbg.de}},
  issn     = {1367-4803},
  doi      = {10.1093/bioinformatics/bts543},
  url      = {https://doi.org/10.1093/bioinformatics/bts543},
  eprint   = {https://academic.oup.com/bioinformatics/article-pdf/28/22/3009/16908600/bts543.pdf}
}

@article{10.1145/1141911.1141920,
  author     = {Schaefer, Scott and McPhail, Travis and Warren, Joe},
  title      = {Image Deformation Using Moving Least Squares},
  year       = {2006},
  issue_date = {July 2006},
  publisher  = {Association for Computing Machinery},
  address    = {New York, NY, USA},
  volume     = {25},
  number     = {3},
  issn       = {0730-0301},
  url        = {https://doi.org/10.1145/1141911.1141920},
  doi        = {10.1145/1141911.1141920},
  abstract   = {We provide an image deformation method based on Moving Least Squares using various classes of linear functions including affine, similarity and rigid transformations. These deformations are realistic and give the user the impression of manipulating real-world objects. We also allow the user to specify the deformations using either sets of points or line segments, the later useful for controlling curves and profiles present in the image. For each of these techniques, we provide simple closed-form solutions that yield fast deformations, which can be performed in real-time.},
  journal    = {ACM Trans. Graph.},
  month      = {jul},
  pages      = {533–540},
  numpages   = {8},
  keywords   = {rigid transformations, deformations, moving least squares}
}

@article{10.1371/journal.pone.0038011,
  doi       = {10.1371/journal.pone.0038011},
  author    = {Cardona, Albert AND Saalfeld, Stephan AND Schindelin, Johannes AND Arganda-Carreras, Ignacio AND Preibisch, Stephan AND Longair, Mark AND Tomancak, Pavel AND Hartenstein, Volker AND Douglas, Rodney J.},
  journal   = {PLOS ONE},
  publisher = {Public Library of Science},
  title     = {TrakEM2 Software for Neural Circuit Reconstruction},
  year      = {2012},
  month     = {06},
  volume    = {7},
  url       = {https://doi.org/10.1371/journal.pone.0038011},
  pages     = {1-8},
  abstract  = {A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.},
  number    = {6}
}

@article{5338015,
  author  = {Klein, Stefan and Staring, Marius and Murphy, Keelin and Viergever, Max A. and Pluim, Josien P. W.},
  journal = {IEEE Transactions on Medical Imaging},
  title   = {<emphasis emphasistype="mono">elastix</emphasis>: A Toolbox for Intensity-Based Medical Image Registration},
  year    = {2010},
  volume  = {29},
  number  = {1},
  pages   = {196-205},
  doi     = {10.1109/TMI.2009.2035616}
}

@inproceedings{6126542,
  author    = {Leutenegger, Stefan and Chli, Margarita and Siegwart, Roland Y.},
  booktitle = {2011 International Conference on Computer Vision},
  title     = {BRISK: Binary Robust invariant scalable keypoints},
  year      = {2011},
  volume    = {},
  number    = {},
  pages     = {2548-2555},
  doi       = {10.1109/ICCV.2011.6126542}
}
  
@inproceedings{6247715,
  author    = {Alahi, Alexandre and Ortiz, Raphael and Vandergheynst, Pierre},
  booktitle = {2012 IEEE Conference on Computer Vision and Pattern Recognition},
  title     = {FREAK: Fast Retina Keypoint},
  year      = {2012},
  volume    = {},
  number    = {},
  pages     = {510-517},
  doi       = {10.1109/CVPR.2012.6247715}
}

@inproceedings{7493463,
  author    = {Bogovic, John A. and Hanslovsky, Philipp and Wong, Allan and Saalfeld, Stephan},
  booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)},
  title     = {Robust registration of calcium images by learned contrast synthesis},
  year      = {2016},
  volume    = {},
  number    = {},
  pages     = {1123-1126},
  doi       = {10.1109/ISBI.2016.7493463}
}

@inproceedings{7493463,
  author    = {Bogovic, John A. and Hanslovsky, Philipp and Wong, Allan and Saalfeld, Stephan},
  booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)},
  title     = {Robust registration of calcium images by learned contrast synthesis},
  year      = {2016},
  volume    = {},
  number    = {},
  pages     = {1123-1126},
  doi       = {10.1109/ISBI.2016.7493463}
}

@article{9058666,
  author    = {Borovec, Jiří and Kybic, Jan and Arganda-Carreras, Ignacio and Sorokin, Dmitry V. and Bueno, Gloria and Khvostikov, Alexander V. and Bakas, Spyridon and Chang, Eric I-Chao and Heldmann, Stefan and Kartasalo, Kimmo and Latonen, Leena and Lotz, Johannes and Noga, Michelle and Pati, Sarthak and Punithakumar, Kumaradevan and Ruusuvuori, Pekka and Skalski, Andrzej and Tahmasebi, Nazanin and Valkonen, Masi and Venet, Ludovic and Wang, Yizhe and Weiss, Nick and Wodzinski, Marek and Xiang, Yu and Xu, Yan and Yan, Yan and Yushkevich, Paul and Zhao, Shengyu and Muñoz-Barrutia, Arrate},
  journal   = {IEEE Transactions on Medical Imaging},
  title     = {ANHIR: Automatic Non-Rigid Histological Image Registration Challenge},
  volume    = {39},
  number    = {10},
  pages     = {3042-3052},
  year      = {2020},
  doi       = {10.1109/TMI.2020.2986331},
  abstract  = {Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98\% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44\% of the image diagonal. The challenge remains open to submissions and all images are available for download.},
  keywords  = {NHIR;histology; image registration; microscopy;},
  issn      = {1558-254X},
  timestamp = {Sab, 24 Oct 2020}
}

@article{Allan2012,
  author   = {Allan, Chris
              and Burel, Jean-Marie
              and Moore, Josh
              and Blackburn, Colin
              and Linkert, Melissa
              and Loynton, Scott
              and MacDonald, Donald
              and Moore, William J.
              and Neves, Carlos
              and Patterson, Andrew
              and Porter, Michael
              and Tarkowska, Aleksandra
              and Loranger, Brian
              and Avondo, Jerome
              and Lagerstedt, Ingvar
              and Lianas, Luca
              and Leo, Simone
              and Hands, Katherine
              and Hay, Ron T.
              and Patwardhan, Ardan
              and Best, Christoph
              and Kleywegt, Gerard J.
              and Zanetti, Gianluigi
              and Swedlow, Jason R.},
  title    = {OMERO: flexible, model-driven data management for experimental biology},
  journal  = {Nature Methods},
  year     = {2012},
  month    = {Mar},
  day      = {01},
  volume   = {9},
  number   = {3},
  pages    = {245-253},
  abstract = {The Open Microscopy Environment Remote Objects (OMERO) software platform provides a server-based system for managing and analyzing microscopy images and non-image data.},
  issn     = {1548-7105},
  doi      = {10.1038/nmeth.1896},
  url      = {https://doi.org/10.1038/nmeth.1896}
}

@article{Alturkistani2015-bz,
  title     = {Histological stains: A literature review and case study},
  author    = {Alturkistani, Hani A and Tashkandi, Faris M and Mohammedsaleh,
               Zuhair M},
  abstract  = {The history of histology indicates that there have been
               significant changes in the techniques used for histological
               staining through chemical, molecular biology assays and
               immunological techniques, collectively referred to as
               histochemistry. Early histologists used the readily available
               chemicals to prepare tissues for microscopic studies; these
               laboratory chemicals were potassium dichromate, alcohol and the
               mercuric chloride to harden cellular tissues. Staining
               techniques used were carmine, silver nitrate, Giemsa, Trichrome
               Stains, Gram Stain and Hematoxylin among others. The purpose of
               this research was to assess past and current literature reviews,
               as well as case studies, with the aim of informing ways in which
               histological stains have been improved in the modern age.
               Results from the literature review has indicated that there has
               been an improvement in histopathology and histotechnology in
               stains used. There has been a rising need for efficient,
               accurate and less complex staining procedures. Many stain
               procedures are still in use today, and many others have been
               replaced with new immunostaining, molecular, non-culture and
               other advanced staining techniques. Some staining methods have
               been abandoned because the chemicals required have been
               medically proven to be toxic. The case studies indicated that in
               modern histology a combination of different stain techniques are
               used to enhance the effectiveness of the staining process.
               Currently, improved histological stains, have been modified and
               combined with other stains to improve their effectiveness.},
  journal   = {Glob. J. Health Sci.},
  publisher = {Canadian Center of Science and Education},
  volume    = 8,
  number    = 3,
  pages     = {72--79},
  month     = Jun,
  year      = 2015,
  language  = {en}
}

@phdthesis{amslaurea19123,
  title       = {Studio e realizzazione di un plugin per l'allineamento di immagini microscopiche},
  author      = {Stefano Belli},
  institution = {UniBo},
  year        = {2019},
  keywords    = {Istologia,Microscopia,Immunoistichimica Sequenziale,Allineamento roto-traslativo,ImageJ Plugin},
  abstract    = {L?immunoistochimica {\`e} una tecnica utilizzata dall?anatomo-patologo per chiarire la natura delle strutture cellulari, laddove la pura morfologia risulti insuf?ciente. Una delle sue applicazioni pi{\`u} diffuse {\`e} rappresentata dall?identi?cazione della natura di tumori maligni indifferenziati, nella caratterizzazione di neoplasie, nell?individuazione dell?origine di una metastasi e nell?identi?cazione di agenti microbici.
                 
                 Questa operazione di analisi prevede l'attuazione di diverse fasi di colorazione del campione tramite marcatori specifici al fine di evidenziarne i principali punti di interesse; tuttavia, queste colorazioni possono comportare deformazioni elastiche o permanenti sul campione, rendendo pi{\`u} difficoltosa la loro successiva sequenziazione e comparazione. L'operazione {\`e} inoltre complicata dalla frequente presenza di immagini di dimensione non uniforme.
                 
                 Ad oggi non esiste un software gold standard per il riallineamento di immagini microscopiche bidimensionali e questo progetto di tesi mira a colmare questa lacuna, proponendo un applicativo user-friendly per permettere a medici e biologi di allineare immagini di provini istologici, provenienti da pazienti, al ?ne di valutare la cross-correlazione tra differenti marcatori tumorali.
                 
                 In particolare, il progetto `e stato sviluppato all?interno del gruppo di ricerca ?Data Science for Health? (DS4H), attivo nel proporre metodi ed applicativi per risolvere problemi aperti nel mondo della salute e sanit{\`a} ed {\`e} stato svolto in collaborazione con l?Istituto Scienti?co Romagnolo per lo Studio e la cura dei Tumori (IRST) di Meldola (FC).
                 
                 L'applicativo, chiamato "DS4H Image Alignment" e sviluppato come Plugin ImageJ, permette l'importazione e la sovvraposizione di immagini di uno stesso tessuto tramite algoritmi di co-registrazione Landmark-based, applicando trasformazioni Rigide e Traslazionali calcolate tramite minimizzazione ai minimi quadrati (MLS).},
  url         = {http://amslaurea.unibo.it/19123/}
}

@article{BAY2008346,
  title    = {Speeded-Up Robust Features (SURF)},
  journal  = {Computer Vision and Image Understanding},
  volume   = {110},
  number   = {3},
  pages    = {346-359},
  year     = {2008},
  note     = {Similarity Matching in Computer Vision and Multimedia},
  issn     = {1077-3142},
  doi      = {https://doi.org/10.1016/j.cviu.2007.09.014},
  url      = {https://www.sciencedirect.com/science/article/pii/S1077314207001555},
  author   = {Herbert Bay and Andreas Ess and Tinne Tuytelaars and Luc {Van Gool}},
  keywords = {Interest points, Local features, Feature description, Camera calibration, Object recognition},
  abstract = {This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters. We conclude the article with SURF’s application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF’s usefulness in a broad range of topics in computer vision.}
}


@article{Bulgarelli2019-oa,
  title     = {Dendritic cell vaccination in metastatic melanoma turns ``non-T
               cell inflamed'' into ``T-cell inflamed'' tumors},
  author    = {Bulgarelli, Jenny and Tazzari, Marcella and Granato, Anna Maria
               and Ridolfi, Laura and Maiocchi, Serena and de Rosa, Francesco
               and Petrini, Massimiliano and Pancisi, Elena and Gentili,
               Giorgia and Vergani, Barbara and Piccinini, Filippo and
               Carbonaro, Antonella and Leone, Biagio Eugenio and Foschi,
               Giovanni and Ancarani, Valentina and Framarini, Massimo and
               Guidoboni, Massimo},
  abstract  = {Dendritic cell (DC)-based vaccination effectively induces
               anti-tumor immunity, although in the majority of cases this does
               not translate into a durable clinical response. However, DC
               vaccination is characterized by a robust safety profile, making
               this treatment a potential candidate for effective combination
               cancer immunotherapy. To explore this possibility, understanding
               changes occurring in the tumor microenvironment (TME) upon DC
               vaccination is required. In this line, quantitative and
               qualitative changes in tumor-infiltrating T lymphocytes (TILs)
               induced by vaccination with autologous tumor lysate/homogenate
               loaded DCs were investigated in a series of 16 patients with
               metastatic melanoma. Immunohistochemistry for CD4, CD8, Foxp3,
               Granzyme B (GZMB), PDL1, and HLA class I was performed in tumor
               biopsies collected before and after DC vaccination. The density
               of each marker was quantified by automated digital pathology
               analysis on whole slide images. Co-expression of markers
               defining functional phenotypes, i.e., Foxp3+ regulatory CD4+ T
               cells (Treg) and GZMB+ cytotoxic CD8+ T cells, was assessed with
               sequential immunohistochemistry. A significant increase of CD8+
               TILs was found in post-vaccine biopsies of patients who were not
               previously treated with immune-modulating cytokines or
               Ipilimumab. Interestingly, along with a maintained tumoral HLA
               class I expression, after DC vaccination we observed a
               significant increase of PDL1+ tumor cells, which significantly
               correlated with intratumoral CD8+ T cell density. This
               observation might explain the lack of a significant concurrent
               cytotoxic reactivation of CD8+ T cell, as measured by the
               numbers of GZMB+ T cells. Altogether these findings indicate
               that DC vaccination exerts an important role in sustaining or de
               novo inducing a T cell inflamed TME. However, the strength of
               the intratumoral T cell activation detected in post-DC therapy
               lesions is lessened by an occurring phenomenon of adaptive
               immune resistance, yet the concomitant PDL1 up-regulation.
               Overall, this study sheds light on DC immunotherapy-induced TME
               changes, lending the rationale for the design of smarter
               immune-combination therapies.},
  journal   = {Front. Immunol.},
  publisher = {Frontiers Media SA},
  volume    = 10,
  pages     = {2353},
  month     = oct,
  year      = 2019,
  keywords  = {PDL1; T cell landscape; dendritic cell vaccine;
               immunomonitoring; immunotherapy; melanoma; tumor
               microenvironment},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  language  = {en}
}

@misc{CheatSheet,
  title        = {Legacy CheatSheet : ImageJ 1.x to ImageJ2 transition},
  howpublished = {\url{https://github.com/mpicbg-scicomp/ij2course-images/blob/master/slides/ij_legacy_cheetsheet.pdf}},
  note         = {Accessed: 2022-04-30}
}

  @article{Hiner2016,
  author   = {Hiner, Mark C.
              and Rueden, Curtis T.
              and Eliceiri, Kevin W.},
  title    = {SCIFIO: an extensible framework to support scientific image formats},
  journal  = {BMC Bioinformatics},
  year     = {2016},
  month    = {Dec},
  day      = {07},
  volume   = {17},
  number   = {1},
  pages    = {521},
  abstract = {No gold standard exists in the world of scientific image acquisition; a proliferation of instruments each with its own proprietary data format has made out-of-the-box sharing of that data nearly impossible. In the field of light microscopy, the Bio-Formats library was designed to translate such proprietary data formats to a common, open-source schema, enabling sharing and reproduction of scientific results. While Bio-Formats has proved successful for microscopy images, the greater scientific community was lacking a domain-independent framework for format translation.},
  issn     = {1471-2105},
  doi      = {10.1186/s12859-016-1383-0},
  url      = {https://doi.org/10.1186/s12859-016-1383-0}
}

@article{https://doi.org/10.1002/mrd.22489,
  author   = {Schindelin, Johannes and Rueden, Curtis T. and Hiner, Mark C. and Eliceiri, Kevin W.},
  title    = {The ImageJ ecosystem: An open platform for biomedical image analysis},
  journal  = {Molecular Reproduction and Development},
  volume   = {82},
  number   = {7-8},
  pages    = {518-529},
  doi      = {https://doi.org/10.1002/mrd.22489},
  url      = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrd.22489},
  eprint   = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrd.22489},
  abstract = {SUMMARY Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more-advanced image processing and analysis techniques. A wide range of software is available—from commercial to academic, special-purpose to Swiss army knife, small to large—but a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open-source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the open-software platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image-processing algorithms. In this review, we use the ImageJ project as a case study of how open-source software fosters its suites of software tools, making multitudes of image-analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is self-influenced by coevolving projects within the ImageJ ecosystem. Mol. Reprod. Dev. 82: 518–529, 2015. © 2015 Wiley Periodicals, Inc.},
  year     = {2015}
}

@article{https://doi.org/10.1111/jmi.12928,
  author   = {ROHDE, FLORENS and BRAUMANN, ULF-DIETRICH and SCHMIDT, MATTHIAS},
  title    = {Correlia: an ImageJ plug-in to co-register and visualise multimodal correlative micrographs},
  journal  = {Journal of Microscopy},
  volume   = {280},
  number   = {1},
  pages    = {3-11},
  keywords = {Correlative microscopy, Correlia, image registration, ImageJ Fiji},
  doi      = {https://doi.org/10.1111/jmi.12928},
  url      = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jmi.12928},
  eprint   = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/jmi.12928},
  abstract = {Summary The correlation of different microscopic imaging techniques alongside with microanalytical methods is crucial to better understand biological processes on a subcellular level. For that, micrographs and chemical maps exhibiting both, very different spatial resolution and field-of-view but also a highly multimodal content has to be co-registered. We developed the ImageJ/Fiji plug-in Correlia that provides an environment for handling multimodal correlative microscopy data. Several linear and nonlinear registration methods using either feature or area-based similarity measures can flexibly be cascaded to align and warp 2D microscopy data sets. The registration of data sets containing light- and electron micrographs as well as chemical maps acquired by secondary-ion mass spectroscopy and energy-dispersive X-ray spectroscopy is demonstrated. Correlia is an open-source tool developed particularly for the registration and analysis of highly multimodal 2D correlative microscopy data. Lay Description If a microscopic object is imaged correlatively by two or more different microscopes the acquired micrographs will have to be overlaid accurately using an image-registration software. In cases of relatively similar image content creating such an overlay is straight-forward but what if the fields-of-view and resolutions of the micrographs differ significantly? What if there are distortions in a micrograph which have to be corrected before creating an overlay? What if furthermore a chemical map shall be overlaid that merely shows regions in which a certain chemical element is present? The rapidly increasing number of applications in correlative microscopy is calling for an easy-to-use and flexible image registration software that can deal with these challenges. Having that in mind, we developed Correlia, an ImageJ/Fiji plug-in that provides an environment for handling multimodal 2D correlative microscopy data-sets. It allows for creating overlays using different registration algorithms that can flexibly be cascaded. In this paper we describe what is happening ‘under the hood’ and give two example data-sets from microbiology which were registered using Correlia. Correlia is open source software and available from www.ufz.de/correlia - including introductory examples, as the authors would like to encourage other scientists to process their individual correlative microscopy data using Correlia.},
  year     = {2020}
}

@misc{IBM_CV,
  title        = {Cos'è la Computer Vision?},
  howpublished = {\url{https://www.ibm.com/it-it/topics/computer-vision}},
  note         = {Accessed: 2022-05-01}
}

  @online{Istologia,
  author = {Enciclopedia Treccani},
  title  = {istologia},
  year   = {2022},
  url    = {https://www.treccani.it/enciclopedia/istologia/}
}

@article{LI201627,
  title    = {Metadata management for high content screening in OMERO},
  journal  = {Methods},
  volume   = {96},
  pages    = {27-32},
  year     = {2016},
  note     = {High-throughput Imaging},
  issn     = {1046-2023},
  doi      = {https://doi.org/10.1016/j.ymeth.2015.10.006},
  url      = {https://www.sciencedirect.com/science/article/pii/S1046202315301250},
  author   = {Simon Li and Sébastien Besson and Colin Blackburn and Mark Carroll and Richard K. Ferguson and Helen Flynn and Kenneth Gillen and Roger Leigh and Dominik Lindner and Melissa Linkert and William J. Moore and Balaji Ramalingam and Emil Rozbicki and Gabriella Rustici and Aleksandra Tarkowska and Petr Walczysko and Eleanor Williams and Chris Allan and Jean-Marie Burel and Josh Moore and Jason R. Swedlow},
  keywords = {Data management, Screening, Metadata, HCS},
  abstract = {High content screening (HCS) experiments create a classic data management challenge—multiple, large sets of heterogeneous structured and unstructured data, that must be integrated and linked to produce a set of “final” results. These different data include images, reagents, protocols, analytic output, and phenotypes, all of which must be stored, linked and made accessible for users, scientists, collaborators and where appropriate the wider community. The OME Consortium has built several open source tools for managing, linking and sharing these different types of data. The OME Data Model is a metadata specification that supports the image data and metadata recorded in HCS experiments. Bio-Formats is a Java library that reads recorded image data and metadata and includes support for several HCS screening systems. OMERO is an enterprise data management application that integrates image data, experimental and analytic metadata and makes them accessible for visualization, mining, sharing and downstream analysis. We discuss how Bio-Formats and OMERO handle these different data types, and how they can be used to integrate, link and share HCS experiments in facilities and public data repositories. OME specifications and software are open source and are available at https://www.openmicroscopy.org.}
}

@article{Liu2018-ds,
  title     = {A {FAST-BRISK} feature detector with depth information},
  author    = {Liu, Yanli and Zhang, Heng and Guo, Hanlei and Xiong, Neal N},
  abstract  = {RGB-D cameras offer both color and depth images of the
               surrounding environment, making them an attractive option for
               robotic and vision applications. This work introduces the
               BRISK\_D algorithm, which efficiently combines Features from
               Accelerated Segment Test (FAST) and Binary Robust Invariant
               Scalable Keypoints (BRISK) methods. In the BRISK\_D algorithm,
               the keypoints are detected by the FAST algorithm and the
               location of the keypoint is refined in the scale and the space.
               The scale factor of the keypoint is directly computed with the
               depth information of the image. In the experiment, we have made
               a detailed comparative analysis of the three algorithms SURF,
               BRISK and BRISK\_D from the aspects of scaling, rotation,
               perspective and blur. The BRISK\_D algorithm combines depth
               information and has good algorithm performance.},
  journal   = {Sensors (Basel)},
  publisher = {MDPI AG},
  volume    = 18,
  number    = 11,
  pages     = {3908},
  month     = nov,
  year      = 2018,
  keywords  = {BRISK (Binary Robust Invariant Scalable Keypoints); depth
               information; rotation invariance; scale factor; scale invariance},
  language  = {en}
}

@article{Lowe2004,
  author   = {Lowe, David G.},
  title    = {Distinctive Image Features from Scale-Invariant Keypoints},
  journal  = {International Journal of Computer Vision},
  year     = {2004},
  month    = Nov,
  day      = {01},
  volume   = {60},
  number   = {2},
  pages    = {91-110},
  abstract = {This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.},
  issn     = {1573-1405},
  doi      = {10.1023/B:VISI.0000029664.99615.94},
  url      = {https://doi.org/10.1023/B:VISI.0000029664.99615.94}
}

@article{MIST,
  author   = {Chalfoun Joe and Majurski Michael and Blattner Tim and Bhadriraju Kiran and Keyrouz Walid and Bajcsy Peter and Brady Mary},
  journal  = {Scientific Reports},
  title    = {MIST: Accurate and Scalable Microscopy Image Stitching Tool with Stage Modeling and Error Minimization},
  volume   = {7},
  number   = {4098},
  url      = {https://doi.org/10.1038/s41598-017-04567-y},
  doi      = {10.1038/s41598-017-04567-y},
  abstract = {Automated microscopy can image specimens larger than the microscope’s field of view (FOV) by stitching overlapping image tiles. It also enables time-lapse studies of entire cell cultures in multiple imaging modalities. We created MIST (Microscopy Image Stitching Tool) for rapid and accurate stitching of large 2D time-lapse mosaics. MIST estimates the mechanical stage model parameters (actuator backlash, and stage repeatability ‘r’) from computed pairwise translations and then minimizes stitching errors by optimizing the translations within a (4r)2 square area. MIST has a performance-oriented implementation utilizing multicore hybrid CPU/GPU computing resources, which can process terabytes of time-lapse multi-channel mosaics 15 to 100 times faster than existing tools. We created 15 reference datasets to quantify MIST’s stitching accuracy. The datasets consist of three preparations of stem cell colonies seeded at low density and imaged with varying overlap (10 to 50\%). The location and size of 1150 colonies are measured to quantify stitching accuracy. MIST generated stitched images with an average centroid distance error that is less than 2\% of a FOV. The sources of these errors include mechanical uncertainties, specimen photobleaching, segmentation, and stitching inaccuracies. MIST produced higher stitching accuracy than three open-source tools. MIST is available in ImageJ at isg.nist.gov.},
  year     = {2017}
}

@article{opencv_library,
  author               = {Bradski, G.},
  citeulike-article-id = {2236121},
  journal              = {Dr. Dobb's Journal of Software Tools},
  keywords             = {bibtex-import},
  posted-at            = {2008-01-15 19:21:54},
  priority             = {4},
  title                = {{The OpenCV Library}},
  year                 = {2000}
}

@article{PaulGilloteaux2017,
  author  = {Paul-Gilloteaux, Perrine
             and Heiligenstein, Xavier
             and Belle, Martin
             and Domart, Marie-Charlotte
             and Larijani, Banafshe
             and Collinson, Lucy
             and Raposo, Gra{\c{c}}a
             and Salamero, Jean},
  title   = {eC-CLEM: flexible multidimensional registration software for correlative microscopies},
  journal = {Nature Methods},
  year    = {2017},
  month   = feb,
  day     = {01},
  volume  = {14},
  number  = {2},
  pages   = {102-103},
  issn    = {1548-7105},
  doi     = {10.1038/nmeth.4170},
  url     = {https://doi.org/10.1038/nmeth.4170}
}

@article{Rosten2010-yf,
  title     = {Faster and better: a machine learning approach to corner
               detection},
  author    = {Rosten, Edward and Porter, Reid and Drummond, Tom},
  abstract  = {The repeatability and efficiency of a corner detector determines
               how likely it is to be useful in a real-world application. The
               repeatability is important because the same scene viewed from
               different positions should yield features which correspond to
               the same real-world 3D locations. The efficiency is important
               because this determines whether the detector combined with
               further processing can operate at frame rate. Three advances are
               described in this paper. First, we present a new heuristic for
               feature detection and, using machine learning, we derive a
               feature detector from this which can fully process live PAL
               video using less than 5 percent of the available processing
               time. By comparison, most other detectors cannot even operate at
               frame rate (Harris detector 115 percent, SIFT 195 percent).
               Second, we generalize the detector, allowing it to be optimized
               for repeatability, with little loss of efficiency. Third, we
               carry out a rigorous comparison of corner detectors based on the
               above repeatability criterion applied to 3D scenes. We show
               that, despite being principally constructed for speed, on these
               stringent tests, our heuristic detector significantly
               outperforms existing feature detectors. Finally, the comparison
               demonstrates that using machine learning produces significant
               improvements in repeatability, yielding a detector that is both
               very fast and of very high quality.},
  journal   = {IEEE Trans. Pattern Anal. Mach. Intell.},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  volume    = 32,
  number    = 1,
  pages     = {105--119},
  month     = jan,
  year      = 2010,
  language  = {en}
}


@article{Rueden2017,
  author   = {Rueden, Curtis T.
              and Schindelin, Johannes
              and Hiner, Mark C.
              and DeZonia, Barry E.
              and Walter, Alison E.
              and Arena, Ellen T.
              and Eliceiri, Kevin W.},
  title    = {ImageJ2: ImageJ for the next generation of scientific image data},
  journal  = {BMC Bioinformatics},
  year     = {2017},
  month    = {Nov},
  day      = {29},
  volume   = {18},
  number   = {1},
  pages    = {529},
  abstract = {ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science.},
  issn     = {1471-2105},
  doi      = {10.1186/s12859-017-1934-z},
  url      = {https://doi.org/10.1186/s12859-017-1934-z}
}


@article{Schindelin2012,
  author   = {Schindelin, Johannes
              and Arganda-Carreras, Ignacio
              and Frise, Erwin
              and Kaynig, Verena
              and Longair, Mark
              and Pietzsch, Tobias
              and Preibisch, Stephan
              and Rueden, Curtis
              and Saalfeld, Stephan
              and Schmid, Benjamin
              and Tinevez, Jean-Yves
              and White, Daniel James
              and Hartenstein, Volker
              and Eliceiri, Kevin
              and Tomancak, Pavel
              and Cardona, Albert},
  title    = {Fiji: an open-source platform for biological-image analysis},
  journal  = {Nature Methods},
  year     = {2012},
  month    = {Jul},
  day      = {01},
  volume   = {9},
  number   = {7},
  pages    = {676-682},
  abstract = {Presented is an overview of the image-analysis software platform Fiji, a distribution of ImageJ that updates the underlying ImageJ architecture and adds modern software design elements to expand the capabilities of the platform and facilitate collaboration between biologists and computer scientists.},
  issn     = {1548-7105},
  doi      = {10.1038/nmeth.2019},
  url      = {https://doi.org/10.1038/nmeth.2019}
}



@article{Schneider2012,
  author   = {Schneider, Caroline A.
              and Rasband, Wayne S.
              and Eliceiri, Kevin W.},
  title    = {NIH Image to ImageJ: 25 years of image analysis},
  journal  = {Nature Methods},
  year     = {2012},
  month    = {Jul},
  day      = {01},
  volume   = {9},
  number   = {7},
  pages    = {671-675},
  abstract = {For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.},
  issn     = {1548-7105},
  doi      = {10.1038/nmeth.2089},
  url      = {https://doi.org/10.1038/nmeth.2089}
}


@Comment{jabref-meta: databaseType:biblatex;}