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Journal ArticleDOI

Globally optimal stitching of tiled 3D microscopic image acquisitions

01 Jun 2009-Bioinformatics (Oxford University Press)-Vol. 25, Iss: 11, pp 1463-1465
TL;DR: This work developed a method that, based on the Fourier Shift Theorem, computes all possible translations between pairs of 3D images, yielding the best overlap in terms of the cross-correlation measure and subsequently finds the globally optimal configuration of the whole group of3D images.
Abstract: Motivation: Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks. Results: To optimally stitch a large collection of 3D confocal images, we developed a method that, based on the Fourier Shift Theorem, computes all possible translations between pairs of 3D images, yielding the best overlap in terms of the cross-correlation measure and subsequently finds the globally optimal configuration of the whole group of 3D images. This method avoids the propagation of errors by consecutive registration steps. Additionally, to compensate the brightness differences between tiles, we apply a smooth, non-linear intensity transition between the overlapping images. Our stitching approach is fast, works on 2D and 3D images, and for small image sets does not require prior knowledge about the tile configuration. Availability: The implementation of this method is available as an ImageJ plugin distributed as a part of the Fiji project (FijiisjustImageJ: http://pacific.mpi-cbg.de/). Contact: tomancak@mpi-cbg.de

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Citations
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Journal ArticleDOI
TL;DR: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis that facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system.
Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

43,540 citations

Journal ArticleDOI
TL;DR: ImageJ2 as mentioned in this paper is the next generation of ImageJ, which provides a host of new functionality and separates concerns, fully decoupling the data model from the user interface.
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. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

4,093 citations

Posted Content
TL;DR: The entire ImageJ codebase was rewrote, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements.
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. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs.

2,156 citations


Cites methods from "Globally optimal stitching of tiled..."

  • ...References for plugins shown: TrackMate [67], MaMuT [150], Multiview Reconstruction [70, 71], MotherMachine Analyzer (MoMA) [151, 152], Sholl Analysis [74], Kymograph Builder [153], Z-Spacing Correction [154], Trainable Weka Segmentation [155], Pendent Drop [156], SciView [157], BigDataViewer [66], Image Stitching [158], Coloc 2 [159], MorphoLibJ [100]....

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Journal ArticleDOI
TL;DR: The ImageJ project is used 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.
Abstract: 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.

2,081 citations


Cites background from "Globally optimal stitching of tiled..."

  • ...The Stitching plugin (Preibisch et al., 2009) is a popular ImageJ option for combining such image collections into a single, cohesive output (see Fig....

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Journal ArticleDOI
24 Apr 2014-Cell
TL;DR: CUBIC enables time-course expression profiling of whole adult brains with single-cell resolution and develops a whole-brain cell-nuclear counterstaining protocol and a computational image analysis pipeline that enable the visualization and quantification of neural activities induced by environmental stimulation.

1,070 citations


Cites background from "Globally optimal stitching of tiled..."

  • ...…of numerous plane images still has issues such as unavoidable errors of mechanical stage accuracy, the need for additional calculations to correct for optical distortion and rotation for reconstitution, or the accumulation of huge data sets (Emmenlauer et al., 2009; Preibisch et al., 2009)....

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  • ...This is particularly important for comparing multiple 3D whole-brain images with single-cell resolution because the assembly of numerous plane images still has issues such as unavoidable errors of mechanical stage accuracy, the need for additional calculations to correct for optical distortion and rotation for reconstitution, or the accumulation of huge data sets (Emmenlauer et al., 2009; Preibisch et al., 2009)....

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References
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Journal ArticleDOI
TL;DR: This work forms stitching as a multi-image matching problem, and uses invariant local features to find matches between all of the images, and is insensitive to the ordering, orientation, scale and illumination of the input images.
Abstract: This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.

2,550 citations


Additional excerpts

  • ...averaging, α=1 linear blending (Brown and Lowe, 2007), α>1 non-linear blending)....

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01 Jan 1975

1,204 citations


"Globally optimal stitching of tiled..." refers background or methods in this paper

  • ...The PCM results in a set of translations P= ( pAB :A,B∈V ) with V being the set of all tiles, and each pAB mapping tile A to an overlapping tile B maximizing the pairwise alignment quality....

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  • ...The method uses phase ∗To whom correspondence should be addressed. correlation (Kuglin and Hines, 1975) to find the translation between all image pairs and registers multi-tile acquisitions globally minimizing all pairwise registration errors....

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  • ...1 Phase correlation and fast Fourier transform We use the Fourier transform (F )-based phase correlation method (PCM) (Kuglin and Hines, 1975) to compute translational offsets between images....

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  • ...correlation (Kuglin and Hines, 1975) to find the translation between all image pairs and registers multi-tile acquisitions globally minimizing all pairwise registration errors....

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  • ...We use the Fourier transform (F )-based phase correlation method (PCM) (Kuglin and Hines, 1975) to compute translational offsets between images....

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Journal ArticleDOI

417 citations


"Globally optimal stitching of tiled..." refers methods in this paper

  • ...Prior to performing the PFFFT the input images have to be extended to a size supported by that algorithm....

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  • ...The resulting images are zero-padded to dimensions supported by the PFFFT algorithm....

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  • ...The Fourier transform F is computed using the multi-threaded Prime Factor Fast Fourier Transform (PFFFT) (Good, 1958)....

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