Software: Difference between revisions

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=<span style="color:Navy;">'''Algorithms'''=
=<span style="color:Navy;">'''Algorithms'''=
==<span style="color:Chocolate;">MSERs, extrema, connected-set filters and sieves==
==<span style="color:Chocolate;">MSERs, extrema, connected-set filters and sieves==
====<span style="color:Chocolate;">The algorithm finding MSER's starts with a connected-set opening or 'o' sieve</span>====
{| border="0" cellpadding="5" cellspacing="5"
{| border="0" cellpadding="5" cellspacing="5"
|- valign="top"
|- valign="top"
|width="30%"| [[Image:Cameraman_iso_topview.jpg|300px|AAMToolbox]]Cameraman image. Superimposed red spots are maximal extrema and blue spots are minima. Irregular cyan, blue and yellow regions illustrate regions associated with maxima and the magenta region is a minimum.
|width="50%"| [[Image:Cameraman_iso_topview.jpg|300px|AAMToolbox]]Cameraman image. Superimposed red spots are maximal extrema and blue spots are minima. Irregular cyan, blue and yellow regions illustrate regions associated with maxima and the magenta region is a minimum.
[[Image:cameraman_iso_tree.jpg|300px|AAMToolbox]]Isometric view of the cameraman image with superimposed maxima and minima. The trees trace the maxima through increasing scale-space. Large spots have been identified as stable extrema.
|[[Image:cameraman_iso_tree.jpg|300px|AAMToolbox]]<br>Isometric view of the cameraman image with superimposed maxima (red) and minima (blue). The trees trace the maxima through increasing scale-space. Large spots have been identified as stable extrema.
|width="70%"|For finding features in and segmenting images. <br><br>
|}
====For finding features in and segmenting images. ====
[[AAMToolbox Details|'''What? How? Where?''']]<br><br>
[[AAMToolbox Details|'''What? How? Where?''']]<br><br>
[[MSER's and Connected sets|'''''Tutorials''''': from the beginning]]<br><br>
[[MSER's and Connected sets|'''''Tutorials''''': from the beginning]]<br><br>


[[siv Download|<span style="color: Gray">'''''Download''''' </span>]]<br><br>
[[siv Download|<span style="color: Gray">'''''Download''''' </span>]]<br><br>
====<span style="color:Chocolate;">The algorithm finding MSER's starts with a connected-set opening or 'o' sieve</span>====
 
<!-- [[Software#MSERs extrema connected-set filters and sieves|<span style="color:Green;">'''MORE'''</span>]] -->
<!-- [[Software#MSERs extrema connected-set filters and sieves|<span style="color:Green;">'''MORE'''</span>]] -->
The papers by George Matas((Matas, et. al. 2002<ref>Matas, J., M. Urban, O. Chum and T. Pajdla (2002). ''Robust Wide baseline Stereo from Maximally Stable Extremal Regions.''  BMVC, Cardiff</ref>))((Matas et al., 2004)<ref>Matas, Jiri, et al. ''Robust wide-baseline stereo from maximally stable extremal regions''. Image and vision computing 22.10 (2004): 761-767.</ref>))
(Mishkin et al., 2013)<ref>Dmytro Mishkin, Michal Perdoch,Jiri Matas (2013) ''Two-view Matching with View Synthesis Revisited'' arXiv preprint arXiv:1306.3855 </ref>  put together an effective way of finding distinguished regions (DR’s)  namely maximally stable extremal regions (MSER’s ) with a powerful way of ''describing'' the regions at multiple scales and ''robustly matching'' such measurements with others in a second image. Since then many authors have confirmed the algorithms as a powerful tool for finding objects in images (review Mikolajczyk et al 2006: <ref>Krystian Mikolajczyk, Tinne Tuytelaars, Cordelia Schmid, Andrew Zisserman, Jiri Matas, Frederik Schaffalitzky, Timor Kadir, L Van Gool, (2006) ''A Comparison of Affine Region Detectors.''International Journal of Computer Vision. DOI: 10.1007/s11263-005-3848-x</ref>
<br><br>
<!--(This is a ''blast from the past''. I failed to popularise it at the time, however, '''MSER's are now attracting lots of attention''' so I'm now contributing my bit a little late in the day.) <br><br>-->
<!--[[http://cmpdartsvr3.cmp.uea.ac.uk/wiki/BanghamLab/index.php/Andrews_Organ_Recital Why the hurry?]]
<br><br>-->
The algorithm '''underlying''' that for finding Maximally stable extremal regions (MSER's) '''is an 'o' sieve'''. Such algorithms relate closely to mathematical morphology (dilations-erosion (Jackway et al 1996<ref>P. T. Jackway and M. Deriche. ''Scale-space properties of multiscale morphological dilation-erosion.'' IEEE Trans. Pattern Analysis and Machine Intelligence
, 18(1):38–51</ref>) openings, closings and in particular watersheds (Vincent et al 1991 <ref>Vincent, Luc, and Pierre Soille. "Watersheds in digital spaces: an efficient algorithm based on immersion simulations." IEEE transactions on pattern analysis and machine intelligence 13.6 (1991): 583-598.</ref>) and reconstruction filters). In mathematical morphology the 'filtering' element of the MSER algorithm might be called a 'connected-set opening' ('o' sieve) . It is one of a family of closely related algorithms which for which I coined the term '''sieves'''. Why?
<br><br>
It is useful to distinguish between '''two very different signal simplifying algorithms''' both of which preserve scale-space. So called diffusion 'filters' and non-linear 'sieves'. In a filter-bank, diffusion filters (Gaussian filter) spread outliers such as impulses and sharp edged extrema over many scales whereas sieves do not (c.f. mechanical sieves in which particles either go through holes or they do not [http://en.wikipedia.org/wiki/Mesh_%28scale%29 Particle filters and sieves]). There seems to be a lot of philosophy/biology associated with arguments in favour of [http://en.wikipedia.org/wiki/Scale_space linear filters]. Why?  The non-linear 'o' sieve filter-bank appears to be a better feature finder (MSER's) and why does it have to be relevant in the natural world of biology where the fundamental signalling devices are non-linear, e.g. action potentials, GTP-binding switch proteins, etc.
|}
===What do we know about sieves?===
As low-pass filters sieves robustly reject outliers (Bangham, J.A. 1993<ref>Bangham, JA (1993) ''Properties of a Series of Nested Median Filters, Namely the Data Sieve.'' IEEE Transactions on Signal Processing, 41 (1). pp. 31-42. ISSN 1053-587X</ref>). Whilst it is clear that by 'knocking off' outliers they cannot introduce new extrema we formally proved that by this definition they preserve '''''scale-scale space''''' (Bangham et al 1996<ref>Bangham, JA, Harvey, RW, Ling, PD and Aldridge, RV (1996) ''Morphological scale-space preserving transforms in many dimensions.'' The Journal of Electronic Imaging (JEI), 5 (3). pp. 283-299.</ref>Bangham et al 1996b<ref>Bangham, JA, Chardaire, P, Pye, CJ and Ling, PD (1996) ''Multiscale nonlinear decomposition: The sieve decomposition theorem.'' IEEE Transactions on Pattern Analysis and Machine Intelligence, 18 (5). pp. 529-539. ISSN 0162-8828</ref> (c.f. the properties of multiscale dilation and erosion (Jackway et al 1996<ref>Jackway, P.T. and Deriche, M. (1996) ''Scale-space properties of the multiscale morphological dilation-erosion'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, no.1, pp.38,51</ref> )<br><br>
'''Applications''' in addition to their role in MSERs. Whilst 2D MSER's (sieves in our old terminology) are used for finding objects in 2D image we have also used sieves in other ways, indeed we started in 1D. For example analysing protein hydrophobicity plots(Bangham, 1988<ref>Bangham, J.A. (1988). ''Data-sieving hydrophobicity plots. Anal. Biochem''. 174, 142–145</ref>), de-noising single channel current data(Bangham et al, 1984<ref>Bangham, J.A., and T.J.C. Jacob (1984). ''Channel Recognition Using an Online Hardware Filter''. In Journal of Physiology, (London: Physiological Society), pp. 3–5</ref>), texture analysis(Southam et al, 2009<ref>Southam, P., and Harvey, R. (2009). ''Texture classification via morphological scale-space: Tex-Mex features''. J. Electron. Imaging 18, 043007–043007</ref>), lipreading(Matthews et al., 2002<ref>Matthews, I., Cootes, T.F., Bangham, J.A., Cox, S., and Harvey, R. (2002). ''Extraction of visual features for lipreading''. Pattern Anal. Mach. Intell. Ieee Trans. 24, 198–213</ref>). In 2D for segmenting 2D through extremal trees(Bangham et al., 1998<ref>Bangham, J.A., Hidalgo, J.R., Harvey, R., and Cawley, G. (1998). ''The segmentation of images via scale-space trees''. In Proceedings of British Machine Vision Conference, pp. 33–43</ref>), maximally stable contours(Lan et al., 2010<ref>Lan, Y., Harvey, R., and Perez Torres, J.R. (2010). ''Finding stable salient contours.'' Image Vis. Comput. 28, 1244–1254</ref>), images (), creating painterly pictures from photos(Bangham et al., 2003<ref>Bangham, J.A., Gibson, S.E., and Harvey, R. (2003). T''he art of scale-space''. In Proc. British Machine Vision Conference, pp. 569–578</ref>); and in 3D for segmenting volumes.




Revision as of 17:17, 25 November 2013

Current activity: a collaboration with the CoenLab with the aim of understanding how patterns of gene activity in biological organs influence the developing shape. The BanghamLab is focussed on the conceptual underpinning: concepts captured in computational growth models, experimental data visualisation and analysis.

Notes on documenting our software

Notes for Lab members on how to contribute to this Wiki and where to put downloads.

Computational biology

Quantitative understanding of growing shapes: GFtbox

Work with GFtbox to practice thinking quantitatively about the relationship between genes, growth and form.



Example of a growing snapdragon flower ( Green et al 2011). Growth is specified by factors (genes) according to the Growing Polarised Tissue Framework. Journal.pbio.1000537.g009.png</wikiflv>
<imgicon>GPT_thumbnail2.png|120px|GFtbox</imgicon>

For modelling the growth of shapes.

Ready Reference Manual

(PC, Mac, Linux, uses Matlab
no Mathworks toolboxes needed
Matlab 30 day free trial and
student edition)


Ready Reference'for ancillary functions: in interaction function and external functions' Manual

What? How? Where? Background

Tutorials: from the beginning Start here

Examples: from publications

Download GFTbox from SourceForge

Download GFTbox project files:

Petals Sauret-Güeto et al 2013

Leaves Kuchen et al 2012

Principles and concepts Kennaway et al 2011

Snapdragon Green et al 2011, Cui et al 2010

 GFtbox is an implementation of the Growing Polarised Tissue Framework for understanding and modelling the relationship between gene activity and the growth of shapes such leaves, flowers and animal embryos (Kennaway et al 2011).

The GPT-framework was used to capture an understanding of (to model) the growing petal (Sauret-Güeto et al 2013), leaf (Kuchen et al 2012) and Snapdragon flower Green et al 2011. The Snapdragon model was validated by comparing the results with other mutant and transgenic flowers Cui et al 2010.

Key is how outgrowths can be specified by genes. The icon shows an asymmetrical outgrowth. Conceptually, it is specifed by two independent patterns under genetic control: a pattern of growth and a pattern of organisers. The outgrowth arises from a region of extra overall growth. Growth is aligned along axes set by two interacting systems. Organisers at the ends of the mesh create a lengthwise gradient. This gradient interacts with the second due to an organiser that generates polariser in a region that becomes the tip of the outgrowth. (Kennaway et al 2011)

Viewing and measuring volume images: VolViewer

<imgicon>VolViewer-logo.png|120px|VolViewer</imgicon> For viewing and measuring volume images on both normal and stereo screens. Typical images from: confocal microscope and Optical Projection Tomography (OPT) images

What? How? Where?

Tutorials: from the beginning

Download

(Windows, Mac, Linux)

Output from VolViewer has appeared in:

Cell: Online Gallery | Front cover: Handbook of Plant Science | Front cover: The Plant Cell | American Scientist | Royal Microscopical Society: Infocus Magazine | Bundled with the Bioptonic 3001 scanner: Bioptonics Viewer | The Daily Mail | The Guardian newspaper: 3D Fruit fly | Qt Ambassador program | Triffid Nurseries website



VolViewer is used as a stand-alone app. or as a viewport for other systems, e.g. Matlab programs. VolViewer uses OpenGL and Qt to provide a user friendly application to interactively explore and quantify multi-dimensional biological images. It has been successfully used in our lab to explore and quantify confocal microscopy and optical projection tomography images. It is open-source and is also compatible with the Open Microscopy Environment (OME).

Analysing shapes in 2D and 3D: AAMToolbox

<imgicon>AAMToolbox_logo.jpg|120px|AAMToolbox</imgicon> For analysing populations of shapes and colours within the shapes using principal component analysis.

What? How? Where?

Tutorials: from the beginning

Download revised Nov2012


(PC, Mac, Linux, uses Matlab
no Mathworks toolboxes needed
Matlab 30 day free trial and
student edition)

 The AAMToolbox enables the user analyse the shape and colour of collections of similar objects. Originally developed to analyse face shapes for lipreading (Matthews et al. 2002version of pdf), we have used it extensively for analysing the shapes of leaves (Langlade et al 2005.,Bensmihen et al. 2010) and petals (Whibley et al 2006,Feng et al. 2010). The analysis can be applied to art, for example, finding systematic differences between portraits by Rembrandt and Modigliani.

Analysing the shapes of clones: SectorAnalysisToolbox

<imgicon>Sector analysis icon.jpg|120px|SectorAnalysisToolbox</imgicon> For analysing the shapes of marked cell clones.

What? How? Where?

Tutorials: from the beginning

Download

(PC, Mac, Linux, uses Matlab
no Mathworks toolboxes needed
Matlab 30 day free trial and
student edition)

 The SectorAnalysisToolbox enables the user analyse the shapes of marked clones in a sheet of tissue.

Algorithms

MSERs, extrema, connected-set filters and sieves

The algorithm finding MSER's starts with a connected-set opening or 'o' sieve

AAMToolboxCameraman image. Superimposed red spots are maximal extrema and blue spots are minima. Irregular cyan, blue and yellow regions illustrate regions associated with maxima and the magenta region is a minimum. AAMToolbox
Isometric view of the cameraman image with superimposed maxima (red) and minima (blue). The trees trace the maxima through increasing scale-space. Large spots have been identified as stable extrema.

For finding features in and segmenting images.

What? How? Where?

Tutorials: from the beginning

Download


Art created using ArtMaster was featured in an exhibit at the London Victoria and Albert (V&A) Museum exhibition '???' exhibition finding its name.
<references group="footnotes" />

References

<references />


Art, extrema of light and shade: PhotoArtMasterItalic text

DegasLightAndShade.jpgEmma face Art C.jpg



Illustration of PhotoArtMaster used to find and 'paint' with regions of light and shade crisply segmented from a photograph. Likewise, on the right, edges.

The algorithms underpinning PhotoArtMaster software (an extract of the 'essay ). These documents were written to support our Fo2Pix company. A Windows version of the software will soon be downloadable from here for free (originally it sold >65,000 licences but Fo2Pix had to be closed primarily due to ill health.)

Trees 1.jpg



Illustration of PhotoArtMaster used to find and 'paint' with regions of colour crisply segmented from a photograph.


Reaction-diffusion and morphogenesis

Tentacles morphogenesis.png

Illustration of morphogenesis inspired by Turing's paper.

Example using growth toolbox GPT_ReactionDiffusionTentacles_20121211.zip

1 A

A simple reaction-diffusion system develops a pattern of spots.


<wikiflv width="300" height="300" logo="false" loop="true" background="white">GPT_rd_rk_tentacles_20120417-0004.flv|GPT_rd_rk_tentacles_20120417-0004_First.png</wikiflv>
Two simple growth rules translate the pattern into directed growth. The changing geometry that arises through growth causes the reaction-diffusion patterning to continue to change.


<wikiflv width="300" height="300" logo="false" loop="true" background="white">GPT_rd_rk_tentacles_20120417-0001.flv|GPT_rd_rk_tentacles_20120417-0004.png</wikiflv>

Open source systems to which we contribute

OMERO

<imgicon>OMERO_DIAGRAM.jpg|100px|OMERO</imgicon> For working with the OME image database.

See Details, Download
OMERO Workshop

(Windows, Mac, Linux)

Open Microscopy Environment Remote Objects (OMERO). for visualising, managing, and annotating scientific image data. See also our OMERO Workshop training course we ran in April 2011.

Tools and Utilities

BioformatsConverter

<imgicon>BioformatsConverterZip.png|100px|BioformatsConverter</imgicon> For converting microscope manufacturer proprietary file formats.

See Details

(Windows, Mac, Linux)

This tool allows for the batch conversion of microscope manufacturer proprietary file formats, to the open source OME-TIFF standard. Uses the Bioformats library.

In development

MTtbox

<imgicon>MTtboxA.jpg|100px|BioformatsConverter</imgicon> For modelling the behaviour of microtubules within a cell.

See Details

(Windows, Mac, Linux)

In development. The idea is to be able to model the behaviour of growing microtubules and factors as they react chemically and diffuse within the different cell compartments.

The icon shows a spherical cell sliced open to show concentric components: cell wall (magenta), plasma-membrane (yellow), cytoplasm (green) and vacuole (yellow). Microtubules (blue) grow in 3D within the cytoplasm.

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