Here, we present the VisioBioshapeR package for R [R
Core, 2014]. This new library is a comprehensive, multifunctional toolbox designed to
automatically analyse biological images. The package extends other common libraries (Momocs,
ShapeR) used for biological shape analysis by allowing the user to extract
closed contour outlines automatically from reading binary
images. Current functionalities of VisioBioshapeR include: random extraction of
image coordinates, analysis of the shape of a biological image by the elliptic
Fourier descriptor (EFD) method, extraction of an image characteristic vector
using multivariate principal component analysis (PCA) and geometrical analysis.
The image vector of characteristics can be directly exported to a wide range of
statistical packages in R and can be used to perform classification or other
types of analysis in order to sort new images into classes. The package could
prove useful in studies of any two-dimensional images and is presented with
three examples of its application in ecology. The library is useful when
multiple images are processed at a time and we wish to automate their analysis
for example for recognition of images from patterns.
References
[1]
Libungan, L.A.
and Pálsson, S. (2015) Shaper:
An r Package to Study Otolith Shape Variation among Fish Populations. PloS ONE, 10, e0121102. http://dx.doi.org/10.1371/journal.pone.0121102
[2]
Yang,
H.-P., Ma, C.-S., Wen,
H., Zhan,
Q.-B. and
Wang, X.-L.
(2015) A Tool
for Developing an Automatic Insect Identification System Based on Wing
Outlines. Scientific Reports, 5, 12786. http://dx.doi.org/10.1038/srep12786
[3]
Uchida, S. (2013)
Image Processing and Recognition for Biological Images. Development, Growth & Differentiation, 55, 523-549. http://dx.doi.org/10.1111/dgd.12054
[4]
Ginoris, Y.P.,
Amaral, A.L., Nicolau,
A., Coelho,
M.A.Z.
and Ferreira, E.C. (2007) Recognition
of Protozoa and Metazoa Using Image Analysis Tools, Discriminant Analysis,
Neural Networks and Decision Trees. Analytica
Chimica Acta, 595, 160-169. http://dx.doi.org/10.1016/j.aca.2006.12.055
[5]
Ghazali,
K.H., Alsameraai,
R.S.H. and
Mohamed, Z. (2013) Automated
System for Diagnosis Intestinal Parasites by Computerized Image Analysis. Modern Applied Science, 7, 98- 114. http://dx.doi.org/10.5539/mas.v7n5p98
[6]
Shamir,
L.,
Delaney, J.D., Orlov,
N., Mark
Eckley, D. and Goldberg, I.G. (2010) Pattern
Recognition Software and Techniques for Biological Image Analysis. PLoS Computational Biology, 6,
e1000974. http://dx.doi.org/10.1371/journal.pcbi.1000974
[7]
Tan,
C.S., Lau,
P.Y.,
Phang, S.-M. and
Low, T.J. (2014) A Framework
for the Automatic Identification of Algae (Neomeris
vanbosseae M.A. Howe): U3S. 2014 International Conference on Computer and Information Sciences (ICCOINS),
Kuala
Lumpur, 3-5
June 2014, 1-6.
[8]
Avci, D.
and Varol, A. (2009) An
Expert Diagnosis System for Classification of Human Parasite Eggs Based on Multi-Class
SVM. Expert Systems with Applications,
36, 43-48. http://dx.doi.org/10.1016/j.eswa.2007.09.012
Claude, J. (2008) Morphometrics
with R. Springer, Berlin.
[11]
Rohlf, F.J., Ferson, S. and Koehn, R.K. (1985) Measuring
Shape Variation of Two-Dimen- sional Outlines. Systematic Zoology, 34, 59-68. http://dx.doi.org/10.2307/2413345
[12]
R
Core Team (2015) R:
A Language and Environment for Statistical Computing.
[13]
Buf, H.D., Bayer,
M., Droop,
S., Botanic,
R., Edinburgh,
G., Head,
R.,
Juggins, S., Fischer,
S. and
Bunke, H.
(1999)
Diatom Identification: A Double Challenge Called ADIAC. International Conference on Image Analysis and Processing,
Italy, 27-29 September 1999. http://dx.doi.org/10.1109/ICIAP.1999.797682
[14]
Kloster,
M., Kauer, G. and
Beszteri, B. (2014)
SHERPA:
An Image Segmentation and Outline Feature Extraction Tool for Diatoms and Other
Objects. BMC Bioinformatics, 15, 218. http://dx.doi.org/10.1186/1471-2105-15-218
[15]
Jalba,
A.C., Wilkinson, M.H.F., Roerdink,
J.B.T.M., Bayer,
M.M. and
Juggins, S. (2005) Automatic
Diatom Identification Using Contour Analysis by Morphological Curvature Scale
Spaces. Machine Vision and Applications,
16, 217-228. http://dx.doi.org/10.1007/s00138-005-0175-8
[16]
Flor-Arnau, N., Cambra, J. and Burfeid
Castellanos, A.M.
(2012) Referencia
y red básica de diatomeas en la cuenca del ebro. evaluación del estado
ecológico de las masas de agua superficiales de la cuenca del ebro utilizando
las diatomeas bentónicas como bioindicadores resultados anos 2011-2012.
Confederación Hidrográfica del Ebro.
[17]
Hijmans, R.J. (2015)
Raster:
Geographic Data Analysis and Modeling. R Package Version 2.5-2.
