Colouration and patterning are widespread amongst organisms. Regarding avian eggs, colouration (reflectances) has been previously measured using spectrometers whereas spottiness has been determined using human-based scoring methods or by applying global thresholding over the luminance channel on photographs. However, the availability of powerful computers and digital image-processing algorithms and software offers new possibilities to develop systematised, automatable, and accurate methods to characterise visual information in eggs. Here, we provide a computing infrastructure (library of functions and a Graphical User Interface) for eggshell colouration and spottiness analysis called SpotEgg, which runs over MATLAB. Compared to previous methods, our method offers four novelties for eggshell visual analysis. First, we have developed a standardised non-human biased method to determine spottiness. Spottiness determination is based on four parameters that allow direct comparisons between studies and may improve results when relating colouration and patterning to pigment extraction. Second, researcher time devoted to routine tasks is remarkably reduced thanks to the incorporation of image-processing techniques that automatically detect the colour reference chart and egg-like shapes in the scene. Third, SpotEgg reduces the errors in colour estimation through the eggshell that are created by the different angles of view subtended from different parts of the eggshell and the optical centre of the camera. Fourth, SpotEgg runs automatic Fractal Dimension analysis (a measure of how the details in a pattern change with the scale at which this pattern is measured) of the spots pattern in case researchers want to relate other measurements with this special spatial pattern. Finally, although initially conceived for eggshell analysis, SpotEgg can also be applied in images containing objects different from eggs as feathers, frogs, insects, etc., since it allows the user to manually draw any region to be analysed making this tool useful not only for oologist but also for other evolutionary biologists.
Journal Paper -
Journal of Avian Biology, first online, 2016 BLACKWELL PUBLISHING
DOI: 10.1111/jav.01117 ISSN: 1600-048X » doi