Speaker
Description
"Biological imaging often generates three-dimensional images with a very large number of elements. In particular, microtomography results in images of several Giga-Bytes. The analysis of such data requires fast and efficient image processing software solutions, especially when user interaction is necessary. In some cases, algorithmic methods have been proposed but are seldom implemented within image processing software. We present here the use of run-length encoding for morphological processing of large binary images, and its application to plant biology.
Run-length encoding allows to represent binary images with by reducing memory footprint. Moreover, efficient algorithms for morphological dilation and erosion have been proposed that take advantage of the encoding, drastically reducing computation time. We also investigated its application to morphological reconstruction.
The methods were implemented and integrated within a graphical user interface. This allowed to apply a semi-automated image processing workflow of large 3D images of wheat grains acquired by synchrotron X-ray microtomography. Further work will consider the extension of the method to the processing of large label map images.
| Authors | David Legland*, Anne-Laure Chateigner-Boutin, Christine Girousse |
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| Keywords | mathematical morphology, 3D image, algorithm, implementation |
