Publication Date:
2018-01-12
Description:
A set of image preprocessing approaches are developed for processing plankton images reconstructed from digital holograms. First, a threshold-based algorithm of image segmentation is proposed and applied to extract the regions of plankton from the original digital images. To improve the performance of image segmentation, an appropriate filter is adopted to reduce the background noise from the image and the image gray level is adjusted to enhance the image contrast. Second, we develop a novel and efficient edge detection method purposefully for the binary images. Third, we propose and use a simple chain-code-based algorithm to eliminate the single-pixel branches along the shape boundary, which will help boundary tracing work stably. Then, an algorithm is improved and applied to trace the boundaries of the plankton regions. This algorithm is optimized based on the relationship between two consecutive chain-codes such that it is fast on implementation. Finally, break points of the shape boundary are efficiently detected based on chain-codes and the boundary is represented compactly by a polygon comprised of those points. After images are preprocessed by these approaches, some redundant information of shape is reduced that will accelerate the running speeds of further image processing and aid identification and classification of plankton at species level. We analyze the accuracy and efficiency of our algorithms. The results show that our algorithm of image segmentation has a good performance in accuracy. Our edge detection method also outperforms the commonly used edge detection methods in terms of localization performance and the running time.
Print ISSN:
0364-9059
Electronic ISSN:
1558-1691
Topics:
Architecture, Civil Engineering, Surveying
,
Physics
