Machine vision detection of isolated and overlapped nematode worms using skeleton analysis
In this paper we present a novel method for detection of individual C.Elegans worms in population images in presence of overlapping. First, in a pre-processing phase the worms skeletons are obtained by morphological skeleton operation after image binarization and filling small holes. Then, after pruning the small branches of the skeletons, the skeletons are splited into several branches from the pixels with more than two neighbors. Angle of each branch side is calculated in the next stage and the neighbor branches with angle difference less than a predefined threshold are merged. Finally, a simple post-processing based on stastical analysis of worms' length on their widths is used in order to increase the automatic efficiency of the method. We have applied our method to a database of 147 isolated and overlapped worms and obtained 81.43% accuracy.