Adaptive striping watershed segmentation method for processing microscopic images of overlapping irregular‐shaped and multicentre particles

Summary Oversegmentation is a major drawback of the morphological watershed algorithm. Here, we study and reveal that the oversegmentation is not only because of the irregular shapes of the particle images, which people are familiar with, but also because of some particles, such as ellipses, with more than one centre. A new parameter, the striping level, is introduced and the criterion for striping parameter is built to help find the right markers prior to segmentation. An adaptive striping watershed algorithm is established by applying a procedure, called the marker searching algorithm, to find the markers, which can effectively suppress the oversegmentation. The effectiveness of the proposed method is validated by analysing some typical particle images including the images of gold nanorod ensembles. Lay Description The morphological watershed algorithm is often used to process microscopic binary (black-and-white) images of overlapping particles, by which to automatically segment the connected particles into separated individual ones. However, oversegmentation is a major drawback of this method. That is, some individual particles may be further segmented into pieces unexpectedly. We find that the oversegmentation is not only due to the irregular shapes of the particle images, but also due to the existence of some particles (such as ellipses) with more than one center. In this work, we introduce a new parameter, called the striping level, and establish the Criterion for Striping Parameter to help find the right markers prior to segmentation. Then an adaptive striping watershed algorithm is established by applying a procedure, called the Marker Searching Algorithm, to find the markers, which can effectively suppress the oversegmentation. The proposed method is used to analyze some typical images, showing the effectiveness and reliability of the method.