The traditional watershed segmentation methods usually suffer from over segmentation for irregularly shaped particles. This is because the distance map of an irregularly shaped particle contains multiple local maxima, and… Click to show full abstract
The traditional watershed segmentation methods usually suffer from over segmentation for irregularly shaped particles. This is because the distance map of an irregularly shaped particle contains multiple local maxima, and over segmentation would happen if these local maxima were used as seeds for watershed segmentation. In this work, several methods based on morphological reconstruction, including h‐dome transform, h‐maxima, and area‐reconstruction h‐dome transform, are introduced to merge, or erase redundant local maxima, and the performance of these methods in avoiding over segmentation is compared. The results show that the area‐reconstruction h‐dome transform is the most effective method in controlling over segmentation among the evaluated methods. However, the area‐reconstruction h‐dome transform is achieved by superposition of binary reconstructions at each grayscale level, which is extremely time‐consuming and impractical for batch processing. A hybrid pixel‐queue algorithm is applied to accelerate the area‐reconstruction h‐dome transform, and the algorithm is implemented in Cython to further improve the computational efficiency. For a 2592 × 1944 pixel image, on a PC with an Intel Core i5 2.4GHz processor and 8 GB RAM, the processing time of the area‐reconstruction h‐dome transform after acceleration is about 549 ms, which is 249 times faster than the unaccelerated algorithm and 4 times faster than the reconstruction function in the Scikit‐image library (an open‐source image processing library for the Python programming language) which performs reconstruction by dilation. The accelerated area‐reconstruction h‐dome transform algorithm was successfully applied to the segmentation of rubber particles in a thermoplastic polyolefin (TPO) compound.
               
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