Aluminum alloy microstructural segmentation method based on simple noniterative clustering and adaptive density-based spatial clustering of applications with noise

Shiyue Zhang; Dali Chen; Shixin Liu; Pengyuan Zhang; Wei Zhao

doi:10.1117/1.JEI.28.3.033035

27 June 2019 Aluminum alloy microstructural segmentation method based on simple noniterative clustering and adaptive density-based spatial clustering of applications with noise

Shiyue Zhang, Dali Chen, Shixin Liu, Pengyuan Zhang, Wei Zhao

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 3, 033035 (June 2019). https://doi.org/10.1117/1.JEI.28.3.033035

Abstract

We propose an unsupervised segmentation method based on simple non-iterative clustering (SNIC) and adaptive density-based spatial clustering of applications with noise (DBSCAN). The method is not sensitive to parameter settings. And cluster parameter suitable for each image can be automatically calculated. SNIC superpixel segmentation is applied in achieving over-segmented images to solve the problem of the image resolution being too high. Then, adaptive DBSCAN clustering is proposed to cluster the over-segmented superpixel blocks to solve the problem of over-segmentation and manual adjustment of DBSCAN parameters. Finally, k-means and connected regions are used for postprocessing to remove the shadow superpixel blocks from the clustered image and to ensure the integrity of a single microstructure. The effectiveness of this method is proved by many experiments. Based on this method, we provide a fast labeling method to help experts quickly label metallographic images.

Citation Download Citation

Shiyue Zhang, Dali Chen, Shixin Liu, Pengyuan Zhang, and Wei Zhao "Aluminum alloy microstructural segmentation method based on simple noniterative clustering and adaptive density-based spatial clustering of applications with noise," Journal of Electronic Imaging 28(3), 033035 (27 June 2019). https://doi.org/10.1117/1.JEI.28.3.033035

Received: 28 December 2018; Accepted: 30 May 2019; Published: 27 June 2019

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