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. 2019 Jan 10;14(1):e0210411.
doi: 10.1371/journal.pone.0210411. eCollection 2019.

A fast threshold segmentation method for froth image base on the pixel distribution characteristic

Dong-Heng Xie  1 , Ming Lu  1 , Yong-Fang Xie  2 , Duan Liu  1 , Xiong Li  3
Affiliations

A fast threshold segmentation method for froth image base on the pixel distribution characteristic

Dong-Heng Xie et al. PLoS One. .

Abstract

With the increase of the camera resolution, the number of pixels contained in froth image is increased, which brings many challenges to image segmentation. Froth size and distribution are the important index in froth flotation. The segmentation of froth images is always a problem in building flotation model. In segmenting froth images, Otsu method is usually used to get a binary image for classification of froth images, this method can get a satisfactory segmentation result. However, each gray level is required to calculate each of the between-class variance, it takes a longer time in froth images with a large number of pixels. To solve this problem, an improved method is proposed in this paper. Most froth images have the pixel distribution characteristic that the gray histogram curve is a sawtooth shape. The proposed method uses polynomial to fit the curve of gray histogram and takes the characteristic of gray histogram's valley into consideration in Otsu method. Two performance comparison methods are introduced and used. Experimental comparison between Otsu method and the proposed method shows that the proposed method has a satisfactory image segmentation with a low computing time.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart of the improved method in this paper.
Fig 2
Fig 2. The curve of the relationship between the filter threshold and the processing time.
Fig 3
Fig 3
Comparison of segmentation results with different filter thresholds in the first case. (a, b, c and d are the segmentation result with the filter thresholds of 5, 10, 15 and 20, respectively).
Fig 4
Fig 4
Comparison of segmentation results with different filter thresholds in the second case. (a, b, c and d are the segmentation result with the filter thresholds of 5, 10, 15 and 20, respectively).
Fig 5
Fig 5. Origin image.
Fig 6
Fig 6. Gray histogram.
Fig 7
Fig 7. The segmentation result of class A froth image.
Fig 8
Fig 8. The segmentation result of class B froth image.
Fig 9
Fig 9. The segmentation result of class C froth image.
Fig 10
Fig 10. The segmentation result of class D froth image.
Fig 11
Fig 11. The original curve and the fitting curve.
See this image and copyright information in PMC

References

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