Segmentation of Metallographic Image Based on Improved CV Model Integrated with Local Fitting Term

Kang Ni; Yiquan Wu; Song Geng

doi:10.3788/AOS201838.0411009

Acta Optica Sinica, Volume. 38, Issue 4, 0411009(2018)

Segmentation of Metallographic Image Based on Improved CV Model Integrated with Local Fitting Term

Kang Ni¹, Yiquan Wu^1,2、*, and Song Geng¹

¹ College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China

² State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, China

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Figures & Tables(7)

Fig. 1. Segmentation results of metallographic image 1 by different methods. (a) Metallographic image 1; (b) traditional CV model; (c) geodesic active contours model; (d) bias field correction level set model; (e) local binary fitting model; (f) proposed model

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Fig. 2. Segmentation results of metallographic image 2 by different methods. (a) Metallographic image 2; (b) traditional CV model; (c) geodesic active contours model; (d) bias field correction level set model; (e) local binary fitting model; (f) proposed model

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Fig. 3. Segmentation results of metallographic image 3 by different methods. (a) Metallographic image 3; (b) traditional CV model; (c) geodesic active contours model; (d) bias field correction level set model; (e) local binary fitting model; (f) proposed model

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Fig. 4. Segmentation results of metallographic image 4 by different methods. (a) Metallographic image 4; (b) traditional CV model; (c) geodesic active contours model; (d) bias field correction level set model; (e) local binary fitting model; (f) proposed model

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Table 1. DSC values of five segmentation methods

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Table 1. DSC values of five segmentation methods

Image	CV model	Geodesic active contour model	Bias field correction level set model	Local binary fitting energy model	Proposed model
Metallographic image 1	0.834	0.652	0.892	0.640	0.913
Metallographic image 2	0.864	0.849	0.715	0.709	0.927
Metallographic image 3	0.780	0.773	0.867	0.831	0.902
Metallographic image 4	0.782	0.675	0.869	0.669	0.881

Table 2. Running time of five segmentation methodss

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Table 2. Running time of five segmentation methodss

Image	CV model	Geodesic active contour model	Bias field correction level set model	Local binary fitting energy model	Proposedmodel
Metallographic image 1	230.54	125.88	450.94	195.23	189.06
Metallographic image 2	235.80	129.02	473.82	202.02	190.94
Metallographic image 3	106.63	59.13	198.39	93.33	89.83
Metallographic image 4	323.97	167.17	653.21	289.06	275.83

Table 3. Thresholds obtained by Otsu algorithm and reciprocal cross entropy algorithm
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Table 3. Thresholds obtained by Otsu algorithm and reciprocal cross entropy algorithm
Algorithm Metallographic image 1 Metallographic image 2 Metallographic image 3 Metallographic image 4
Otsu 109 139 137 157
Reciprocal cross entropy 97 157 152 124

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Kang Ni, Yiquan Wu, Song Geng. Segmentation of Metallographic Image Based on Improved CV Model Integrated with Local Fitting Term[J]. Acta Optica Sinica, 2018, 38(4): 0411009

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