Breast Tumor Segmentation Based on SLIC and GVF Snake Algorithm

Yan Wang; Jiying Li; Yilin Yang; Yongqian Yu; Jinghui Wang

doi:10.3788/LOP57.141023

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 14 >Page 141023 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 14, 141023 (2020)

Breast Tumor Segmentation Based on SLIC and GVF Snake Algorithm

Yan Wang^*, Jiying Li, Yilin Yang, Yongqian Yu, and Jinghui Wang

Author Affiliations

School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

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DOI: 10.3788/LOP57.141023 Cite this Article Set citation alerts

Yan Wang, Jiying Li, Yilin Yang, Yongqian Yu, Jinghui Wang. Breast Tumor Segmentation Based on SLIC and GVF Snake Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141023 Copy Citation Text

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Fig. 1. Intercepting the MRI image of the breast

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SLIC results comparison chart. (a) Original image; (b) pre-processing result map; (c) SLIC algorithm segmentation result map when K'K; (d) adaptive K value corresponding result map; (e) result graph when K'>K

Fig. 2. SLIC results comparison chart. (a) Original image; (b) pre-processing result map; (c) SLIC algorithm segmentation result map when K'<K; (d) adaptive K value corresponding result map; (e) result graph when K'>K

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Fig. 3. PR curves corresponding to different K values

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Fig. 4. Experimental results. (a1)-(d1) Original images; (a2)-(d2) gradient images; (a3)-(d3) results of watershed algorithm; (a4)-(d4) results of level set algorithm; (a5)-(d5) results of GVF Snake algorithm; (a6)-(d6) results of proposed algorithm; (a7)-(d7) results of standard segmentation

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Fig. 5. PR curves and DICE linear graph of different segmentation models. (a) PR curves; (b) DICE linear graph

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Index	Fig. 1(a)			Fig. 1(b)			Fig. 1(c)
Index	100	125	150	50	71	100	15	18	50
DICE	0.8307	0.8419	0.8242	0.8382	0.8441	0.8301	0.7657	0.7700	0.7394
VOE	0.3383	0.3160	0.3515	0.3236	0.3117	0.3397	0.4686	0.4599	0.5212

Table 1. Evaluation of segmentation results of different K values

Index	K'>K	K'<K	K'=K
MAE	0.173802	0.172418	0.171266

Table 2. Average absolute error corresponding to different K values

Algorithm index	DICE		VOE		RVD		Precision		Recall
Algorithm index	Fig. 4(a)	Fig. 4(b)	Fig. 4(a)	Fig. 4(b)	Fig. 4(a)	Fig. 4(b)	Fig. 4(a)	Fig. 4(b)	Fig. 4(a)	Fig. 4(b)
Level set	0.9101	0.9173	0.1662	0.1621	0.1810	0.1763	0.8403	0.8485	0.9925	0.9981
SLIC	0.8419	0.8441	0.3160	0.3117	0.3752	0.3693	0.7271	0.7303	0.9204	0.9327
GVF Snake	0.8987	0.8898	0.2024	0.1018	0.2252	0.0018	0.8161	0.7870	0.9258	0.9483
Watershed	0.9161	0.9122	0.1534	0.1705	0.1661	0.1864	0.8509	0.8406	0.9922	0.9973
SLIC+GVF Snake	0.9305	0.9357	0.1255	0.0935	0.1339	0.0893	0.8755	0.9816	0.9928	0.8939

Table 3. Evaluation indicators for different segmentation models

Yan Wang, Jiying Li, Yilin Yang, Yongqian Yu, Jinghui Wang. Breast Tumor Segmentation Based on SLIC and GVF Snake Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141023

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