[1] Ren X F, Malik J. Learning a classification model for segmentation. [C]∥Proceedings Ninth IEEE International Conference on Computer Vision, October 13-16, 2003. Nice, France. IEEE, 10(2003).

[2] Zhang J D, Jiang W H, Wang R C et al. Brain MR image segmentation with spatial constrained K-mean algorithm and dual-tree complex wavelet transform[J]. Journal of Medical Systems, 38, 93(2014).

[3] Anand S, Vinod V, Rampure A. Application of fuzzy c-means and neural networks to categorize tumor affected breast MR images[J]. International Journal of Applied Engineering Research, 10, 14303362(2015).

[4] Achanta R, Shaji A, Smith K et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 2274-2282(2012).

[5] Bahreini L, Fatemizadeh E, Gity M. Gradient vector flow snake segmentation of breast lesions in dynamic contrast-enhanced MR images. [C]∥2010 17th Iranian Conference of Biomedical Engineering (ICBME), November 3-4, 2010. Isfahan, Iran. IEEE, 1-4(2010).

[6] Ren L, Li Q, Guan X et al. Three-dimensional segmentation of brain tumors in magnetic resonance imaging based on improved continuous max-flow[J]. Laser & Optoelectronics Progress, 55, 111011(2018).

[7] Pereira S, Pinto A, Alves V et al. Brain tumor segmentation using convolutional neural networks in MRI images[J]. IEEE Transactions on Medical Imaging, 35, 1240-1251(2016).

[8] Li X W, Wu Y Q, Yao Y. Level set method based on wavelet multi-scale transform and clustering for medical image segmentation[J]. Journal of Computer Applications, 34, 298-301, 316(2014).

[9] Chu J H, Min H, Liu L et al. A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation[J]. Medical Physics, 42, 3859-3869(2015).

[10] Chu J H, Li X C, Zhang J Q et al. Fine-granted segmentation method for three-dimensional brain tumors using cascaded convolutional network[J]. Laser & Optoelectronics Progress, 56, 101001(2019).

[11] Xu J, Gao X. Fully automatic detection and segmentation algorithm for ultrasound breast images using SVM and level set[J]. Journal of Computer-Aided Design & Computer Graphics, 24, 662-668, 676(2012).

[12] Wu S, Wang X P, Wang C Y et al. Interactive mammary gland segmentation based on marker-controlled watershed and Snake model[J]. Computer Engineering and Applications, 50, 189-192(2014).

[13] Hou X D, Li B C, Liu H P et al. SLICT: computing texture-sensitive superpixels in medical images[J]. Acta Automatica Sinica, 45, 965-974(2019).

[14] Qiu J J, Wu Y, Hui B et al. Texture classification study of MR images for hepatocellular carcinoma[J]. Journal of University of Electronic Science and Technology of China, 48, 619-626(2019).

[15] Wang W, Wang X J, Liu X W et al. Image segmentation algorithm based on image complexity[J]. Journal of Detection & Control, 37, 5-9(2015).

[16] Meng C C, Zhao L H. Segmentation of cervical cell image using improved global thresholding method with gradient edge information and GVF Snake model[J]. Intelligent Computer and Applications, 9, 28-32(2019).

[17] Kong Y Y, Li J H, Wang Y et al. Brain tumor segmentation method based on Hough transform and GVF Snake model[J]. Application Research of Computers, 35, 3469-3471, 3475(2018).