Abstract
Medical image segmentation has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, weak edges and intensity inhomogeneity (Niu et al. in 2nd IEEE international conference on computational intelligence and applications (ICCIA). https://doi.org/10.1109/ciapp.2017.816722, 2017) in medical images may hinder the accuracy of any traditional active contour segmentation methods. In this paper, we propose an improved active contour method by embedding a boundary constraint factor and adding the local information of images to the energy function of Chan–Vese model. Then graph cuts method is used to optimize the new energy function of the improved active contour method in this paper. There are several salient features: (1) we can obtain more accurate boundaries by embedding a constraint factor and adding local information of images. (2) The energy function is not easily to fall into local optimum. (3) Finally, our method only need to adjust one parameter. The evaluation results on Magnetic Resonance Imaging and Computed Tomography, blood vessel images, and mammograms masses show that the proposed method leads to more accurate boundary detection results than the state-of-the-art edge-based and region-based active contour segmentation methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arbelaez P, Maire M, Fowlkes C et al (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916. https://doi.org/10.1109/TPAMI.2010.161
Boykov YY, Jolly MP (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In: Computer vision, 2001. ICCV 2001. Proceedings. 8th IEEE international conference, vol 1. IEEE, pp 105–112. https://doi.org/10.1109/ICCV.2001.937505
Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137. https://doi.org/10.1109/TPAMI.2004.60
Cai X, Sowmya A (2009) Learning to tune level set methods. In: Image and vision computing New Zealand, 2009. IVCNZ’09. 24th international conference. IEEE, pp 310–315. https://doi.org/10.1109/IVCNZ.2009.5378391
Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79. https://doi.org/10.1023/A:1007979827043
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277. https://doi.org/10.1109/83.902291
Chen L, Lu J, Huang T et al (2014) Finding candidate drugs for hepatitis C based on chemical-chemical and chemical-protein interactions. PLoS One 9(9):e107767. https://doi.org/10.1371/journal.pone.0107767
Doctor F, Iqbal R, Naguib RNG (2014) A fuzzy ambient intelligent agents approach for monitoring disease progression of dementia patients. J Ambient Intell Humaniz Comput 5(1):147–158. https://doi.org/10.1007/s12652-012-0135-x
Domínguez AR, Nandi AK (2007) Improved dynamic-programming-based algorithms for segmentation of masses in mammograms. Med Phys 34(11):4256–4269. https://doi.org/10.1118/1.2791034
Engel TA, Charão AS, Kirsch-Pinheiro M et al (2015) Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls. J Ambient Intell Humaniz Comput 6(4):377–390. https://doi.org/10.1007/s12652-015-0292-9
Gelas A, Bernard O, Friboulet D et al (2007) Compactly supported radial basis functions based collocation method for level-set evolution in image segmentation. IEEE Trans Image Process 16(7):1873–1887. https://doi.org/10.1109/TIP.2007.898969
Heath M, Bowyer K, Kopans D et al (2000) The digital database for screening mammography. Dig Mammogr 2000:431–434
Kashyap R, Gautam P (2017) Fast medical image segmentation using energy-based method. In: Tiwari V, Tiwari B, Thakur R, Gupta S (eds) Pattern and data analysis in healthcare settings. IGI Global, Hershey, pp 35–60. https://doi.org/10.4018/978-1-5225-0536-5.ch003
Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331. https://doi.org/10.1007/BF00133570
Kihara Y, Soloviev M, Chen T (2016) In the shadows, shape priors shine: using occlusion to improve multi-region segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 392–401. https://doi.org/10.1109/CVPR.2016.49
Lan H, Liu XT (2012) Energy minimization model for image segmentation via graph cut optimization. Appl Res Comput 42(19):4656–4664
Lee S, Ahmad A, Jeon G (2018) Combining bootstrap aggregation with support vector regression for small blood pressure measurement. J Med Syst 42(4):63. https://doi.org/10.1007/s10916-018-0913-x
Li C, Xu C, Gui C et al (2005) Level set evolution without re-initialization: a new variational formulation. In: Computer vision and pattern recognition. CVPR 2005. IEEE computer society conference, vol 1. IEEE, pp 430–436. https://doi.org/10.1109/cvpr.2005.213
Li C, Kao CY, Gore JC et al (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10):1940–1949. https://doi.org/10.1109/TIP.2008.2002304
Li C, Xu C, Gui C et al (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12):3243–3254. https://doi.org/10.1109/TIP.2010.2069690
Mylona EA, Savelonas MA, Maroulis D (2014) Automated adjustment of region-based active contour parameters using local image geometry. IEEE Trans Cybern 44(12):2757–2770. https://doi.org/10.1109/TCYB.2014.2315293
Niu Y, Cao J, Liu L, Guo H (2017) A novel ACM for segmentation of medical image with intensity inhomogeneity. In: 2nd IEEE international conference on computational intelligence and applications (ICCIA). https://doi.org/10.1109/ciapp.2017.816722
Rad AE, Rahim MSM, Kolivand H et al (2017) Morphological region-based initial contour algorithm for level set methods in image segmentation. Multimed Tools Appl 76(2):2185–2201. https://doi.org/10.1007/s11042-015-3196-y
Tomczyk A, Szczepaniak PS, Pryczek M (2013) Cognitive hierarchical active partitions in distributed analysis of medical images. J Ambient Intell Humaniz Comput 4(3):357–367. https://doi.org/10.1007/s12652-012-0110-6
Wang Y, Tao D, Gao X et al (2011) Mammographic mass segmentation: embedding multiple features in vector-valued level set in ambiguous regions. Pattern Recognit 44(9):1903–1915. https://doi.org/10.1016/j.patcog.2010.08.002
Wu J, Deng L, Jeon G et al (2018) GPU-parallel interpolation using the edge-direction based normal vector method for terrain triangular mesh. J Real Time Image Process. https://doi.org/10.1007/s11554-016-0575-1
Yang X, Gao X, Tao D et al (2015) An efficient MRF embedded level set method for image segmentation. IEEE Trans Image Process 24(1):9–21. https://doi.org/10.1109/TIP.2014.2372615
Zhou Y, Shi WR, Chen W et al (2015) Active contours driven by localizing region and edge-based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images. Neurocomputing 156:199–210. https://doi.org/10.1016/j.neucom.2014.12.061
Zou Q, Zeng J, Cao L et al (2016) A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 173:346–354. https://doi.org/10.1016/j.neucom.2014.12.123
Acknowledgements
This work was supported by the National Natural Science Foundation of China (41031064; 61572384; 61432014), Shaanxi Key Technologies Research Program (2017KW-017), China’s postdoctoral fund first-class funding (2014M560752), Shanxi province postdoctoral science fund, the central university basic scientific research business fee (JBG150225).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Han, B., Han, Y., Gao, X. et al. Boundary constraint factor embedded localizing active contour model for medical image segmentation. J Ambient Intell Human Comput 10, 3853–3862 (2019). https://doi.org/10.1007/s12652-018-0978-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-018-0978-x