Abstract
To find the optimum threshold of an image is still an important research topic in the recent years. This paper presents a segmentation of liver cyst for ultrasound image through combining Wellner’s thresholding algorithm with particle swarm optimization (PSO). The proposed method firstly obtains an optimal parameter, which expressed as a percentage or fixed amount of dark objects against a white background in a gray image, of Wellner’s thresholding algorithm by PSO method. And then the gray image is binarized according to the optimized parameter. Finally, a semi-automatic method for locating and identifying multiple liver cysts or single liver cyst of ultrasound images is performed. For a validation, the results of the proposed technique are compared with those of other segmented methods. We also tested 92 ultrasound images of the liver cysts by our software. The corrected identification rate of the single liver cysts is 97.7 %, and that of multiple liver cysts is 87.5 %. Experimental results demonstrate that the proposed technique is reliable on segmenting the contour of liver cyst and identifying single or multiple liver cysts.
Similar content being viewed by others
References
Bradley D, Roth G (2007) Adaptive thresholding using the integral image. J Graph Tools 12(2):13–21
Chen CM, Lu HHS, Huang YS (2002) Cell-based dual snake model: a new approach to extracting highly winding boundaries in the ultrasound images. Ultrasound Med Biol 28(8):1061–1073
Chen MF, Zhu HS, Zhu HJ (2013) Segmentation of liver in ultrasonic image applying local optimal threshold method. Imaging Sci J 61(7):579–591
Crespo J, Maojo V (1998) New results on the theory of morphological filters by reconstruction. Pattern Recogn 31(4):419–429
Feng X, Shen X, Wang Q, Kim J et al (2013) Learning based ensemble segmentation of anatomical structures in liver ultrasound image. In: Proc. of SPIE in Biomedical Optics and Imaging
Huang Q, Bai X, Li Y, ** L, Li X (2014) Optimized graph-based segmentation for ultrasound images. Neurocomputing 129:216–224
Jeon J, Choi J, Lee S, Ro Y (2013) Multiple ROI selection based focal liver lesion classification in ultrasound images. Expert Syst Appl 40(2):450–457
Kotropoulos C, Pitas I (2003) Segmentation of ultrasonic images using support vector machines. Pattern Recogn Lett 24(4–5):715–727
Latifoglu F (2013) A novel approach to speckle noise filtering based on artificial bee colony algorithm: an ultrasound image application. Comput Methods Prog Biomed 111(3):561–569
Lee WL, Chen YC, Hsieh KS (2005) Unsupervised segmentation of ultrasonic liver images by multi-resolution fractal feature vector. Inf Sci 175:177–199
Linguraru MG, Richbourg WJ, Liu J et al (2012) Tumor burden analysis on computed tomography by automated liver and tumor segmentation. IEEE Trans Med Imaging 31(10):1965–1976
Milko S, Samset E, Kadir T (2008) Segmentation of the liver in ultrasound: a dynamic texture approach. Int J Comput Assist Radiol Surg 3:143–150
Mittal D, Kumar V, Saxena SC, Khandelwal N, Kalra N (2010) Enhancement of the ultrasound image by modified anisotropic diffusion method. Med Biol Eng Comput 48(12):1281–1291
Niblack W (1986) An introduction to digital image processing. Prentice/Hall International, pp. 115–124
Noble JA, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25(8):987–1010
Otsu N (1979) A threshold selection method from grey level histogram. IEEE Trans Syst Man Cybern 9(1):62–66
Ozic MU, Ozbay Y, Baykan OK (2014) Detection of tumor with Otsu-PSO method on brain MR image, Signal Processing and Communications Applications Conference, pp. 1999–2002
Phee SJ, Yang K (2010) Interventional navigation systems for treatment of unresectable liver tumor. Med Biol Eng Comput 48(2):103–111
Riberiro RT, Marinho RT, Miguel Sanches J (2013) Classification and staging of chronic liver disease from multimodal data. IEEE Trans Biomed Imaging 60(5):1336–1344
Singh M, Singh S, Gupta S (2014) An information fusion based method for liver classification using texture analysis of ultrasound images. Inf Fusion 19(1):91–96
Slabaugh G, Unal G, Wels M, Fang T, Rao B (2009) Statistical region-based segmentation of ultrasound images. Ultrasound Med Biol 35(5):781–795
Smeets D, Loeckx D, Stijnen B, De Dobbelaer B, Vandermeulen D, Suetens P (2010) Semi-automatic level set segmentation of liver tumors combining a spiral scanning technique with supervised fuzzy pixel classification. Med Image Anal 14(1):13–20
Virmani J, Kumar V, Kalra N, Khandelwar N (2013) SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. J Digit Imaging 26(3):530–543
Weijers G, Starke A, Haudum A, Thijssen JM, Rehage J, De Korte CL (2010) Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis. Ultrason Imaging 32(3):143–153
Wellner PD (1993) Adaptive thresholding for the digital desk. Tech. Rep. EPC-93-110, EuroPARC
**an G (2010) An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst Appl 37(10):6737–6741
**ao G, Brady M, Noble JA, Zhang Y (2002) Segmentation of ultrasound B-mode images with intensity inhomogeneity correction. IEEE Trans Med Imaging 21(1):48–57
Yoshida H, Keserci B, Casalino D, Coskun A, Ozturk O, Savranlar A (1998) Segmentation of liver tumors in ultrasound images based on scale-space analysis of the continuous Wavelet transform. In: Proc. of IEEE Ultrasonics symposium, 1713–1716
Zhang Q, Huang C, Li C, Yang L, Wang W (2012) Ultrasound image segmentation based on multi-scale fuzzy c-means and particle swarm optimization. IET Int Conf Inf Sci Control Eng 2012(636):1–5
Zhang D, Zhou J, Yang Y, Qin Q (2012) Automatic segmentation of liver tumor ultrasound images based on GGVF snake. In: Proc. Symposium on Photonics and Optoelectronics
Acknowledgments
This work was supported in part by Supported by the National High Technology Research and Development Program of China (863 Program)under grant No.2015AA020504 and the National Natural Science Foundation of China under grant No. 61473025, the Fundamental Research Funds for the Central Universities (YS1404) and the open-project grant funded by the State Key Laboratory of Synthetical Automation for Process Industry at the Northeastern University in China.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhu, H., Zhuang, Z., Zhou, J. et al. Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization. Multimed Tools Appl 76, 8951–8968 (2017). https://doi.org/10.1007/s11042-016-3486-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-3486-z