An Efficient Method for Hepatic Cyst Segmentation on Ultrasound Images

  • Ishani kapoor Electrical and Instrumentation Engineering Department, Thapar University, Patiala (Punjab), India
  • Deepti Mittal Electrical and Instrumentation Engineering Department, Thapar University, Patiala (Punjab), India
Keywords: Hepatic cyst, Image processing, Image segmentation, Ultrasound images, Active contour.

Abstract

Hepatic cysts are usually rare complications but when they occur they may be fatal. Thus the radiologist should be able to monitor the cyst regularly and for doing so they need some parameters to monitor. The size of the cyst being one of the parameters that can be monitored after the segmentation of the cysts. The present work is focused on the designing of a modified method that can segment hepatic cyst on ultrasound images. This proposed method is designed by modification in geometric method proposed by Chan and Vese [1] which is combination of active contour and level set method. The work is performed on seventeen clinically acquired ultrasound images, which comprises of 6 small, 5 large, 4 multiple and 2 atypical type cyst images. The modified method is validated by both qualitative and quantitative criteria. The performance of proposed method can be expressed in terms of accuracy, sensitivity and specificity showing 97.9%, 77.3% and 90.7% for small cysts, 94.1%, 68.0% and 80.0% for large cysts, 98.2%, 96.3% and 98.5%for multiple cysts and for 95.7%,  68.9% and 91.3% atypical cysts respectively. The results demonstrate that the modified method can segment effectively hepatic cyst on ultrasound images and therefore can be used by the radiologist for diagnostic purposes.

References

(1) Horton KM, Bluemke DA, Hruban RH et-al. CT and MR imaging of benign hepatic and biliary tumors. Radiographics. 19 (2): 431-51. Radiographics (full text) - Pubmed citation.

(2) Mortelé KJ, Ros PR. Cystic focal liver lesions in the adult: differential CT and MR imaging features. Radiographics. 21 (4): 895-910. Radiographics (full text) - Pubmed citation.

(3) Jackson HH, Mulvihill SJ. Hepatic cysts. Available at http://emedicinemedscape.com /article/190818-overwiew. Updated March 11 2010.

(4) Niall Power, FRCR, MRCPI; Clare Bent, MRCS; Otto Chan, FRCR, FRCS, Imaging of cystic liver lesions in the adult. Available at http://appliedradiology.com/articles/imaging-of-cystic-liver-lesions-in-the-adult.

(5) M. Kass, A. Witkin, D. Terzopoulos, “Snakes: Active contour models,”International Journal of Computer Visio, vol. 1, pp. 321-331, 1988.

(6) C. Xu and J. L. Prince, “Snakes, Shapes, and Gradient Vector Flow,”IEEE Transactions on Image Processing, vol. 7(3), pp. 359-369, 1998.

(7) D. Mittal, V. kumar, S.C. Sexena, N. Khandelwal, N. Kalra, “ Neural Network Based Focal Liver Lesion Diagnosis using Ultrasound Images,” Computerized Medical Imaging and Graphics, Vol. 35, No. 4, pp 315-325, 2011.

(8) D. Mittal, V. Kumar, S.C. Sexena, N. Khandelwal, N. Kalra “

Enhancement of the Ultrasound Images by Modified Diffusion Method,” Medical and Biomedical Engineering and Computing, Vol. 48, No. 12, pp 281-1291, 2010.

(9) P. Bharti, D. Mittal, R. Ananthasivan, “Computer-Aided characterization and Diagnosis of Diffused Liver Diseases Based on Ultrasound Imaging A Review,” Ultrasonic Imaging, doi: 10.1177/0161734616639875, 2016.

(10) D. Mittal, “Impact of Modified Anisotopic Diffusion-Basd Enhancement method in Computer-Aided Classification of Focal Liver Lesion,” Ultrasonic Imaging, doi: 10.1177/0161734616654933, 2016.

(11) D. Mittal, A. Rani, Ritambhara, “ Detection and Classification of Focal Liver Lession using Support Vector Machine Classifiers,” Biomedical Imaging and Medical Imaging, Vol. 3, No. 1, pp 21-34, 2016.

(12) J. Ueda, H. Yoshida, N. Tanial, S. Minta, Y. Kawano, E. Uchida, “A Case of Spontaneous Rupture of A Simple Hepatic Cyst,” Nippon Med Sch, Vol. 77, No. 3, pp-181-185, 2013.

(13) Y. Marion, C. Brevart, L. Plard, L. Chiche, “Hemorrhagic liver cyst rupture: An unusual life threatening complication of hepatic cyst and literature review,” Annals of Heptology, Vol. 12, No. 2, pp 336-339, 2013.

(14) M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” Int. J. Comput. Vis., vol. 1, pp. 321–331, 1988.

(15) S. Osher and J. A. Sethian, “Fronts propagating with

curvature dependent speed: Algorithms based on Hamilton–Jacobi Formulation,” J. Comput. Phys., vol. 79, pp. 12–49, 1988.

(16) D. Mumford and J. Shah, “Optimal approximation by piecewise smooth functions and associated variational problems,” Commun. Pure Appl. Math, vol. 42, pp. 577–685, 1989.

(17) J. Jeon, J. Choi, S. Lee, Y. Ro, “Multiple ROI selection based focal liver lesion classification in ultrasound images,” Expert Systems with Applicatons, Vol. 40, N0. 2, pp 450-457, 2011

(18) S. Milko , E. Samset, T. Kadir, “Segmentation of the liver in ultrasound: a dynamic texture approach,” International Journal of Computer Assisted Radiology and Surgery, doi: 10.1007/s11548-0217-6

(19) C.M. Chen, H.H.S. Lu, YS Huang, “Cell-based dual snake model: a new approach to extracting highly winding boundaries in the ultrasound images”. Ultrasound in Medicine and Biology, Vol. 28, No. 8, pp 1061-1073, 2002.

(20) P. S. Hiremath and J. R. Tegnoor. “Automatic detection of follicles in ultrasound images of ovaries using edge based method,” IJCA special issue on “Recent Trends in Image Processing and Pettern Recognition” RTIPPR, 2010

(21) T.F. Chan, L.A. Vese, “Active Contours Without Edges,” IEEE Transactions on Image Processing, Vol.10, N0. 1, 2001.

(22) J. S. Weszka, R. N. Nagel, and A. Rosenfeld, "A threshold selection technique." IEEE Trans. Comput., vol. C-23, pp. 1322 -1326, 1974

(23) S. Watanabe and CYBEST Group. "An automated apparatus for cancer Pre-screening: CYBEST," Comp. Graph. Image Process. vol. 3. pp. 350--358, 1974.

(24) C. K. Chow and T. Kaneko, "Automatic boundary detection

of the left ventricle from cineangiograms," Comput. Biomed. Res., vol. 5, pp. 388- 410, 1972.

(25) Xu, J., Chen, K., Yang, X., Wu, D., Zhu, S. Adaptive level

set method for segmentation of liver tumors in minimally invasive surgery using ultrasound images. In: Bioinformatics and Biomedical Engineering. ICBBE 2007. The 1st International Conference on, 2007. IEEE, pp. 1091–1094, 2007.

Published
2016-12-30
How to Cite
kapoor, I., & Mittal, D. (2016). An Efficient Method for Hepatic Cyst Segmentation on Ultrasound Images. Journal of Biomedical Engineering and Medical Imaging, 3(6), 55. https://doi.org/10.14738/jbemi.36.2550