Abstract
Computer-aided systems are extensively used in the recent medical field for diagnostics. Medical imaging, particularly ultrasonography is the most commonly used primary diagnostic tool by the medical experts. The segmentation and analysis of region of interest in ultrasound images is a difficult task due to the shape variant objects, orientation, and poor quality. Existing segmentation algorithms need an initial seed point as user input. In this chapter, a novel approach for automatic kidney cysts segmentation in digital ultrasound images is proposed. Initially, contourlet transform is used for preprocessing of kidney ultrasound images and contrast enhancement is performed by using histogram equalization. Initial contour for segmentation closer to the actual cysts boundaries is obtained automatically using morphological operations. Complete automation of active contour method and level set segmentation method is mainly addressed in the work proposed. Further, the diagnostic parameters essentially needed by medical experts, namely, the number of cysts and the size of each cyst, are calculated. The developed method is evaluated using performance parameters such as Jaccard coefficient, Dice, sensitivity, specificity, and accuracy. The efficacy of the developed approach is proved by the results of the experiment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hagen-Ansert S. Urinary system. Diagnostic ultrasonography-diagnostic ultrasound. St. Louis: Mosby; 1995.
Pollack HM, McClennan BL. Clinical urography. 2nd ed. Reading: W.B. Saunders; 2000. p. 1817–23.
Maulik U. Medical image segmentation using genetic algorithms. IEEE Trans Inform Tecnol Biomed. 2009;13(2):166–73.
Xie J, Jiang Y, Tsui H. Segmentation of kidney from ultrasound images based on texture and shape. Prioris IEEE Trans Med Imag. 2005;24(1):45–57.
Raja KB, Madheswaran M, Thyagarajah K. Quantitative and qualitative evaluation of US kidney images for disorder classification using multi-scale differential features. ICGST-BIME J. 2000;7:1):1–8.
Mendoza CS, Kang X, Safdar N, Myers E, Peters CA, Linguraru MG. Kidney segmentation in ultrasound via genetic initialization and active shape models with rotation correction. Proc IEEE Symp Biomed Imag (ISBI’13) 2013; 69–72.
Raja KB, Madheswaran M, Thyagarajah K. A general segmentation scheme for contouring kidney region in ultrasound kidney images using improved higher order spline interpolation. Int J Biological and Life Sci. 2007;2(2):81–8.
Akkasaligar Prema T, Biradar S. Classification of medical ultrasound images of kidney. Int J Comput Appl 2014. (ISSN:0975–8887). Special Issue on ICICT. 2014:32–6.
Noble JA, Boukerroui D. Ultrasound image segmentation: a survey. IEEE Trans Med Imag. 2006;25(8):987–1010.
Meiburger KM, Acharya UR, Molinari F. Automated localization and segmentation techniques for B-mode ultrasound images: a review. Comput Biol Med. 2018;92:210–35.
Huang J, Yang H, Chen Y, Tang L. Ultrasound kidney segmentation with a global prior shape. J Vis Commun Image Rep. 2013;24:937–43.
Spiegel M, Dieter AH, Volker D, Jakob W, Joachi H. Segmentation of kidney using a new active shape model generation technique based on non-rigid image registration. J Comput Med Imag Graph. 2009;33:29–39.
Hafizah WM, Supriyanto E. Automatic generation of region of interest for kidney ultrasound images using texture analysis. Int J Biol Biomed Eng. 2012;6(1):1289–305.
Jeyakumar V, Hasmi MK. Quantitative analysis of segmentation methods on ultrasound kidney image. Int J Adv Res Comput Commun Eng. 2013;2(5):2319–5940.
Burckhardt C. Speckle in ultrasound B- mode scans. IEEE Trans Sonics Ultrason. 1978;25:1–6.
Hiremath PS, Akkasaligar Prema T, Badiger S. Speckle reducing Contourlet transform for medical ultrasound images. World Acad Sci Eng Technol Special J Issue. 2011;80:1217–24.
Agarwal T, et al. Modified histogram based contrast Enhancementusing Homomorphic filtering for medical images. Paper presented at International Advance Computing Conference (IACC) 2014 Gurgaon, New Delhi, India,21st-22nd Feb 2014, p 964–968.
Sonka Milan VH, Roger B. Image processing, analysis and machine vision, 3rd ed. Cengage Learning, Boston. 2013:630–5.
Cohen LD, Cohen I. A finite element method applied to new active contour models and 3D reconstruction from cross sections. Paper presented at the 3rd international conference on computer vision, Osaka, Japan, 1990; p 587–591.
Chan T, Vese L. Active contours without edges. IEEE Trans Image Proc. 2001;10(2):266–77.
Sussman M, Smereka P, Osher S. A level set approach for computing solutions to incompressible two-phase flow. J Comput Phys. 1994;114(1):146–59.
Osher S, Fedkiw R. Level set methods and dynamic implicit surfaces. New York: Springer; 2002.
Udupa JK, LeBlanc VR. Methodology for evaluating image segmentation algorithms SPIE medical imaging; 2002.
Li C, Xu C, Gui C, Fox MD. Level set evolution without re-initialization: A new variational formulation. IEEE Trans Image Process. 2010;19(12):3243–54.
Cerrolaza J, et al. Quantification of kidneys from 3D ultrasound in pediatric hydronephrosis. IEEE International Symp. 2015:157–60.
Akkasaligar Prema T, Biradar S. Analysis of polycystic kidney disease in medical ultrasound images. Int J Med Eng Inform. 2018;10(1):49–64.
Acknowledgements
The authors are thankful to Dr. Bhushita B. Lakhkar, Radiologist, BLDEDU’s Shri. B. M. Patil Medical College Hospital and Research Centre, Vijayapur for providing USG image set of kidney. Authors are also thankful to Dr. Vinay Kundaragi, Nephrologist, BLDEDU’s Shri. B. M. Patil Medical College Hospital and Research Centre, Vijayapur for rendering manual segmentation of images. This work is financially supported by Vision Group of Science and Technology (VGST), Government of Karnataka under RGS/F scheme.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Akkasaligar, P.T., Biradar, S. (2019). Automatic Kidney Cysts Segmentation in Digital Ultrasound Images. In: Shukla, A. (eds) Medical Imaging Methods. Springer, Singapore. https://doi.org/10.1007/978-981-13-9121-7_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-9121-7_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9120-0
Online ISBN: 978-981-13-9121-7
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)