Detection and Segmentation of Kidney from Ultrasound Image Using GVF

  • M. P. PawarEmail author
  • P. S. Doshi
  • R. R. Shinde
Conference paper


The ultrasound imaging technique is used to identify abnormalities of the kidney. The kidney may contain abnormalities, e.g. kidney swelling, change in its position and appearance. Kidney abnormality may also appear due to cysts, cancerous cells, congenital anomalies, blockage of urine, etc. To operate abnormal kidney, it is required to detect the exact and accurate place of cyst in the kidney. The ultrasound images have low contrast and mainly contain speckle noise which creates a challenging task in kidney abnormalities detection. Thus preprocessing of ultrasound images is an important task to remove speckle noise. In preprocessing, to reduce speckle noise Gaussian filter is applied the resultant image is enhanced using histogram equalization. The preprocessed ultrasound image is segmented using Gradient Vector Flow (GVF) segmentation. This external force (i.e., GVF) was calculated gradient vectors of gray level and binary edge map.


Ultrasounds imaging Kidney cyst detection Edge detection Gradient vector flow Image segmentation 


  1. 1.
    Skounakis E, Banitsas K, Badii A, Tzoulakis S, Maravelakis E, Konstantaras A (2014) ATD: a multiplatform for semiautomatic 3-D detection of kidneys and their pathology in real time. IEEE Trans Hum-Mach Syst 44(1):146–153CrossRefGoogle Scholar
  2. 2.
    Viswanathand JK, Gunasundari R (2014) Design and analysis performance of kidney stone detection from ultrasound image by level set segmentation and ANN classification, In: International conference on advances in computing, communications and informatics (ICACCI), 978-1-4799-3080-7114/2014 IEEE 2014Google Scholar
  3. 3.
    Prevost R, Mory B, Correas J, Cohen LD, Ardon R (2012) Kidney detection and real-time segmentation in 3D contrast-enhanced ultrasound images, In: Proceeding of 9th IEEE international symposium on biomedical imaging ISBI, Barcelona, Spain, pp 15591562Google Scholar
  4. 4.
    Akkasaligar PT, Biradar S (2014) Classification of medical ultrasound images of kidney. Int J Comput Appl (0975 8887). International conference on information and communication technologies (ICICT – 2014)Google Scholar
  5. 5.
    Shruthi B, Renukalatha S, Siddappa M (2015) Speckle noise reduction in ultrasound images – a review. Int J Adv Res Comput Sci Softw Eng 5(2):251–255. ISSN: 2277 128XGoogle Scholar
  6. 6.
    Ambardar S, Singhal M (2014) A review and comparative study of de-noising filters in ultrasound imaging. Intl J Emerg Technol Adv Eng 4(8):824–831. Website: (ISSN 2250-2459, ISO 9001:2008 certified journal)Google Scholar
  7. 7.
    Shameena N, Jabbar R (2014) A study of preprocessing and segmentation techniques on cardiac medical imaging. Int J Eng Res Technol 3(4). ISSN2278-0181Google Scholar
  8. 8.
    Haralick RM, Shapiro LG (1985) Survey image segmentation techniques. Comput Vis Graph Image Process 29:100–132CrossRefGoogle Scholar
  9. 9.
    Mendoza CS, Kang X, Safdar N, Myers E, Peters CA, Linguraru MG (2013) Kidney segmentation in ultrasound via initialization and active shape models with rotation correction. IEEE Int Symp Biomed Imaging:69–72Google Scholar
  10. 10.
    Xu C, Student Member, IEEE, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369MathSciNetCrossRefGoogle Scholar
  11. 11.
    kop AM, Hegadi R (2010) Kidney segmentation from ultrasound images using gradient vector force. IJCA Spec Issue 2:104–109Google Scholar
  12. 12.
  13. 13.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.SVERI COESolapurIndia

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