GHT based automatic kidney image segmentation using modified AAM and GBDT

  • R. Amala RoseEmail author
  • A. Annadhason
Original Paper
Part of the following topical collections:
  1. Internet Of Medical Things In E-Health


These days age development to be more prominent in biometric angle. Particularly CAD machine is essentially renowned for ordering and division. In this theory Kidney issue and the division systems are examined. The kidney inconvenience recognizable proof and the finding in logical region are well focused on kidney’s external layer. Accordingly, the inward inconvenience isn’t yet considered in each case. This is mulled over and another time is produced in this examination for division of Kidney using GBDT (Gradient Boosting Decision Tree) thought. The exploration managed with a novel proficient componentThis research was handled with novel efficient mechanism named as GBDT. A systematic technique termed GBDT was utilized to enhance the predictive model. In the process of renal cortex phase localization, a technique which integrates Generalized Hough Transform (GHT) with Active Appearance Models (AAM) was enforced for kidney localization to appraise the renal cortex thickness. The AAM method always matches a new data to the appearance model by minimizing the intensity of root mean square (RMS) between the new data as well as appearance model instance. And finally, from the result of the localization phase, the proposed method GBDT was employed to segregate the kidney into various components. Then an accumulator matrix indicating the possible position of an object was constructed pursuant to the R-table, where the training set data is normally used in this form of table. The results were evaluated to reveal the higher achievement of the proposed system.


Kidney classification CAD Biometric Segmentation AAM GBDT Kidney renal cortex Renal column Renal medulla and renal pelvis 


Compliance with ethical standards

Conflict of interest

None of the author received fund from any agencies or committee or organization.


  1. 1.
    Source: Summary Health Statistics for U.S. Adults: National Health Interview Survey,
  2. 2.
    Wouters OJ, O'donoghue DJ, Ritchie J, Kanavos PG, Andrew S. Narva, ―early chronic kidney disease: diagnosis, management and models of care. Nat Rev Nephrol. 2015;11(8):491–502.CrossRefGoogle Scholar
  3. 3.
    He JC, Chuang PY, Ma'Ayan A, Ravi Iyengar. Systems biology of kidney diseases. Kidney Int. 2012;81(1):22–39.CrossRefGoogle Scholar
  4. 4.
    Rusinek H, Lim JC, Wake N, Seah J-m, Botterill E, Farquharson S, et al. A semi-automated “blanket” method for renal segmentation from non-contrast T1-weighted MR images. MAGMA. 2016;29(2):197–206.CrossRefGoogle Scholar
  5. 5.
    Shi F, Chen X, Zhao H, Zhu W, Xiang D, Gao E, et al. Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments. IEEE Trans Med Imaging. 2015;34(2):441–52.CrossRefGoogle Scholar
  6. 6.
    Balakrishnan K, Deng J, Varshney VK. TWOACK: Preventing selfishness in mobile ad hoc networks. In: Wireless communications and networking conference, 2005 IEEE. 2005;4: 2137–2142.Google Scholar
  7. 7.
    Yang X, Yu L, Wu L, Wang Y, Ni D, Qin J, Heng P-A. Fine-grained recurrent neural networks for automatic prostate segmentation in ultrasound images. In AAAI. 2017;1633-1639.Google Scholar
  8. 8.
    Gloger O, Tonnies K, Laqua R, et al. Fully automated renal tissue volumetry in MR volume data using prior shape based segmentation in proband-specific probability maps. IEEE Trans Biomed Eng. 2015;62(10):2338–51.CrossRefGoogle Scholar
  9. 9.
    Gloger O, Tonnies K. Subject-specific prior shape knowledge ¨ in feature-oriented probability maps for fully automatized liver segmentation in MR volume data. Pattern Recogn. 2018;84:288–300.CrossRefGoogle Scholar
  10. 10.
    Yaqub M, Javaid M, Cooper C, Noble J. Investigation of the role of feature selection and weighted voting in random forests for 3-D volumetric segmentation. IEEE Trans Med Imag. 2014;33:258–71.CrossRefGoogle Scholar
  11. 11.
    Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp. 556–564, 2015.Google Scholar
  12. 12.
    Van Gastel MDA, Messchendorp AL, Kappert P, Kaatee MA, de Jong M, Renken RJ, et al. Gansevoort, and DIPAK Consortium, T1 vs. T2 weighted magnetic resonance imaging to assess total kidney volume in patients with autosomal dominant polycystic kidney disease. Abdominal Radiology. 2018;43(5):1215–22.CrossRefGoogle Scholar
  13. 13.
    Xie L, Koukos G, Barck K, Foreman O, Lee WP, Brendza R, Eastham-Anderson J, McKenzie BS, Peterson A, Carano RAD. Micro-CT imaging and structural analysis of glomeruli in a model of Adriamycin-induced nephropathy, American Journal of Physiology-Renal Physiology. 2018.Google Scholar
  14. 14.
    Blankholm AD, Pedersen BG, Østrat EØ, Andersen G, Stausbøl-Grøn B, Laustsen S, et al. Noncontrast-enhanced magnetic resonance versus computed tomography angiography in preoperative evaluation of potential living renal donors. Acad Radiol. 2015;22(11):1368–75.CrossRefGoogle Scholar
  15. 15.
    Yaqub M, Mahon P, Javaid MK, Cooper C, Noble JA. Weighted voting in 3d random forest segmentation. Warwick: Proc. Medical Image Understanding and Analysis; 2010. p. 261–6.Google Scholar
  16. 16.
    Beland MD, Walle NL, Machan JT, Cronan JJ. Renal cortical thickness measured at ultrasound: is it better than renal length as an indicator of renal function in chronic kidney disease? Am J Roentgenol. 2010;195(2):W146–9.CrossRefGoogle Scholar
  17. 17.
    Chen X, Summers RM, Cho M, Bagci U, Yao J. An automatic method for renal cortex segmentation on CT images: evaluation on kidney donors. Acad Radiol. 2012;19(5):562–70.CrossRefGoogle Scholar
  18. 18.
    Jin C, Shi F, Xiang D, Jiang X, Zhang B, Wang X, et al. 3D fast automatic segmentation of kidney based on modified AAM and random forest. IEEE Trans Med Imaging. 2016;35(6):1395–407.CrossRefGoogle Scholar
  19. 19.
    Xie J, Jiang Y, Hung-tat Tsui. Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans Med Imaging. 2005;24(1):45–57.CrossRefGoogle Scholar
  20. 20.
    Nagappan VK. 3D fast automatic segmentation of kidney based on modified AAM and random forest. Asia Pacific Journal of Research. 2018; I(LXXXVII).Google Scholar
  21. 21.
    BalaAnand M, Karthikeyan N, Karthik S. Designing a framework for communal software: based on the assessment using relation modelling. Int J Parallel Prog. 2018.
  22. 22.
    BalaAnand M, Sankari S, Sowmipriya R, Sivaranjani S. Identifying Fake User’s in Social Networks Using Non Verbal Behavior. International Journal of Technology and Engineering System (IJTES). 7(2):157–61.Google Scholar
  23. 23.
    Maram B, Gnanasekar JM, Manogaran G, et al. SOCA. 2018. 10.1007/s11761-018-0249-x.Google Scholar
  24. 24.
    BalaAnand M, Karthikeyan N, Karthick S, Sivaparthipan CB. Demonetization: a visual exploration and pattern identification of people opinion on tweets. In: 2018 International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India, 2018, pp. 1–7. 10.1109/ICSNS.2018.8573616.Google Scholar
  25. 25.
    Anupriya K, Gayathri R, Balaanand M, Sivaparthipan CB. Eshopping scam identification using machine learning. In: 2018 International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, 2018, pp. 1–7. 10.1109/ICSNS.2018.8573687.Google Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceSt. John’s College of Arts And ScienceKanyakumariIndia
  2. 2.MS UniversityTirunelveliIndia
  3. 3.Department of Computer ScienceGovernment Arts & Science CollegeRamanathapuramIndia

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