Prediction of cirrhosis disease from radiologist liver medical image using hybrid coupled dictionary pairs on longitudinal domain approach

  • J. KirubakaranEmail author
  • G. K. D. Prasanna Venkatesan
  • S. Baskar
  • M. Kumaresan
  • S. Annamalai


This paper presents a novel algorithm for the liver diseases fibrosis called Cirrhosis, which is considered as the most communal diseases in healthcare research. This research work introduced a technique for discriminating the cirrhotic liver from normal liver through adaptive ultrasound (AUS) instead of ultrasound (US) images with Hybrid Coupled Dictionary Pairs on Longitudinal Domain (HCDPLD). The parameters such as region covered and data structure values or variables has been analyzed using heuristic pattern producing classifierfor identifying the sub-bands and edge features. The developed cirrhosis prediction strategy helps to improve the results of image resolution with the accuracy of 99.82%, Average Peak Signal to Noise Ratio (PSNR) of 3.22 dB and Structural Similarity Index (SSIM) of 0.89 through HCDPLD when compared with existing counterparts. Further Ingestible Internet of Things (IoT) sensors with activity tracker helps to monitor the patient health accurately in reliable data transfer.


Cirrhosis Internet of things (IoT) Ultrasound Region of interest PSNR SSIM 



  1. 1.
    Acharya UR, Faust O, Molinari F, Sree SV, Junnarkar SP, Sudarshan V (2015) Ultrasound-based tissue characterization and classification of fatty liver disease: a screening and diagnostic paradigm. Knowl-Based Syst 75:66–77CrossRefGoogle Scholar
  2. 2.
    Akinyemiju T, Abera S, Ahmed M, Alam N, Alemayohu MA, Allen C, … Ayele TA (2017) The burden of primary liver cancer and underlying etiologies from 1990 to 2015 at the global, regional, and national level: results from the global burden of disease study 2015. J Am Med Assoc (JAMA) Oncology 3(12):1683–1691Google Scholar
  3. 3.
    Arshad I, Dutta C, Choudhury T, Thakral A (2018) Liver disease detection due to excessive alcoholism using data mining techniques. In: 2018 international conference on advances in computing and communication engineering (ICACCE). IEEE, pp 163–168Google Scholar
  4. 4.
    Chen Y, Yue X, Fujita H, Fu S (2017) Three-way decision support for diagnosis on focal liver lesions. Knowl-Based Syst 127:85–99CrossRefGoogle Scholar
  5. 5.
    Crocetti L (2018) Radiofrequency ablation versus resection for small hepatocellular carcinoma: are randomized controlled trials still needed? Radiology 287:473–475CrossRefGoogle Scholar
  6. 6.
    Fujimoto K, Kato M, Kudo M, Yada N, Shiina T, Ueshima K, … Yamamoto K (2013) Novel image analysis method using ultrasound elastography for noninvasive evaluation of hepatic fibrosis in patients with chronic hepatitis C. Oncology 84(Suppl. 1):3–12Google Scholar
  7. 7.
    Gane EJ., Pianko S, Roberts SK, Thompson AJ, Zeuzem S, Zuckerman E, … Gerstoft J (2017) Safety and efficacy of an 8-week regimen of grazoprevir plus ruzasvir plus uprifosbuvir compared with grazoprevir plus elbasvir plus uprifosbuvir in participants without cirrhosis infected with hepatitis C virus genotypes 1, 2, or 3 (C-CREST-1 and C-CREST-2, part a): two randomised, phase 2, open-label trials. The Lancet Gastroenterology & Hepatology (LGH) 2(11):805–813Google Scholar
  8. 8.
    Gao S, Peng Y, Guo H, Liu W, Gao T, Xu Y, Tang X (2014) Texture analysis and classification of ultrasound liver images. Biomed Mater Eng 24(1):1209–1216Google Scholar
  9. 9.
    Guirguis-Blake JM, Beil TL, Senger CA, Whitlock EP (2014) Ultrasonography screening for abdominal aortic aneurysms: a systematic evidence review for the US preventive services task force. Ann Intern Med 160(5):321–329CrossRefGoogle Scholar
  10. 10.
    Gunasundari S, Janakiraman S (2013) A study of textural analysis methods for the diagnosis of liver diseases from abdominal computed tomography. Int J Comput Appl 74(11)Google Scholar
  11. 11.
    Huguet A, Latournerie M, Debry PH, Jezequel C, Legros L, Rayar M., … Thibault R (2018) The psoas muscle transversal diameter predicts mortality in patients with cirrhosis on a waiting list for liver transplantation: a retrospective cohort study. Nutrition 51:73–79Google Scholar
  12. 12.
    Iizuka S, Simo-Serra E, Ishikawa H (2017) Globally and locally consistent image completion. ACM Trans Graph (TOG) 36(4):107CrossRefGoogle Scholar
  13. 13.
    Islam SR, Kwak D, Kabir MH, Hossain M, Kwak KS (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708CrossRefGoogle Scholar
  14. 14.
    Jiang J, Ma X, Chen C, Lu T, Wang Z, Ma J (2017) Single image super-resolution via locally regularized anchored neighborhood regression and nonlocal means. IEEE Journal of Transactions on Multimedia 19(1):15–26CrossRefGoogle Scholar
  15. 15.
    Labranche R, Gilbert G, Cerny M, Vu KN, Soulières D, Olivié D, … Tang A (2018) Liver iron quantification with MR imaging: a primer for radiologists. RadioGraphics 38(2):392–412Google Scholar
  16. 16.
    Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A., … Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR, vol 2, no 3, pp 4Google Scholar
  17. 17.
    Lee S, Nam Y, Jang J, Na GH, Kim DG, Shin NY, … Kim BS (2018) Deep gray matter iron measurement in patients with liver cirrhosis using quantitative susceptibility mapping: relationship with pallidal T1 hyperintensity. J Magn Reson Imaging 47(5):1342–1349Google Scholar
  18. 18.
    Manogaran G, Shakeel PM, Hassanein AS, Priyan MK, Gokulnath C (2018) Machine-learning approach based gamma distribution for brain abnormalities detection and data sample imbalance analysis. IEEE Access.
  19. 19.
    Preeth SKSL, Dhanalakshmi R, Kumar R, Shakeel PM (2018) An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. J Ambient Intell Humaniz Comput:1–13.
  20. 20.
    Quesada R, Poves I, Berjano E, Vilaplana C, Andaluz A, Moll X, … Burdio F (2017) Impact of monopolar radiofrequency coagulation on intraoperative blood loss during liver resection: a prospective randomised controlled trial. Int J Hyperth 33(2):135–141Google Scholar
  21. 21.
    Ricchi P, Meloni A, Spasiano A, Costantini S, Pepe A, Cinque P, Filosa A (2018) The impact of liver steatosis on the ability of serum ferritin levels to be predictive of liver iron concentration in non-transfusion-dependent thalassaemia patients. Br J Haematol 180(5):721–726CrossRefGoogle Scholar
  22. 22.
    Shakeel PM, Manogaran G (2018) Prostate cancer classification from prostate biomedical data using ant rough set algorithm with radial trained extreme learning neural network. Heal Technol:1–9.
  23. 23.
    Shakeel PM, Baskar S, Dhulipala VS, Mishra S, Jaber MM (2018) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42(10):186. CrossRefGoogle Scholar
  24. 24.
    Shakeel PM, Baskar S, Dhulipala VS, Jaber MM (2018) Cloud based framework for diagnosis of diabetes mellitus using K-means clustering. Journal of Health Informatic Science and Systems 6(1):16CrossRefGoogle Scholar
  25. 25.
    Sprague BL, Stout NK, Schechter C, Van Ravesteyn NT, Cevik M, Alagoz O, De Koning HJ (2015) Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts. Ann Intern Med 162(3):157–166CrossRefGoogle Scholar
  26. 26.
    Sridhar KP, Baskar S, Shakeel PM, Dhulipala VS (2018) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Humaniz Comput:1–9.
  27. 27.
    Valerio M, Donaldson I, Emberton M, Ehdaie B, Hadaschik BA, Marks LS, … Ahmed HU (2015) Detection of clinically significant prostate cancer using magnetic resonance imaging–ultrasound fusion targeted biopsy: a systematic review. Eur Urol 68(1):8–19Google Scholar
  28. 28.
    Wong VWS, Chan WK, Chitturi S, Chawla Y, Dan YY, Duseja A, … Kim SU (2018) Asia–pacific working party on non-alcoholic fatty liver disease guidelines 2017—part 1: definition, risk factors and assessment. J Gastroenterol Hepatol 33(1):70–85Google Scholar
  29. 29.
    Wu CC, Lee WL, Chen YC, Hsieh KS (2013) Evolution-based hierarchical feature fusion for ultrasonic liver tissue characterization. IEEE J Biomed Health Inform 17(5):967–976CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • J. Kirubakaran
    • 1
    Email author
  • G. K. D. Prasanna Venkatesan
    • 2
  • S. Baskar
    • 3
  • M. Kumaresan
    • 4
  • S. Annamalai
    • 4
  1. 1.Department of ECEMuthayammal Engineering College (Autonomous)NamakkalIndia
  2. 2.Karpagam Academy of Higher EducationCoimbatoreIndia
  3. 3.Department of Electronics and CommunicationKarpagam Academy of Higher EducationCoimbatoreIndia
  4. 4.School of Computing Science and EngineeringGalgotias UniversityGreater NoidaIndia

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