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Optimal GLCM combined FCM segmentation algorithm for detection of kidney cysts and tumor

  • Paladugu RajuEmail author
  • Veera Malleswara Rao
  • Bhima Prabhakara Rao
Article
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Abstract

In this document, we employed an efficient Optimal GLCM attribute related FCM segmentation algorithm which is used to categorize the kidney cysts and tumor from the ultrasound kidney images. The FCM is exploiting some appropriate attributes of GLCM texture feature extractor and optimally attach the cluster centroids of FCM by the help of Whale optimization algorithm. The proposed approach is executed in the working platform of Matlab. The findings demonstrate that the proposed model have better performance in recognizing the detection of kidney cysts and tumor in patients by examining US kidney images. Also, we have shown the comparison of our proposed method FB-FCM-WOA with the existing methodologies like FB-FCM, FB-K-means, IB-FCM and IB-K-means. Hence, we would suggest that our proposed method is much better for detecting kidney cysts and tumor.

Keywords

Feature based Fuzzy C-means (FbFCM) Whale Optimization algorithm (WOA) Gray Level Co-occurrence Matrix (GLCM) Ultrasound (US) kidney tumor and cyst segmentation 

Notes

References

  1. 1.
    Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 22(1):1–15CrossRefGoogle Scholar
  2. 2.
    Attia MW et al. (2015) "Classification of ultrasound kidney images using PCA and neural networks." IJACSA) International Journal of Advanced Computer Science and Applications 6.4Google Scholar
  3. 3.
    Chao J, Shi F, Xiang D, Jiang X, Zhang B, Wang X, Zhu W, Gao E, Chen X (2016) 3D fast automatic segmentation of kidney based on modified AAM and random forest. IEEE Trans Med Imaging 35(6):1395–1407CrossRefGoogle Scholar
  4. 4.
    Ding M, Fan G (2016) Articulated and generalized gaussian kernel correlation for human pose estimation. IEEE Trans Image Process 25(2):776–789MathSciNetCrossRefGoogle Scholar
  5. 5.
    Divya KK, Akkala V, Bharath R, Rajalakshmi P, Mohammed AM, Merchant SN, Desai UB (2016) Computer aided abnormality detection for kidney on FPGA based IoT enabled portable ultrasound imaging system. Irbm 37(4):189–197CrossRefGoogle Scholar
  6. 6.
    Gayathri K, Vasanthi D (2017) Brain Tumor Segmentation Using K-Means Clustering and Fuzzy C-Means AlgorithmsGoogle Scholar
  7. 7.
    Goceri N, Goceri E (2015) "A neural network based kidney segmentation from MR images." Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on. IEEEGoogle Scholar
  8. 8.
    Guo L et al (2017) Image guided Fuzzy C-means for image segmentation. Int J Fuzzy Syst 19(6):1660–1669MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hong S, Kang W, Zhang Q, Wang S (2015) Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm. BMC Syst Biol 9(5):S5Google Scholar
  10. 10.
    Jin C et al (2017) Fast segmentation of kidney components using random forests and ferns. Med Phys 44(12):6353–6363CrossRefGoogle Scholar
  11. 11.
    Kairuddin WNHW, Mahmud WMHW (2017) Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images. IOP Conference Series: Materials Science and Engineering. Vol. 226. No. 1. IOP PublishingGoogle Scholar
  12. 12.
    Kaveh A, Ilchi Ghazaan M (2017) Enhanced whale optimization algorithm for sizing optimization of skeletal structures. Mech Based Des Struct Mach 45(3):345–362CrossRefGoogle Scholar
  13. 13.
    Khalifa F et al. (2016) A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images. Image Processing (ICIP), 2016 IEEE International Conference on. IEEEGoogle Scholar
  14. 14.
    Kirubha V, Manju Priya S (2016) Survey on Data Mining Algorithms in Disease Prediction. Int J Comput Trends Tech 38(3):24–128CrossRefGoogle Scholar
  15. 15.
    Ladumor DP et al. (2016) A whale optimization algorithm approach for unit commitment problem solution. Proc. National Conf. Advancement in Electrical & Power Electronics Engineering (AEPEE 2016), Morbi, IndiaGoogle Scholar
  16. 16.
    Lee, L-K, Liew S-C (2015) "A survey of medical image processing tools." In Software Engineering and Computer Systems (ICSECS), 2015 4th International Conference on, pp. 171–176. IEEEGoogle Scholar
  17. 17.
    Lin D-T, Lei C-C, Hung S-W (2006) Computer-aided kidney segmentation on abdominal CT images. IEEE Trans Inf Technol Biomed 10(1):59–65CrossRefGoogle Scholar
  18. 18.
    Mahdi M, Plataniotis KN, Stergiopoulos S (2017) An automated approach for kidney segmentation in three-dimensional ultrasound images. IEEE j biomed health inform 21(4):1079–1094CrossRefGoogle Scholar
  19. 19.
    Mahmud W, Hafizah WM (2013) Kidney Abnormality Detection and Classification Using Ultrasound Vector Graphic Image Analysis. Diss. Universiti Teknologi MalaysiaGoogle Scholar
  20. 20.
    Mary JM (2017) Image Segmentation Technique-A study on Region Growing ApproachesGoogle Scholar
  21. 21.
    Pawar MP, Mulla AN (2017) Design and Analysis Performance of Kidney Cyst Detection from Ultrasound ImagesGoogle Scholar
  22. 22.
    Pugazhenthi D (2016) "Breast Abnormalities Detection in Digital Mammogram Using Fuzzy C-Means Clustering and Support Vector Machine-Matlab Implementation." Breast 4.2Google Scholar
  23. 23.
    Sharawi M, Zawbaa HM, Emary E (2017) "Feature selection approach based on whale optimization algorithm." Advanced Computational Intelligence (ICACI), 2017 Ninth International Conference on. IEEEGoogle Scholar
  24. 24.
    Torres HR et al. (2018) "Kidney Segmentation in Ultrasound, Magnetic Resonance and Computed Tomography Images: A Systematic Review." Computer Methods and Programs in BiomedicineGoogle Scholar
  25. 25.
    Trivedi IN et al. (2016) Novel adaptive whale optimization algorithm for global optimization. Indian Journal of Science and Technology 9.38Google Scholar
  26. 26.
    Velmurugan V, Arunkumar M, Gnanasivam P (2017) A review on systemic approach of the ultra sound image to detect renal calculi using different analysis techniques. Biosignals, Images and Instrumentation (ICBSII), 2017 Third International Conference on. IEEEGoogle Scholar
  27. 27.
    Xie X, Liu S, Yang C, Yang Z, Xu J, Zhai X (2017) The application of smart materials in tactile actuators for tactile information delivery. arXiv preprint arXiv:1708.07077Google Scholar

Copyright information

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

Authors and Affiliations

  • Paladugu Raju
    • 1
    Email author
  • Veera Malleswara Rao
    • 2
  • Bhima Prabhakara Rao
    • 3
  1. 1.Department of ECEJNTUK KakinadaKakinadaIndia
  2. 2.Department of ECE, GITGITAM Deemed to be UniversityVisakhapatnamIndia
  3. 3.Programme Director, NanotechnologyJNTUK KakinadaKakinadaIndia

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