Gallbladder Shape Estimation Using Tree-Seed Optimization Tuned Radial Basis Function Network for Assessment of Acute Cholecystitis

  • V. Muneeswaran
  • M. Pallikonda Rajasekaran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


In this paper, computerized scheme for automatic volume estimation of inflamed gallbladder in ultrasound images has been investigated. Diagnosis of acute cholecystitis at an early stage is an arduous task as the difference between normal shape and inflamed gallbladder shape cannot be visualized in ultrasound images. This paper comes out with an unsupervised machine learning algorithm—tree-seed optimization algorithm tuned radial basis function network for segmentation of gallbladder in ultrasound images. Tree-seed optimization algorithm, which optimizes function and parameters in real values, is a population-based stochastic search algorithm. Prior to the classification, speckle reduction and feature extraction process were successfully used. These features are then used in classification process to define the gallbladder and non-gallbladder regions. The proposed optimized classifier system is evaluated in real-time clinical datasets with cholecystitis and cholelithiasis. The inherent differentiation of the proposed intelligent classifier is analyzed using standard evaluation parameters. Comparison with expert decisions provides further evidence that the optimally tuned radial basis function network has important implications for diagnosis of acute cholecystitis.


Tree-seed optimization Gallbladder segmentation Radial basis function network 



The authors thank Vijay Scans, Rajapalayam, Tamil Nadu, for supporting the research by providing ultrasound images and necessary patient information. Also we thank the Department of ECE, Kalasalingam University, Tamil Nadu, India, for permitting to use the computational facilities available in Centre for Research in Signal Processing and VLSI Design which was set up with the support of the Department of Science and Technology (DST), New Delhi, under FIST Program in 2013 (Reference No: SR/FST/ETI-336/2013 dated November 2013).


  1. 1.
    Abolmaesumi, P., Sirouspour, M.R.: An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images. IEEE Trans. Med. Imaging 23(6), 772–784 (2004)CrossRefGoogle Scholar
  2. 2.
    Ayala, H.V.H., Coelho, L.: Multiobjective Cuckoo Search Applied to Radial Basis Function Neural Networks Training for System Identification. IFAC Proc. 47(3), 2539–2544 (2014)CrossRefGoogle Scholar
  3. 3.
    Chang, W.Y.: An RBF neural network combined with OLS algorithm and genetic algorithm for short-term wind power forecasting. J. Appl. Math., 9 (2013). 10.1155/2013/971389. Article ID 971389
  4. 4.
    Chu, A., Sehgal, C.M., Greenleaf, J.F.: Use of gray value distribution of run lengths for texture analysis. Pattern Recogn. Lett. 11(6), 415–419 (1990)CrossRefGoogle Scholar
  5. 5.
    Ciecholewski, M.: Gallbladder boundary segmentation from ultrasound images using active contour model. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds.) Intelligent Data Engineering and Automated Learning IDEAL 2010. IDEAL 2010. LNCS, vol. 6283 , pp. 63–69. Springer, Berlin, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Das, N., Sarkar, R., Basu, S., Kundu, M., Nasipuri, M., Basu, D.K.: A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application. Appl. Soft Computing. 12(5), 1592–1606 (2012)CrossRefGoogle Scholar
  7. 7.
    Fathi, V., Montazer, G.A.: An improvement in RBF learning algorithm based on PSO for real time applications. Neurocomputing 111, 169–176 (2013)CrossRefGoogle Scholar
  8. 8.
    Kiran, M.S.: TSA: Tree-seed algorithm for continuous optimization. Expert Syst. Appl. 42(19), 6686–6698 (2015)CrossRefGoogle Scholar
  9. 9.
    Kiran, M.S.: An Implementation of Tree Seed Algorithm (TSA) for Constrained Optimization. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J.H. (eds.) IES 2015. PALO, vol. 5, pp. 189–197. Springer, Cham (2016)Google Scholar
  10. 10.
    LaRocca, C.J., Hoskuldsson, T., Beilman, G.J.: The use of imaging in gallbladder disease. In: Eachempati, S., Reed II, R. (eds.) Acute Cholecystitis, pp. 41–53. Springer, Cham (2015)CrossRefGoogle Scholar
  11. 11.
    Latifoglu, F.: A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: An ultrasound image application. Comput. Methods Program. Biomed. 111(3), 561–569 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Li, J., Liu, X., Jiang, H., Xiao, Y.: Melt index prediction by adaptively aggregated RBF neural networks trained with novel ACO algorithm. J. Appl. Polym. Sci. 125, 943–951 (2012). 10.1002/app.35688CrossRefGoogle Scholar
  13. 13.
    Montazer, G.A., Giveki, D.: An improved radial basis function neural network for object image retrieval. Neurocomputing 168, 221–233 (2015)CrossRefGoogle Scholar
  14. 14.
    Muneeswaran, V., Pallikonda Rajasekaran, M.: Performance evaluation of radial basis function networks based on tree seed algorithm. In: Proceeding of the 2016 International Conference of Circuit Power and Computing Technologies, pp. 1-4. IEEE Explore (2016)Google Scholar
  15. 15.
    Muneeswaran, V., Pallikonda Rajasekaran, M.: Analysis of particle swarm optimization based 2D FIR filter for reduction of additive and multiplicative noise in images. In: Arumugam, S., Bagga, J., Beineke, L., Panda, B. (eds) Theoretical Computer Science and Discrete Mathematics. ICTCSDM 2016. Lecture Notes in Computer Science, vol. 10398, Springer, Cham (2017)CrossRefGoogle Scholar
  16. 16.
    Muneeswaran, V., Pallikonda Rajasekaran, M.: Beltrami-regularized denoising filter based on tree seed optimization algorithm: an ultrasound image application. In: Satapathy, S., Joshi, A. (eds.) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Vol. 1. ICTIS 2017. Smart Innovation, Systems and Technologies, p. 83, Springer, Cham (2018)Google Scholar
  17. 17.
    Shao, Y., Chen, Q., Jiang, H.: RBF neural network based on particle swarm optimization. In: Zhang, L., Lu, B.L., Kwok, J. (eds.) Advances in Neural Networks—ISNN 2010. LNCS, vol. 6063, pp. 169–176. Springer, Berlin, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Shen, W., Guo, X., Wu, C., Wu, D.: Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowl. Based Syst. 24(3), 378–385 (2011)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Instrumentation and Control EngineeringKalasalingam UniversityKrishnankoilIndia
  2. 2.Department of Electronics and Communication EngineeringKalasalingam UniversityKrishnankoilIndia

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