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Comparative Analysis of Edge Detection Techniques for Medical Images of Different Body Parts

  • Bhawna DhruvEmail author
  • Neetu Mittal
  • Megha Modi
Conference paper
  • 1.1k Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 799)

Abstract

Medical images are arduous to process since they possess distinct modalities. Therefore, the medical practitioners cannot competently detect and diagnosis the diseases in conventional ways. There should be a system which helps physicians to understand medical images very easily. Image segmentation using edge detection is commonly used for image analysis and better visualization of medical images. Various methods have been used for image segmentation such as Threshold detection, Region detection, Edge detection and Clustering technique. Edge detection is one of the prominently used methods for segmentation. This technique focuses on identifying and analyzing the entire image based upon the detected edges. In this paper, MRI images of human body parts such as abdomen, ankle, elbow, hand, knee, leg, liver and brain are considered for edge detection. Further, filtering has been performed on the segmented images to remove the unwanted noise. This makes the image more clearly for further reference. The effectiveness of the proposed technique has been evaluated quantitatively by using the performance measures like Entropy and Standard Deviation. The proposed technique may be highly beneficial for medical practitioners to carry out the diagnosis for effective treatment.

Keywords

Image segmentation Medical images MRI Edge detection Entropy Standard deviation Noise removal 

References

  1. 1.
    Singh, H., Agrawal, D.: A meta analysis on content based image retrieval system. In: Proceedings of the IEEE International Conference on Emerging Technological Trends, pp. 1–6 (2016)Google Scholar
  2. 2.
    Khader, A., Ali, A., Alfaki, A.: Color and texture fusion-base method for content-based image retrieval. In: Proceedings of the IEEE International Conference on Computing for Sustainable Global Development, pp. 3205–3210 (2016)Google Scholar
  3. 3.
    Patel, J.M., Gamit, N.C.: A review on feature extraction techniques in content based image retrieval. In: IEEE International Conference on Wireless Communications, Signal Processing and Networking, pp. 2259–2263 (2016)Google Scholar
  4. 4.
    Manno, A.: Content based image retrieval using salient orientation histograms. In: IEEE International Conference on Image Processing, pp. 2480–2484 (2016)Google Scholar
  5. 5.
    Zaitouna, N.M., Aqelb, M.J.: Survey on image segmentation techniques. Procedia Comput. Sci. 65, 797–806 (2015). International Conference on Communication, Management and Information TechnologyCrossRefGoogle Scholar
  6. 6.
    Kabai, L., Abdellaoui, M.: Content based image retrieval using local and global feature extractor. In: IEEE International Conference on Advanced Technologies for Signal and Image Processing, pp. 151–154 (2016)Google Scholar
  7. 7.
    Mageswari, S.U., Sridevi, M., Mala, C.: An experimental study and analysis of different image segmentation techniques. Procedia Eng. 64, 36–45 (2013). International Conference on Design and ManufacturingCrossRefGoogle Scholar
  8. 8.
    Abdulrazzaq, M.M., Noah, S.A., Fadhil, M.A.: X-Ray medical image classification based on multi classifiers. In: IEEE International Conference on Advanced Computer Science Applications and Technologies, pp. 218–223 (2015)Google Scholar
  9. 9.
    Zheng, K.: Content based image retrieval for medical image. In: Proceedings of the IEEE International Conference on Computational Intelligence and Security, pp. 219–222 (2015)Google Scholar
  10. 10.
    Lijuan, S., Fengqi, H.: Research on color and texture feature based image retrieval. In: IEEE International Conference on Intelligent, Transportation, Big Data and Smart City, pp. 626–628 (2015)Google Scholar
  11. 11.
    Kumar, T.G.S., Nagarajan, V.: Local smoothness pattern for content based image retrieval. In: IEEE International Conference on Communications and Signal Processing, pp. 1190–1193 (2015)Google Scholar
  12. 12.
    Jyothi, B., Madhavee Latha, Y., Mohan, P.G.K.: An effective multiple visual features of content based medical image retrieval. In: Proceedings of the 9th IEEE International Conference on Intelligent Systems and Control, pp. 1–5 (2015)Google Scholar
  13. 13.
    Rocha, R., Saito, P.T.M., Bugatti, P.H.: Exploiting revolutionary approaches for content based image retrieval. In: International Symposium on Computer Based Medical System, pp. 370–372 (2015)Google Scholar
  14. 14.
    Gupta, N.M.R.: Comparative analysis of medical images fusion using different fusion methods for Daubechies complex wavelet transform. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(6) (2013)Google Scholar
  15. 15.
    Gupta, A., Gangadharppa, M.: Image retrieval based on color, shape and texture. In: 2nd IEEE International Conference on Computing for Sustainable Global Development, pp. 2097–2103 (2015)Google Scholar
  16. 16.
    Bhagyalakshmi, A., Vijayachamundeeswari, V.: A survey on image retrieval using various operators In: IEEE International Conference on Computer Communication and Systems, pp. 18–23 (2014)Google Scholar
  17. 17.
    Wang, Y., Li, Q., Lan, T., Chen, J.: A comparison of image based image retrieval system. In: 17th IEEE International Conference on Computational Science and Engineering, pp. 669–673 (2014)Google Scholar
  18. 18.
    Jenni, K., Mandala, S.: Pre-processing image database for efficient content based image retrieval. In: IEEE International Conference on Advances in Computing, Communications and Informatics, pp. 968–972 (2014)Google Scholar
  19. 19.
    Shriwas, M.K., Raut, V.R.: Content based image retrieval: a past, present and new feature descriptor. In: IEEE International Conference on Circuit, Power and Computing Technologies, pp. 1–7 (2015)Google Scholar
  20. 20.
    Mendoza, O., Melin, P., Licea, G.: A new method for edge detection in image processing using interval type-2 fuzzy logic. In: IEEE International Conference on Granular Computing, pp. 151–155 (2007)Google Scholar
  21. 21.
    Anandakrishnan, N., Santhosh Baboo, S.: An evaluation of popular edge detection techniques in digital image processing. In: IEEE International Conference on Intelligent Computing Applications, pp. 213–217 (2014)Google Scholar
  22. 22.
    Selvakar, P., Hariganesh, S.: The performance analysis of edge detection algorithms for image processing. In: IEEE International Conference on Computing Technologies and Intelligent Data Engineering, pp. 1–5 (2016)Google Scholar
  23. 23.
    Vijaya, A., Sunderesan, M.: Significant image enhancement techniques for removal of noise in LiDar images. In: 3rd International IEEE Conference on Computing for Sustainable Global Development, pp. 3904–3908 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Amity UniversityNoidaIndia
  2. 2.Yashoda Super Specialty HospitalGhaziabadIndia

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