Advertisement

Analysis of Image Segmentation Algorithms Using MATLAB

  • Sumita Verma
  • Deepika Khare
  • Ravindra Gupta
  • Gajendra Singh Chandel
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 150)

Abstract

Image segmentation has played an important role in computer vision especially for human tracking. The result of image segmentation is a set of segments that collectively cover the entire image or a set of contours extracted from the image. Its accuracy but very elusive is very crucial in areas as medical, remote sensing and image retrieval where it may contribute to save, sustain and protect human life. This paper presents the analysis and implementation using MATLAB features and one best result can be selected for any algorithm using the subjective evaluation. We considered the techniques under the following five groups: Edge-based, Clustering-based, Region-based, Threshold-based and Graph-based.

Keywords

Image segmentation N-cut Mean-shift Fuzzy-C mean Image analysis 

References

  1. 1.
    Gonzalez RC, Woods RE (2002) Digital image processing. 2nd Prentice-Hall Inc, Upper Saddle RiverGoogle Scholar
  2. 2.
    Shapiro LG, Stockman GC (2001) Computer vision. Prentice-Hall Inc., Upper Saddle River, pp 279–325Google Scholar
  3. 3.
  4. 4.
  5. 5.
    Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Underst 10(2):260–280CrossRefGoogle Scholar
  6. 6.
    Polak M, Zhang H, Pi M (2009) An evaluation metric for image segmentation of multiple objects. Image Vis Comput 27(8):1223–1227CrossRefGoogle Scholar
  7. 7.
    Hu S, Hoffman EA, Reinhardt JM (2001) Automatic lung segmentation for accurate quantization of volumetric X-ray CT images. IEEE 20(6):490–498Google Scholar
  8. 8.
    Zhang YJ (2001) A review of recent evaluation methods for image segmentation. Paper presented at the International Symposium on Signal Processing and its Applications (ISSPA), Kuala LumpurGoogle Scholar
  9. 9.
    Udupa JK, Leblanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM et al (2006) A framework for evaluating image segmentation algorithms Comput Med Imaging Graph 30:75–87Google Scholar
  10. 10.
    Varshney SS, Rajpal N, Purwar R (2009) Comparative study of image segmentation techniques and object matching using segmentation. Paper presented at the international conference on methods and models in computer scienceGoogle Scholar
  11. 11.
    Wang L, He L, Mishra A, Li C (2012) Active contours driven by local Gaussian distribution fitting energy. Signal Process 2(3):737–739Google Scholar
  12. 12.
    Wang Y, Guo Q, Zhu Y (2007) Medical image segmentation based on deformable models and its applications. Springer, p 2Google Scholar
  13. 13.
    Boucheron LE, Harvey NR, Manjunath BS (2007) A quantitative object-level metric for segmentation performance and its application to cell nuclei. Springer, pp 208–219Google Scholar
  14. 14.
    Padmavathi G, Subashini P, Sumi A (2010) Empirical evaluation of suitable segmentation algorithms for IR images. IJCSI Int J Comput Sci 7(4)(2):Google Scholar
  15. 15.
    Mobahi H, Rao SR, Yang AY, Sastry SS, Ma Y. Segmentation of natural images by texture and boundary compressionGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Sumita Verma
    • 1
  • Deepika Khare
    • 1
  • Ravindra Gupta
    • 1
  • Gajendra Singh Chandel
    • 1
  1. 1.SSSISTSehoreMadhya Pradesh

Personalised recommendations