Performance comparison and evaluation of vari-ous segmentation methods

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Image segmentation is the most important method in the concept of image processing. It helps in analyzing the image accurately in many applications. It is generally used to assign or name, a label to individual pixels in an image, so that labels with similar name share common features. These related pixels result in same color, texture, or intensity. It also helps in identifying lines, curves and objects. These kinds of results help in different applications in the field of medical imaging, 3D constructions, etc. There are different kinds of segmentation methods already available for such applications. This paper briefs and compares three different types of segmentation methods like multithreshold method, watershed method and normalized cut method. It is compared based on computational time, complexity and number of clusters of the different methods used in the image.


  • Keywords


    Multithreshold; Watershed; Normalised Cut.

  • References


      [1] Manjula. KA, ”Role of Image Segmentation in Digital Image Pro-cessing for Information Processing”, IJCST, vol 3,issue 3,pp.312-318,May-June 2015.

      [2] Sharma D.,”Image processing”, Course-ECE-411, Technical pa-per.

      [3] https://en.wikipedia.org/wiki/Image segmentation.

      [4] Yanowitz S.D., Bruckstein A.M.,”A new method for image sege-mentation”, pp.82-95, February 4, 1998.

      [5] Morse B.S., Brigham Young University, “Thresholding”, 1998-2000, technical paper.

      [6] Ferrari S., “Image segmentation”, pp.1-22, 2011, technical paper.

      [7] Amoda N., Kulkarni R.K., “Image segmentation and detection using watershed transform and region based image retrieval”, IJETTCS, vol 2, Issue 2, pp.89-94, 2013.

      [8] XUE Guoxin, LIU Qiang, XUE Pei, “Method of watershed with threshold decision for multiscale tra c image segmentation, International Symposium on Computational Intelligence and Design”, pp.186-189, 2010.

      [9] Beucher S., Lantuejoul C., “Use of watersheds in contour detection”, pp.2.1-2.12, technical paper, 1979.

      [10] Shi J., Malik J., cuts and image segmentation, pp.1-52, technical paper.

      [11] Xiaoli Xu, Zhimao Lu, Haiyan Li, “Color Image Segmentation Based on Watershed and Ncut of Improved Weight Matrix”, IEEE, pp.681-686, 2011.

      [12] Mikulka J., Gescheidtova E., Bartusek K., and Nespor D., “Comparison of Segmentation Methods in MR Image Processing”, Progress in Electromagnetics Research Symposium Proceedings, pp.429-432, 2012.

      [13] Vaia Machairas, Etienne Decenciere, Thomas Walter, “Waterpixels: Superpixels based on the watershed transformation”, IEEE, 2014.

      [14] Qian Zhu, Liqiu Jing and Rongsheng Bi, “Exploration and Im-provement of Otsu Threshold Segmentation Algorithm”, IEEE, 2010.

      [15] Sahil Narang, Kishore Rathinavel, “An implementation of Efficient Graph-Based Image Segmentation”, pp.1-4.

      [16] Kun Yang, Cai-Xia Deng, Yu Chen, Li-Xiang Xu, “The denoising method of threshold function based on wavelet”, IEEE, 13-16 July, 2014.

      [17] Xiie Dong Yang and Vipin Gnpta, “An Improved Threshold Se-lection Method for Image Segmentation”,IEEE, 1993.

      [18] https://www.sciencedirect.com/science/article/pii/S0165168409004423

      [19] http://www.bioone.org/doi/abs/10.3394/0380 1330(2007)33

      [20] Najmuzzama Zerdi, Dr. Subhash Kulkarni,Dr. V.D.Mytri,” Detailed image segmentation using normalised cuts and weighted coefficient”, IJEEE,April 2014.

      [21] Wenbing Tao, Hai Jin, and Yimin Zhang, “Color Image Seg-mentation Based on Mean Shift and Normalized Cuts”, October 2007.


 

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Article ID: 9687
 
DOI: 10.14419/ijet.v7i2.9687




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