Boundary detection in Medical Images using Edge Field Vector based on Law’s Texture and Canny Method

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2013 by IJETT Journal
Volume-4 Issue-5                      
Year of Publication : 2013
Authors : Swetha.M , Jyohsna.C

Citation 

Swetha.M , Jyohsna.C. "Boundary detection in Medical Images using Edge Field Vector based on Law’s Texture and Canny Method". International Journal of Engineering Trends and Technology (IJETT). V4(5):1912-1917 May 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.

Abstract

detecting the correct boundary in noisy images is a difficult task. Images are used in many fields,including surveillance, medical diagnostics and non - destrucive testing. Edge detection and boun dary detection plays a fundamental role in image analysis. B oundaries are mainly used to detect the outline or shape of the object. image segmentation is used to locate objects and boundaries in images and it assigns a lable in every pixel in an image such that pixels with the same level share have certain virtual characteristics. The proposed edge detection technique for detecting the boundaries of object using the information from intensity gradient using the vector model a nd texture gradient using the edge map modle.the results show that the technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kind of noisy images without prior knowledge of noise properties.

References

[1] J. Guerrero, S.E. Salcudean, J.A.McEwen, B.A. Masri, and S. Nicolaou, “Real - time vessel segmentation and tracking for ultrasound imaging applications,” IEEE Trans, Med.Imag., vol.26,no.8,pp.1079 - 1090, Aug.2007.
[2] F.Destrempes, J. Meunier, M. - F. Giroux, G.Soulez, and G. Cloutier, “se gmentation in ultrasounic B - mode images of Nakagami distributions and stochastic optimization,” IEEE Trans. Med.Imag., vol.28, no. 2, pp.215 - 229, Feb.2009.
[3] N. Theera - Umpon and P.D. Gader, “System level training of neural networks for counting white blo od cells,” IEEE Trans. Syst., Man, Cybern. C, App. Rev., vol.32,no.1,pp.48 - 53,Feb.2002.
[4] N. Threera - Umpon, “White blood cell segmentation and classification in microscopic bone marrow images ,” Lecture Notes Comput. Sci., vol. 3614, pp. 787 - 796, 2005.
[5] N. Theera - Umpon and S. Dhompongsa, “Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification,” IEEE Trans. Inf. Technol. Biomed., vol.11, no.3, pp. 353 - 359, May 2007.
[6] J. Carballido - Gamio, S. J. Belong ie, and S. Majumdar, “Normalized cuts in 3 - D for spinal MRI segmentation,” IEEE Trans. Med. Imag., vol.23, no.1, pp.36 - 44, jan. 2004.
[7] H. Greenspan, A. Ruf, and J. Goldbeger, “Constrainted Gaussian mixture model framework for automatic segmentation of M R brain images.” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1233 - 1245, Sep. 2006.
[8] J. – D. Lee, H. - R. su, P. E. Cheng, M. Liou, J. Aston, A. C. Tsai, and C. - Y. Chen, “ MR image segmentation using a power transformation approach .” IEEE Trans, Med. Image. , vol . 28, no. 6, pp. 894 - 905. Jun, 2009

Keywords
Boundary extraction, vector field model, edge mapping model, edge following technique, boundary detection.