BNIMS: Block-based Non-iterative Mean-shift Segmentation algorithm for Medical Images
Keywords:Image Segmentation, Mean-shift, medical images, Computational Complexity
This paper proposed a novel Block based Mean Shift Image Segmentation Algorithm to significantly reduce the computation and improve the segmentation accuracy for high resolution Medical Image. One of the challenging tasks in the image analysis and computer vision area is to correctly classify the pixels as there are no crisp borders among entities in an image. In this proposed methodology, it is observed that the computational complexity of the procedure is diminished by combining the pixels of an image of size MXN into non overlapping image blocks of size 3x3 by eliminating the iterative way of the mean shift procedure. This proposed algorithm shrinks the size of the image by one third of its original image for the computational purpose and then equalizes the number of computations for each new image pixel by constructing links between pixels using their first mean-shift vectors without any iteration process. The accurateness and effectiveness of the proposed methodology is matched with the existing Iterative Mean Shift Algorithm by accomplishing the empherical experiments on the Medical Images (Pathologies Buccales and Eye Retina) composed along with the similarity measures.
 Yizong Cheng, “Mean shift, mode seeking, and clustering”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 8, pp.790–799, 1995.
 Carreira-Perpinan, Miguel A, “Acceleration strategies for Gaussian mean-shift image segmentation,” Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference, vol. 1, pp. 1160-1167, June 2006.
 H. Cho, S. I. Cho, Y. H. Kim, “Image segmentation using linked mean shift vectors for SIMD architecture,” in Proc. IEEE int. Conf.Consumer Electronics, Las Vegas, USA, pp. 484-485, Jan. 2014.
 L. Shafarenko, M. Petrou, and J. V. Kittler, “Histogram based segmentation in a perceptually uniform color space,” IEEE Trans. Image Process, vol. 7, no. 9, pp. 1354-1358, 1998.
 C. Li, C. Y. Kao, J. C. Gore, and Z. H. Ding, “Minimization of region scalable fitting energy for image segmentation,” IEEE Trans. Image Process, vol. 17, no. 10, pp. 1940-1949, 2008.
 C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization: A new variation formulation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Diego, USA, 2005, pp.430-436.
 Y. Boykov, and M. P. Jolly, “Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images,” in ICCV, Vancouver, Canada, 2001, pp. 105-112.
 Y. Boykov, and G. Funka-Lea, “Graph cuts and efficient n-d image segmentation,” International Journal of computer vision, vol. 70, no. 2,pp. 109-131, 2006.
 L. Grady, and E. L. Schwartz, “Isoperimetric graph partitioning for image segmentation,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 469-475, 2006.
 J. Shi, and J. Malik, “Normalized cuts and image segmentation,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 22, no. 8,pp. 888-905, 2000.
 Gillet, L. Macaire, C. Botte-Lecocq, and J. G. Postaire, “Color image segmentation by analysis of 3D histogram with fuzzy Morphological filters,” in Springer-Verlag Editor, Fuzzy Filters for Image Processing-Studies in Fuzziness and Fuzziness and Soft Computing, New York,USA, 2002, pp. 154-177.
 C.-C. Cheng, C.-T. Li, and L.-G Chen, “A novel 2D-to-3D conversionsystem using edge information,” IEEE Trans. Consumer Electron., vol.56, no.3, pp. 1739-1745, Aug. 2010.
 J. Lee, D.-K.Lee, and R.-H. Park, “Robust exemplar-based inpaintingalgorithm using region segmentation,” IEEE Trans. Consumer Electron.,vol.58, no. 2, pp. 553-561, May 2012.
 S.-G. Jeong, C. Lee, and C.S. Kim, “Motion-compensated frame interpolation based on multi hypothesis motion estimation and texture optimization,” IEEE Trans., Image Processing, vol. 22, no. 11, pp.4497-4509, Nov. 2013.
 H. Tang and Z. Zhu, “Content-based 3-D mosaics for representing videos of dynamic urban scenes.” IEEE
Trans., Circuits and Systems forVideo Techno. vol. 22, no. 2, pp. 295-308, Feb. 2012.
 K. Fukunaga and L. Hostetler. The estimation of the gradientof a density function, with applications in pattern
recognition.IEEE Trans. Information Theory, 21(1):32-40, Jan.1975.
 R. Collins. Mean-shift blob tracking through scale space. In’I Computer and Pattern Recognition, pages
 Z. Zivkovic and B. Krose. An EM-like algorithm for histogram-based object tracking. In Proc. Int’l Conf.
Computer Vision and Pattern Recognition,pages 798-803,2004.
 B. Han, D. Comaniciu, Y. Zhu, and L. Davis. Incremental density approximation and kernel-based Bayesian
filtering for object tracking. In Proc. Int’l Conf. Computer vision and Pattern Recognition, pages 638-644, 2004.
 D. Commaniciu, “Mean Shift: A Robust Approach toward Feature Space Analysis,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 24, no.5, pp. 603–619, May 2002.
 D. Comaniciu, V. Ramesh, and P. Meer. The variable bandwidth mean shift and data-driven scale selection. In
Proc. Intel Conf.Computer Vision, pages 498-445, 2001.
 H. Chen and P. Meer. Robust fusion of uncertain information.In Proc. Int’l Computer Vision and
PatternRecognition, pages 1622,2003.
 B.Georgescu, I. Shimshoni and P. Meer. Mean shift based clustering in high dimensions: A texture classification
example.In Proc. IEEE Int’l Computer Vision, pages 456-463, 2003.
 D. Comaniciu and P. Meer. Distribution free decomposition of multivariate data. Pattern Analysis and
Applications, Springer, 2(1): 22-30; April 1999.
 D. Dementhon. Spatial-temporal segmentation of video by hierarchical mean shift analysis. In Proc. Statistical
Methods in Video Processing Workshop, 2002.
 Dor in Comaniciu and Peter Meer, “Mean shift analysis and applications,” in ICCV (2), 1999, pp. 1197–1203.
 DeMenthon D., “Spatio-temporal segmentation of video by hierarchical mean shift analysis,” in In Proc.
Statistical Methods in Video Processing Workshop, Washington, DC, USA, 2002, IEEE Computer Society.
 Changjiang Yang, Ramani Duraiswami, Nail A. Gumerov, and Larry Davis, “Improved fast gauss transform and efficient kernel density estimation,” in ICCV ’03: Proceedings of the Ninth IEEE International Conference on
Computer Vision, Washington,DC, USA, 2003, p. 464, IEEE Computer Society.
 Kai Zhang, Ming Tang, and J.T. Kwok, “Applying neighborhood consistency for fast clustering and kernel
densityestimation,” in Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
Volume 2,20-25 June, Washington, DC, USA, 2005, pp. 1001 –1007, IEEE Computer Society.
 Zhang Y. Tao W., Jin H., “Color imag e segmentation based on mean shift and normalized cuts,” in Systems, Man and Cybernetics- Part B, IEEE Transactions on Volume 37, Issue 5, October, Washington, DC, USA, 2007,
pp. 1382–1389, IEEE Computer Society.
 B. Silverman. Density Estimation for Statistics and Data Analysis. Chapman and Hall, 1986.
 C. M. Christoudias, B. Georgescu, and P. Meer, “Synergism in low level vision,” in Proc. IEEE Int.
 Recognition, Quebec City, Canada, vol. 4, pp. 150-155, Aug. 2002.