An approach for segmentation of medical images using pillar K-means algorithm
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - April Issue 2013 by IJCTT Journal|
|Volume-4 Issue-4 |
|Year of Publication : 2013|
|Authors : M.Pavani, Prof. S.Balaji|
M.Pavani, Prof. S.Balaji"An approach for segmentation of medical images using pillar K-means algorithm"International Journal of Computer Trends and Technology (IJCTT),V4(4):636-641 April Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -This paper presents an approach for image segmentation using pillar K-Means algorithm. In this paper the segmentation process includes a mechanism for clustering the elements of high resolution images. By using this process we can improve precision and reduce computational time. The system applies K-means clustering to image segmentation after optimized by pillar algorithm. The pillar algorithm considers that pillars placement should be located as far as possible from each other. The pillars placement is located far from each other to withstand against the pressure distribution of a roof, as identical to number of centroids among the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in terms of precision and computational time. By calculating the accumulated distance metric between each data point and all previous centroids it designates the initial centroids position and then it selects the data points which have maximum distance as new initial centroids. According to accumulated distance metric all the initial centroids are distributed in his algorithm. This paper evaluates by using an existing approach for image segmentation. But here we use medical images for segmentation. The experimental results clarify that this approach improves the segmentation quality in terms of precision and computational time.
 A New Approach for Image Segmentation using Pillar-Kmeans Algorihm, World Academy of Science, Engineering and Technology 59 2009, Ali Ridho Barakbah and Yasushi Kiyoki
 Bishop, C. M. Neural Networks for Pattern Recognition. Oxford, England: Oxford University Press, 1995, http://mathworld.wolfram.com/K-Means Clustering Algorithm.html
 A.R. Barakbah, Y. Kiyoki, “An Image Database Retrieval System with 3D Color Vector Quantization and Cluster-based Shape and Structure Features”, The 19th European-Japanese Conference on Information Modelling and Knowledge Bases, Maribor, 2009.
 A.R. Barakbah, Y. Kiyoki, “A Pillar Algorithm for K-Means Optimization by Distance Maximization for Initial Centroid Designation”, IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Nashville-Tennessee, 2009.
 A. Murli, L. D’Amore, V.D. Simone, “The Wiener Filter and Regularization Methods for Image Restoration Problems”, Proc. The 10th International Conference on Image Analysis and Processing, pp. 394-399, 1999
 J. Chen, J. Benesty, Y.A. Huang, S. Doclo, “New Insights Into the Noise Reduction Wiener Filter”, IEEE Transactions on Audio, Speech, and Language Processing, Vol. 14, No. 4, 2006.
Keywords — Image segmentation, K-means clustering, Pillar algorithm.