Research Paper | Electronics & Communication Engineering | India | Volume 6 Issue 4, April 2017
Detection of Lung Tumor in MR Images using Modified Pillar K-Means Algorithm with Gabor Filter and Color Mapping
Hakeem Aejaz Aslam, Prof. T. Ramashri
This paper presents an approach to image segmentation using Modified Pillar K-Means algorithm. This segmentation method includes a new mechanism for distance metric and grouping the elements of high resolution images in order to improve accuracy. The system uses modified Pillar K-means algorithm for optimized image segmentation. The Pillar algorithm considers the placement of pillars that should be located as far from each other to resist the pressure distribution of a roof same as the number of centroids between the data distribution. This algorithm is able to optimize the K-Means clustering for image segmentation in the aspects of accuracy. This algorithm distributes all initial centroids according to the Accumulated Distance Metric (ADM) & the distance is calculated by Chessboard distance measure. This paper evaluates the proposed approach for image segmentation with Gabor filter, Modified Pillar K-Means clustering algorithm and Marker controlled Watershed Transform with different samples of MR Images for Accuracy, Precision, Sensitivity Factor, Specificity Factor F-Factor & Success rate. Experimental results clarify the effectiveness of our approach to improve the segmentation quality.
Keywords: Tumor, Segmentation, Detection, Centroids, Gabor filter, Marker Controlled Watershed Transform
Edition: Volume 6 Issue 4, April 2017
Pages: 1487 - 1494
How to Cite this Article?
Hakeem Aejaz Aslam, Prof. T. Ramashri, "Detection of Lung Tumor in MR Images using Modified Pillar K-Means Algorithm with Gabor Filter and Color Mapping", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20172774, Volume 6 Issue 4, April 2017, 1487 - 1494
114 PDF Views | 83 PDF Downloads
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