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Research Paper | Electronics Communication Engineering | Volume 6 Issue 4, April 2017 | Pages: 1487 - 1494 | India
Detection of Lung Tumor in MR Images using Modified Pillar K-Means Algorithm with Gabor Filter and Color Mapping
Abstract: 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
How to Cite?: 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", Volume 6 Issue 4, April 2017, International Journal of Science and Research (IJSR), Pages: 1487-1494, https://www.ijsr.net/getabstract.php?paperid=ART20172774, DOI: https://dx.doi.org/10.21275/ART20172774