Optimal component selection for image segmentation via Parallel Analysis - Catalbas, Mehmet Cem; Yildirim, Merve; Gulten, Arif; Kurum, Hasan (Institute of Electrical and Electronics Engineers Inc., 2016)
In this paper, an image segmentation method is presented to analyze the clusters of Computed Tomography (CT) image. Target image is divided to small parts called as observation screens. Principal Component Analysis (PCA) is used for better representation of features about observation screens. The optimal number of component related with observation screen is determined by Horn's Parallel Analysis (PA). Besides, Local Standard Deviation (LSD) which is a method for extracting meaningful sub-features is applied to whole image for successful segmentation. The effect of segmentation success rate is analyzed by selected features. Consequently, a novel algorithm is proposed for minimizing total computation time and error of dimension reduction significantly. It is seen that the results of the algorithm are approximately same as conventional segmentation algorithms. © 2016 IEEE.
8th IEEE International Conference on Intelligent Systems, IS 2016 -- 4 September 2016 through 6 September 2016 -- Sofia -- 124793
Keywords: adaptive image segmentation; data mining; local standard deviation; parallel analysis; principal component analysis; statistical image processing