Title :
Auto-thresholding Edge Detector for bio-image processing
Author :
Yan Zhang ; Makowski, Lee
Author_Institution :
Electr. & Comput. Eng., Northeastern Univ. Boston, Boston, MA, USA
Abstract :
Canny Edge Detector has been widely used in biological related areas to detect edges of objects in an image. It has been recognized as an efficient edge detector, but the two thresholds that need to be provided by users remains an issue. In this paper, we develop an Auto-thresholding Edge Detector which applies k-means clustering algorithm for gradient intensity partitioning. The partitioned edge intensities are furthur utilized to compute high and low thresholds for the last step which is Hysteresis edge tracking. The Auto-thresholding Edge Detector solves the trial-and-error problem and doesn´t require users to input thresholds. The thresholds are calculated statistically based on individual images and the performance is promising.
Keywords :
biological techniques; biology computing; edge detection; image processing; pattern clustering; statistical analysis; tracking; Canny edge detector; autothresholding edge detector; bioimage processing; edge intensity partitioning; gradient intensity partitioning; high threshold computation; hysteresis edge tracking; k-means clustering algorithm; low threshold computation; object edge detection; statistical calculation; threshold input; trial-and-error problem; Biology; Biomedical imaging; Clustering algorithms; Detectors; Hysteresis; Image edge detection; Partitioning algorithms;
Conference_Titel :
Biomedical Engineering Conference (NEBEC), 2015 41st Annual Northeast
Conference_Location :
Troy, NY
Print_ISBN :
978-1-4799-8358-2
DOI :
10.1109/NEBEC.2015.7117209
