Leticia V. GUIMARÃES¹, Altamiro A. SUZIM² and Junji MAEDA^1,*

¹Faculty of Engineering, Muroran Institute of Technology, Mizumoto-cho, Muroran 050-8585, Japan
²Electrical Engineering Department, Universidade Federal do Rio Grande do Sul, CEP 90035-190, Porto Alegre, Brazil

(Received July 30, 2001; Accepted September 5, 2001)

This paper presents an iterative algorithm for circular decomposition which investigates the separation of overlapped circular particles of a binary image, in order to locate their center coordinates and to estimate their radii. Since this algorithm is based on the measure of a circle similarity of an object in an image to execute a search for concavities, object segmentation and circle recognition, its implementation is simpler than the algorithm based on polygonal approximation. In this work we compare the accuracy and robustness of the proposed circle similarity algorithm with a polygonal approximation based algorithm using synthetic images and real blood cell images. Both the algorithms are able to decompose connected blood cells. However, the input parameters of the polygonal approximation method shows high sensitivity to the shape or agglomeration of an object, while the proposed algorithm demonstrates more stable performance.

Key words : particle decomposition, circle detection, cell counting, image segmentation, concavity detection

junji@csse.muroran-it.ac.jp