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A Colony Counting Algorithm based on Distance Transformation  

Mun, Hyeok (Korea University of Technology and Education, School of Computer Science and Engineering)
Lee, Bok Ju (Korea University of Technology and Education, School of Computer Science and Engineering)
Choi, Young Kyu (Korea University of Technology and Education, School of Computer Science and Engineering)
Publication Information
Journal of the Semiconductor & Display Technology / v.15, no.3, 2016 , pp. 24-29 More about this Journal
Abstract
One of the main applications of digital image processing is the estimation of the number of certain types of objects (cells, seeds, peoples etc.) in an image. Difficulties of these counting problems depends on various factors including shape and size variation, degree of object clustering, contrast between object and background, object texture and its variation, and so on. In this paper, a new automatic colony counting algorithm is proposed. We focused on the two applications: counting the bacteria colonies on the agar plate and estimating the number of seeds from images captured by smartphone camera. To overcome the shape and size variations of the colonies, we adopted the distance transformation and peak detection approach. To estimate the reference size of the colony robustly, we also used k-means clustering algorithm. Experimental results show that our method works well in real world applications.
Keywords
colony counting; distance transform; Gaussian filter; peak detection; k-means algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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