A Robust and Accurate Automated Colony Counter App Based on Image Processing
Date Issued
2015
Date
2015
Author(s)
Chien, Yueh-Ming
Abstract
Colony counting is an important part of microbiological experiment. At present, colony number is usually counted by manual method. It is time-consuming, inefficient and high error rate when the amount of experimental samples is large. Many automatic colony counting system and machines are developed so far, but they are either expensive or inconvenient to use. Some authors have recently developed automatic colony counting systems, but they require fixed equipment to capture the image. Some authors even release open-source code or software to count colonies. However, none of them is widely adopted. As a consequence, by the popularization of the smart phone, we intend to develop an automatic colony counting app on cellphone to improve it. We propose a new method of image segmentation to get foreground region. First, split the image into many grids and assume most pixels in each grid belong to background region. Next, use principal component analysis to build background model, which is used by region growing to find all background region and separate foreground from image. We take the foreground regions as colony regions if they are like a circle. Extract RGB histogram and HOG features from colony regions to build different sizes of colony model. Finally, match the remaining region with colony models to get all colony regions and count the number of colonies. Our approach’s accuracy is 80% and outperforms the best open software of the related research─OpenCFU, which accuracy is 48%. It is proved that our method is robust and accurate to count the colonies automatically on mobile App.
Subjects
colony counting
image segmentation
feature extraction
Type
thesis
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