Paper
13 January 2003 Semi-Automated Segmentation of Microbes in Color Images
Chandankumar K. Reddy, Feng-I Liu, Frank B. Dazzo
Author Affiliations +
Proceedings Volume 5008, Color Imaging VIII: Processing, Hardcopy, and Applications; (2003) https://doi.org/10.1117/12.472024
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States
Abstract
The goal of this work is to develop a system that can semi-automate the detection of multicolored foreground objects in digitized color images that also contain complex and very noisy backgrounds. Although considered a general problem of color image segmentation, our application is microbiology where various colored stains are used to reveal information on the microbes without cultivation. Instead of providing a simple threshold, the proposed system offers an interactive environment whereby the user chooses multiple sample points to define the range of color pixels comprising the foreground microbes of interest. The system then uses the color and spatial distances of these target points to segment the microbes from the confusing background of pixels whose RGB values lie outside the newly defined range and finally finds each cell's boundary using region-growing and mathematical morphology. Some other image processing methods are also applied to enhance the resultant image containing the colored microbes against a noise-free background. The prototype performs with 98% accuracy on a test set compared to ground truth data. The system described here will have many applications in image processing and analysis where one needs to segment typical pixel regions of similar but non-identical colors.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chandankumar K. Reddy, Feng-I Liu, and Frank B. Dazzo "Semi-Automated Segmentation of Microbes in Color Images", Proc. SPIE 5008, Color Imaging VIII: Processing, Hardcopy, and Applications, (13 January 2003); https://doi.org/10.1117/12.472024
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Microorganisms

Image segmentation

Image processing

RGB color model

Bacteria

Digital imaging

Image analysis

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