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Extracting Objects with Adaptive Segmentation Techniques: Going Beyond Intensity Thresholding

Published online by Cambridge University Press:  02 July 2020

Peter Eggleston
Peter Eggleston*
Affiliation:
Amerinex Applied Imaging, Inc., 409 Main St., Amherst, MA, 01002
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In designing automated systems for interpretation of micrographs, it is often the goal to separate and discern various objects within the image data. After segmentation, measurements of the objects, called features, can then be calculated and used for process or statistical analysis. Simple segmentation schemes based on single threshold operations, often lack the sophistication to deal with intricate or subtle details of the image data, or require user intervention in the threshold selection process. This talk will present and discuss advanced techniques which adapt to the data, and which can operate autonomously without human supervision.

Segmentation is a process which transforms pixel based image data into symbolic descriptors representing groups of pixel elements. These descriptors are called objects and take the form of lines, regions, polygons, points, windows of interests, or other unique representations. Most laboratory image analysis software packages utilize segmentation schemes based on the principle of intensity analysis.

Type
Applied Image Processing: What it Can do for Digital Imaging
Information
Microscopy and Microanalysis , Volume 4 , Issue S2: Proceedings: Microscopy & Microanalysis '98, Microscopy Society of America 56th Annual Meeting, Microbeam Analysis Society 32nd Annual Meeting, Atlanta, Georgia July 12-16, 1998 , July 1998 , pp. 64 - 65
Copyright
Copyright © Microscopy Society of America

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References

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