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2017 | OriginalPaper | Chapter

6. Segmentation: Principles and Basic Techniques

Author : Klaus D. Toennies

Published in: Guide to Medical Image Analysis

Publisher: Springer London

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Abstract

The purpose of image segmentation is to generate pixel agglomerations from an image that constitute parts of depicted objects. In medical imaging, segmentation often refers to the delineation of specific structures. Hence, it includes parts of classification as well. Segmentation strategies in medical imaging combine data knowledge with domain knowledge to arrive at the result. Data knowledge refers to assumptions about continuity, homogeneity, and local smoothness of image features within segments. Domain knowledge represents information about the objects to be delineated. In this chapter, basic strategies for integrating the two types of knowledge into the segmentation process will be discussed. We will also describe basic segmentation methods that are popular in medical image analysis.

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previous chapter Feature Detection

next chapter Segmentation in Feature Space

If the model is mainly based on its parameterization and requires frequent feedback, it again would indicate a poor design of the segmentation model. This should not happen in practice, because in such case the success of segmentation would vary substantially with different parameter settings making segmentation time-consuming and awkward to use.

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Title: Segmentation: Principles and Basic Techniques
Author: Klaus D. Toennies
Publisher: Springer London
Book: Guide to Medical Image Analysis
Print ISBN: 978-1-4471-7318-2

Electronic ISBN: 978-1-4471-7320-5

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-1-4471-7320-5_6

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