Abstract
A new methodology for segmenting gaseous object images is introduced. Unlike in case of a rigid object, the edge intensity of a gaseous object varies along the object boundary (edge intensities of some pixels on a gaseous object boundary are weaker than those of small rigid objects or noise itself). Therefore, the conventional edge detectors may not adequately detect boundary-like edge pixels of gaseous objects. We develop a novel object segmenting method using fuzzy algorithm trained by the genetic algorithm. The proposed method consists of a fuzzy-based boundary detector applicable to gaseous as well as rigid objects, and concave region filling to recover object regions. This algorithm is well applicable to medical image such as breast cancer or tumor segmentation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Parker, J.R.: Algorithms for Image Processing and Computer Vision. John Wiley & Sons, Inc., Chichester (1997)
Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs (1989)
Davis, L.S.: A survey of edge detection techniques. Computer Graphics and Image Processing 4, 248–270 (1975)
Wang, L.X.: A course in fuzzy systems and control. Prentice-Hall, Inc., MA (1997)
Goldberg, D.E.: Genetic algorithm in search, optimization and machine learning. Addison-Wesley, Reading (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, SM., Kim, W. (2004). An Algorithm for Segmenting Gaseous Objects on Images. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-24653-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21378-9
Online ISBN: 978-3-540-24653-4
eBook Packages: Springer Book Archive