Morphological region-based initial contour algorithm for level set methods in image segmentation

Initial Contour (IC) is the essential step in level set image segmentation methods due to start the efficient process. However, the main issue with IC is how to generate the automatic technique in order to reduce the human interaction and moreover, suitable IC to have accurate result. In this paper...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 76; no. 2; pp. 2185 - 2201
Main Authors: Rad, Abdolvahab Ehsani, Mohd Rahim, Mohd Shafry, Kolivand, Hoshang, Mat Amin, Ismail Bin
Format: Journal Article
Language:English
Published: New York Springer US 01.01.2017
Springer Nature B.V
Subjects:
Boundaries
Computational efficiency
Computer Communication Networks
Computer Science
Contours
Data Structures and Information Theory
Image segmentation
Mathematical analysis
Mathematical models
Morphology
Multimedia Information Systems
Shape
Special Purpose and Application-Based Systems
Image segmentation
Initial contour
Level set
Image processing
ISSN:1380-7501, 1573-7721
Online Access:Get full text
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Summary:Initial Contour (IC) is the essential step in level set image segmentation methods due to start the efficient process. However, the main issue with IC is how to generate the automatic technique in order to reduce the human interaction and moreover, suitable IC to have accurate result. In this paper a new technique which we called Morphological Region-Based Initial Contour (MRBIC), is proposed to overcome this issue. The idea is to generate the most suitable IC since the manual initialization of the level set function surface is a well-known drawback for accurate segmentation which has dependency on selection of IC and wrong selection will affect the result. We have utilized the statistical and morphological information inside and outside the contour to establish a region-based map function. This function is able to find the suitable IC on images to perform by level set methods. Experiments on synthetic and real images demonstrate the robustness of segmentation process using MRBIC method even on noisy images and with weak boundary. Furthermore, computational cost of segmentation process will be reduced using MRBIC.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-015-3196-y