Adaptive Energy Weight Based Active Contour Model for Robust Medical Image Segmentation

The performance of the traditional active contour model is subject to the energy weight parameters and initial level set functions, which significantly affect the accuracy of segmentation results. This paper proposes a new robust active contour model to eliminate the above limitations for both 2D si...

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Bibliographic Details
Published inJournal of signal processing systems Vol. 90; no. 3; pp. 449 - 465
Main Authors Li, Xuanping, Wang, Xue, Dai, Yixiang
Format Journal Article
LanguageEnglish
Published New York Springer US 01.03.2018
Springer Nature B.V
Subjects
Boundaries
Circuits and Systems
Clustering
Computer Imaging
Contours
Deformation
Electrical Engineering
Energy
Engineering
Formability
Fuzzy sets
Image Processing and Computer Vision
Image segmentation
Mathematical models
Medical imaging
Parameters
Pattern Recognition
Pattern Recognition and Graphics
Robustness
Shape
Signal,Image and Speech Processing
Two dimensional models
Vision
Active contour model
Adaptive energy weight
Optimized initial level set function
Robust
Medical image segmentation
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Summary:The performance of the traditional active contour model is subject to the energy weight parameters and initial level set functions, which significantly affect the accuracy of segmentation results. This paper proposes a new robust active contour model to eliminate the above limitations for both 2D single-object and multi-object medical image segmentation. The optimal values of energy weight parameters are defined with adaptive energy weight functions to adjust the contribution of each external energy term dynamically. Thus, the energy functional will not be controlled by the large external energy terms, otherwise boundaries leakage would occur. The initial level set functions are optimized with the coarse results obtained by fuzzy C-means clustering method. The evolution of level set functions starts from the locations near the true boundaries. Therefore, the deformable curves could converge to the true boundaries robustly. The proposed algorithm is verified using both synthetic images and medical images from different modalities. The experimental results demonstrate that the proposed algorithm could realize accurate and robust segmentation for medical images even in the presence of noises and weak boundaries.
ISSN:1939-8018
1939-8115
DOI:10.1007/s11265-017-1257-3