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Weighted level set evolution based on local edge features for medical image segmentation
journal contribution
posted on 2017-04-01, 00:00 authored by Alaa Khadidos, Victor Sanchez, Chang-Tsun LiChang-Tsun LiLevel set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow field and the evolving contour's normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than state-of-the-art edge-based level set segmentation methods, particularly around weak edges.
History
Journal
IEEE transactions on image processingVolume
26Issue
4Pagination
1979 - 1991Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
eISSN
1941-0042Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2017, IEEEUsage metrics
Keywords
Image segmentationmedical imagesactive contourslevel set methodsScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringACTIVE CONTOURS DRIVENFITTING ENERGYLEFT-VENTRICLECT IMAGESREGIONMODELSKULLMUMFORDArtificial Intelligence and Image Processing
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