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Weighted level set evolution based on local edge features for medical image segmentation
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Khadidos, Alaa, Sanchez Silva, Victor and Li, Chang-Tsun (2017) Weighted level set evolution based on local edge features for medical image segmentation. IEEE Transactions on Image Processing, 26 (4). pp. 1979-1991. doi:10.1109/TIP.2017.2666042
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Official URL: http://doi.org/10.1109/TIP.2017.2666042
Abstract
Level 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.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Diagnostic imaging, Magnetic resonance imaging, Tomography , X ray medical | ||||||||
| Journal or Publication Title: | IEEE Transactions on Image Processing | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 1057-7149 | ||||||||
| Official Date: | April 2017 | ||||||||
| Dates: |
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| Volume: | 26 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 1979-1991 | ||||||||
| DOI: | 10.1109/TIP.2017.2666042 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Funder: | Engineering and Physical Sciences Research Council (EPSRC), Marie Curie Integration Grant |
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