Probabilistic shape‐based segmentation method using level sets
In this study, a novel probabilistic, geometric and dynamic shape‐based level sets method is proposed. The shape prior is coupled with the intensity information to enhance the segmentation results. The two‐dimensional principal component analysis method is applied on the training shapes to represent...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-06-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2012.0226 |
| _version_ | 1797685119119523840 |
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| author | Melih S. Aslan Ahmed Shalaby Hossam Abdelmunim Aly A. Farag |
| author_facet | Melih S. Aslan Ahmed Shalaby Hossam Abdelmunim Aly A. Farag |
| author_sort | Melih S. Aslan |
| collection | DOAJ |
| description | In this study, a novel probabilistic, geometric and dynamic shape‐based level sets method is proposed. The shape prior is coupled with the intensity information to enhance the segmentation results. The two‐dimensional principal component analysis method is applied on the training shapes to represent the shape variation with enough number of shape projections in the training step. The shape model is constructed using the implicit representation of the projected shapes. A new energy functional is proposed (i) to embed the shape model into the image domain and (ii) to estimate the shape coefficients. The proposed method is validated on synthetic and clinical images with various challenges such as the noise, occlusion and missing information. The authors compare their method with some of related works. Experiments show that the proposed segmentation method is more accurate and robust than other alternatives under different challenges. ∗ Note: Colour figures are available in the online version of this paper. |
| first_indexed | 2024-03-12T00:40:51Z |
| format | Article |
| id | doaj.art-07eeca047f424edfa80c47aff75600a9 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:40:51Z |
| publishDate | 2014-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-07eeca047f424edfa80c47aff75600a92023-09-15T07:15:52ZengWileyIET Computer Vision1751-96321751-96402014-06-018318219410.1049/iet-cvi.2012.0226Probabilistic shape‐based segmentation method using level setsMelih S. Aslan0Ahmed Shalaby1Hossam Abdelmunim2Aly A. Farag3ECEUniversity of LouisvilleCVIP LabLUTZ HALL, #006LouisvilleKentucky40209USAECEUniversity of LouisvilleCVIP LabLUTZ HALL, #006LouisvilleKentucky40209USAECEUniversity of LouisvilleCVIP LabLUTZ HALL, #006LouisvilleKentucky40209USAECEUniversity of LouisvilleCVIP LabLUTZ HALL, #006LouisvilleKentucky40209USAIn this study, a novel probabilistic, geometric and dynamic shape‐based level sets method is proposed. The shape prior is coupled with the intensity information to enhance the segmentation results. The two‐dimensional principal component analysis method is applied on the training shapes to represent the shape variation with enough number of shape projections in the training step. The shape model is constructed using the implicit representation of the projected shapes. A new energy functional is proposed (i) to embed the shape model into the image domain and (ii) to estimate the shape coefficients. The proposed method is validated on synthetic and clinical images with various challenges such as the noise, occlusion and missing information. The authors compare their method with some of related works. Experiments show that the proposed segmentation method is more accurate and robust than other alternatives under different challenges. ∗ Note: Colour figures are available in the online version of this paper.https://doi.org/10.1049/iet-cvi.2012.0226image segmentationocclusionmissing informationsynthetic imagesclinical imagesshape coefficients |
| spellingShingle | Melih S. Aslan Ahmed Shalaby Hossam Abdelmunim Aly A. Farag Probabilistic shape‐based segmentation method using level sets IET Computer Vision image segmentation occlusion missing information synthetic images clinical images shape coefficients |
| title | Probabilistic shape‐based segmentation method using level sets |
| title_full | Probabilistic shape‐based segmentation method using level sets |
| title_fullStr | Probabilistic shape‐based segmentation method using level sets |
| title_full_unstemmed | Probabilistic shape‐based segmentation method using level sets |
| title_short | Probabilistic shape‐based segmentation method using level sets |
| title_sort | probabilistic shape based segmentation method using level sets |
| topic | image segmentation occlusion missing information synthetic images clinical images shape coefficients |
| url | https://doi.org/10.1049/iet-cvi.2012.0226 |
| work_keys_str_mv | AT melihsaslan probabilisticshapebasedsegmentationmethodusinglevelsets AT ahmedshalaby probabilisticshapebasedsegmentationmethodusinglevelsets AT hossamabdelmunim probabilisticshapebasedsegmentationmethodusinglevelsets AT alyafarag probabilisticshapebasedsegmentationmethodusinglevelsets |