Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods
Accurate 3D computer models of the brain, and also of parts of its structure such as the corpus callosum (CC) are increasingly used in routine clinical diagnostics. This study presents comparative research to assess the utility and performance of three active contour methods (ACMs) for segmenting th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Online Access: | https://www.mdpi.com/2073-8994/10/11/589 |
| _version_ | 1798003714453143552 |
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| author | Marcin Ciecholewski Jan H. Spodnik |
| author_facet | Marcin Ciecholewski Jan H. Spodnik |
| author_sort | Marcin Ciecholewski |
| collection | DOAJ |
| description | Accurate 3D computer models of the brain, and also of parts of its structure such as the corpus callosum (CC) are increasingly used in routine clinical diagnostics. This study presents comparative research to assess the utility and performance of three active contour methods (ACMs) for segmenting the CC from magnetic resonance (MR) images of the brain, namely: an edge-based active contour model using an inflation/deflation force with a damping coefficient (EM), the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method and the Distance Regularized Level Set Evolution (DRLSE) method. The pre-processing methods applied during research work were to improve the contrast, reduce noise and thus help segment the CC better. In this project, 3D CC models reconstructed based on the segmentations of cross-sections of MR images were also visualised. The results, as measured by quantitative tests of the similarity indice (SI) and overlap value (OV) are the best for the EM model (SI = 92%, OV = 82%) and are comparable to or better than those for other methods taken from a literature review. Furthermore, the properties of the EM model consisting in its ability to both expand and shrink at the same time allow segmentations to be better fitted in subsequent CC slices then in state-of-the art ACMs such as DRLSE or SBGFRLS. The CC contours from previous and subsequent iterations produced by the EM model can be used for initiation in subsequent or previous frames of MR images, which makes the segmentation process easier, particularly as the CC area can increase or decrease in subsequent MR image frames. |
| first_indexed | 2024-04-11T12:12:00Z |
| format | Article |
| id | doaj.art-aa34b57bde4a4ace9cc091a3e0d61cb0 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T12:12:00Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-aa34b57bde4a4ace9cc091a3e0d61cb02022-12-22T04:24:34ZengMDPI AGSymmetry2073-89942018-11-01101158910.3390/sym10110589sym10110589Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour MethodsMarcin Ciecholewski0Jan H. Spodnik1Institute of Informatics, Faculty of Mathematics, Physics and Informatics, University of Gdańsk, 80-308 Gdańsk, PolandDepartment of Anatomy and Neurobiology, Medical University of Gdańsk, 80-211 Gdańsk, PolandAccurate 3D computer models of the brain, and also of parts of its structure such as the corpus callosum (CC) are increasingly used in routine clinical diagnostics. This study presents comparative research to assess the utility and performance of three active contour methods (ACMs) for segmenting the CC from magnetic resonance (MR) images of the brain, namely: an edge-based active contour model using an inflation/deflation force with a damping coefficient (EM), the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method and the Distance Regularized Level Set Evolution (DRLSE) method. The pre-processing methods applied during research work were to improve the contrast, reduce noise and thus help segment the CC better. In this project, 3D CC models reconstructed based on the segmentations of cross-sections of MR images were also visualised. The results, as measured by quantitative tests of the similarity indice (SI) and overlap value (OV) are the best for the EM model (SI = 92%, OV = 82%) and are comparable to or better than those for other methods taken from a literature review. Furthermore, the properties of the EM model consisting in its ability to both expand and shrink at the same time allow segmentations to be better fitted in subsequent CC slices then in state-of-the art ACMs such as DRLSE or SBGFRLS. The CC contours from previous and subsequent iterations produced by the EM model can be used for initiation in subsequent or previous frames of MR images, which makes the segmentation process easier, particularly as the CC area can increase or decrease in subsequent MR image frames.https://www.mdpi.com/2073-8994/10/11/589active contouredge-based active contourregion-based active contourimage processingsegmentation3d visualisationmagnetic resonance imagingcorpus callosumAlzheimer’s Disease |
| spellingShingle | Marcin Ciecholewski Jan H. Spodnik Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods Symmetry active contour edge-based active contour region-based active contour image processing segmentation 3d visualisation magnetic resonance imaging corpus callosum Alzheimer’s Disease |
| title | Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods |
| title_full | Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods |
| title_fullStr | Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods |
| title_full_unstemmed | Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods |
| title_short | Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods |
| title_sort | semi automatic corpus callosum segmentation and 3d visualization using active contour methods |
| topic | active contour edge-based active contour region-based active contour image processing segmentation 3d visualisation magnetic resonance imaging corpus callosum Alzheimer’s Disease |
| url | https://www.mdpi.com/2073-8994/10/11/589 |
| work_keys_str_mv | AT marcinciecholewski semiautomaticcorpuscallosumsegmentationand3dvisualizationusingactivecontourmethods AT janhspodnik semiautomaticcorpuscallosumsegmentationand3dvisualizationusingactivecontourmethods |