Digital medical images segmentation by active contour model based on the signed pressure force function
The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM d...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/111984/ http://psasir.upm.edu.my/id/eprint/111984/1/81474.pdf |
| _version_ | 1848865826723069952 |
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| author | Azman, N. F. Jumaat, Abdul Kadir Badarul Azam, A. S. Mohd Ghani, N. A. S Maasar, M. A Laham, Mohamed Faris Nek Abd Rahman, Normahirah |
| author_facet | Azman, N. F. Jumaat, Abdul Kadir Badarul Azam, A. S. Mohd Ghani, N. A. S Maasar, M. A Laham, Mohamed Faris Nek Abd Rahman, Normahirah |
| author_sort | Azman, N. F. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM demonstrates effectiveness in image segmentation. However, it may fail if the targeted object is close to a neighbouring object. Additionally, the presence of intensity inhomogeneity and noise in medical images degrades segmentation accuracy and local target areas. Thus, we proposed a new SPF-based ACM, namely the Selective Segmentation with Signed Pressure Force 1 (SSPF1) model, by incorporating the ideas of the SPF function and the distance fitting term based on geometrical constraints. Then, the new SSPF1 model was extended by incorporating an image enhancement technique to develop our second new model, termed the Selective Segmentation with Signed Pressure Force 2 (SSPF2). Numerical results indicated that the SSPF2 model was more recommended than SSPF1 as the SSPF2 model was approximately 4.7% more accurate, as indicated by the Jaccard value and was about 112 times faster in segmenting noisy images compared to the existing selective segmentation model. |
| first_indexed | 2025-11-15T14:10:53Z |
| format | Article |
| id | upm-111984 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:10:53Z |
| publishDate | 2024 |
| publisher | Universiti Utara Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1119842024-09-17T02:20:48Z http://psasir.upm.edu.my/id/eprint/111984/ Digital medical images segmentation by active contour model based on the signed pressure force function Azman, N. F. Jumaat, Abdul Kadir Badarul Azam, A. S. Mohd Ghani, N. A. S Maasar, M. A Laham, Mohamed Faris Nek Abd Rahman, Normahirah The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM demonstrates effectiveness in image segmentation. However, it may fail if the targeted object is close to a neighbouring object. Additionally, the presence of intensity inhomogeneity and noise in medical images degrades segmentation accuracy and local target areas. Thus, we proposed a new SPF-based ACM, namely the Selective Segmentation with Signed Pressure Force 1 (SSPF1) model, by incorporating the ideas of the SPF function and the distance fitting term based on geometrical constraints. Then, the new SSPF1 model was extended by incorporating an image enhancement technique to develop our second new model, termed the Selective Segmentation with Signed Pressure Force 2 (SSPF2). Numerical results indicated that the SSPF2 model was more recommended than SSPF1 as the SSPF2 model was approximately 4.7% more accurate, as indicated by the Jaccard value and was about 112 times faster in segmenting noisy images compared to the existing selective segmentation model. Universiti Utara Malaysia Press 2024-07-28 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/111984/1/81474.pdf Azman, N. F. and Jumaat, Abdul Kadir and Badarul Azam, A. S. and Mohd Ghani, N. A. S and Maasar, M. A and Laham, Mohamed Faris and Nek Abd Rahman, Normahirah (2024) Digital medical images segmentation by active contour model based on the signed pressure force function. Journal of Information and Communication Technology, 23 (3). pp. 393-419. ISSN 1675-414X; EISSN: 2180-3862 https://www.e-journal.uum.edu.my/index.php/jict/article/view/22863 10.32890/jict2024.23.3.2 |
| spellingShingle | Azman, N. F. Jumaat, Abdul Kadir Badarul Azam, A. S. Mohd Ghani, N. A. S Maasar, M. A Laham, Mohamed Faris Nek Abd Rahman, Normahirah Digital medical images segmentation by active contour model based on the signed pressure force function |
| title | Digital medical images segmentation by active contour model based on the signed pressure force function |
| title_full | Digital medical images segmentation by active contour model based on the signed pressure force function |
| title_fullStr | Digital medical images segmentation by active contour model based on the signed pressure force function |
| title_full_unstemmed | Digital medical images segmentation by active contour model based on the signed pressure force function |
| title_short | Digital medical images segmentation by active contour model based on the signed pressure force function |
| title_sort | digital medical images segmentation by active contour model based on the signed pressure force function |
| url | http://psasir.upm.edu.my/id/eprint/111984/ http://psasir.upm.edu.my/id/eprint/111984/ http://psasir.upm.edu.my/id/eprint/111984/ http://psasir.upm.edu.my/id/eprint/111984/1/81474.pdf |