Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation
Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9245488/ |
| _version_ | 1818349739262869504 |
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| author | Asif Aziz Memon Asim Niaz Shafiullah Soomro Ehtesham Iqbal Asad Munir Kwang Nam Choi |
| author_facet | Asif Aziz Memon Asim Niaz Shafiullah Soomro Ehtesham Iqbal Asad Munir Kwang Nam Choi |
| author_sort | Asif Aziz Memon |
| collection | DOAJ |
| description | Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model's sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods. |
| first_indexed | 2024-12-13T18:10:44Z |
| format | Article |
| id | doaj.art-a6baeede55004823a84f933daaa168b6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:10:44Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a6baeede55004823a84f933daaa168b62022-12-21T23:35:57ZengIEEEIEEE Access2169-35362020-01-01819836819838310.1109/ACCESS.2020.30349089245488Hybrid SPF and KD Operator-Based Active Contour Model for Image SegmentationAsif Aziz Memon0https://orcid.org/0000-0002-9386-0979Asim Niaz1https://orcid.org/0000-0003-3905-9774Shafiullah Soomro2https://orcid.org/0000-0002-4318-5055Ehtesham Iqbal3https://orcid.org/0000-0003-1798-036XAsad Munir4Kwang Nam Choi5https://orcid.org/0000-0002-7420-9216School of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaSTARS Team, Inria Sophia Antipolis, Biot, FranceDepartment of Computer Science, Quaid-e-Awam University of Engineering, Science and Technology (QUEST), Nawabshah, PakistanSchool of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaDepartment of Industrial and Information Engineering, Università degli Studi di Udine, Udine, ItalySchool of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaImage segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model's sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods.https://ieeexplore.ieee.org/document/9245488/Active contourintensity inhomogeneityimage segmentationregion-basedlocal and global intensity |
| spellingShingle | Asif Aziz Memon Asim Niaz Shafiullah Soomro Ehtesham Iqbal Asad Munir Kwang Nam Choi Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation IEEE Access Active contour intensity inhomogeneity image segmentation region-based local and global intensity |
| title | Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation |
| title_full | Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation |
| title_fullStr | Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation |
| title_full_unstemmed | Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation |
| title_short | Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation |
| title_sort | hybrid spf and kd operator based active contour model for image segmentation |
| topic | Active contour intensity inhomogeneity image segmentation region-based local and global intensity |
| url | https://ieeexplore.ieee.org/document/9245488/ |
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