Abstract
Active contour models (ACMs) have been widely used in image segmentation to segment objects. However, when it comes to segmenting images with severe intensity inhomogeneity, most current frameworks do not perform well, which can make it difficult to achieve the desired results. To address this issue, a decision-making model is proposed, which involves using enhanced local direction pattern (ELDP) and local directional number pattern (LDNP) texture descriptors to create an encoded-texture ACM. The principal component analysis (PCA) is then used to optimize the two encoded images and reduce the correlations before they are fused. To further improve the performance of the encoded-texture ACM, a function of minimizing energy globally (FMEG) is suggested by applying the vector-valued exploration technique from a non-convex surface to region-based ACMs. This approach enables the development of a model capable of directly building complex decision boundaries. The experimental results show that the proposed encoded-texture ACM outperforms many recent frameworks in terms of robustness and accuracy for segmenting images with intensity inhomogeneity, fuzzy boundaries, and noise. Therefore, the suggested approach provides a more effective and efficient solution to the problem of image segmentation, particularly for challenging images.
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The Caltech-256 dataset used in this study is a public dataset. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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This publication has emanated from research conducted with the financial support of/supported in part by a grant from Science Foundation Ireland under Grant number No. 18/CRT/6183 and is supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106/_P2), Lero SFI Centre for Software (Grant 13/RC/2094/_P2) and is co-funded under the European Regional Development Fund. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.
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RR contributed to conceptualization, investigation, methodology, software, writing the paper. SS contributed to conceptualization, investigation, methodology, software, validation. AF contributed to data preparation and curation, investigation, writing the paper. SJG contributed to data preparation and curation, formal analysis. EBT contributed to methodology, validation. formal analysis. AC contributed to supervisor, reviewing and editing, validation. MB contributed to supervisor, reviewing and editing, validation.
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Ranjbarzadeh, R., Sadeghi, S., Fadaeian, A. et al. ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08983-3
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DOI: https://doi.org/10.1007/s00500-023-08983-3