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Fuzzy region merging with hierarchical clustering to find optimal initialization of fuzzy region in image segmentation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the most important goals in image segmentation is the process of separating the object parts from the image background. Image segmentation is also a fundamental stage in the development of other image applications such as object recognition, target tracking, computer vision, and biomedical image processing. Interactive image segmentation methods with additional user interaction are still popular in research. Interactive image segmentation aims to provide additional information through simple interactions, especially in images with complex objects. Interactive image segmentation with region merging processes has drawbacks, one of which is suboptimal region splitting due to soft color shades, blurred contours, and uneven lighting, referred to in this study as ambiguous regions. However, in the fuzzy region initialization stage after obtaining values from the marker process, there is a possibility of missing or suboptimal determination of fuzzy regions. This is because it only takes the highest gray level value for the background marker and the lowest gray level value for the object marker. In this study, fuzzy region merging using hierarchical clustering is proposed to find optimal initialization for fuzzy regions in image segmentation. Based on the experimental results, the proposed method can achieve optimal segmentation with an average misclassification error value of 2.62% for Natural Images and 9.33% for Dental Images.
Rocznik
Strony
211--220
Opis fizyczny
Bibliogr. 15 poz., fig., tab.
Twórcy
  • Universitas Islam Negeri Raden Intan Lampung: Bandar Lampung
Bibliografia
  • [1] Alemi Koohbanani, N., Jahanifar, M., Zamani Tajadin, N., & Rajpoot, N. (2020). NuClick: A Deep Learning framework for interactive segmentation of microscopic images. Medical Image Analysis, 65, 101771. https://doi.org/10.1016/j.media.2020.101771
  • [2] Alpert, S., Galun, M., Basri, R., & Brandt, A. (2007). Image segmentation by probabilistic bottom-up aggregation and cue integration. 2007 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE. https://doi.org/10.1109/CVPR.2007.383017
  • [3] Arifin, A. Z., & Asano, A. (2005). Image thresholding by measuring the fuzzy sets. Information Dan Technology Seminar (pp. 189-194).
  • [4] Boykov, Y. Y., & Jolly, M.-P. (2001). Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. Eighth IEEE International Conference on Computer Vision. ICCV 2001 (pp. 105-112). IEEE. https://doi.org/10.1109/ICCV.2001.937505
  • [5] Da Fonseca, G. B., Perret, B., Negrel, R., Cousty, J., & Guimarães, S. J. F. (2021). Fuzzy-Marker-Based segmentation using hierarchies. In J. Lindblad, F. Malmberg, & N. Sladoje (Eds.), Discrete Geometry and Mathematical Morphology (Vol. 12708, pp. 391–403). Springer International Publishing. https://doi.org/10.1007/978-3-030-76657-3_28
  • [6] Ding, Z., Wang, T., Sun, Q., & Chen, F. (2023). Rethinking click embedding for deep interactive image segmentation. IEEE Transactions on Industrial Informatics, 19(1), 261-273. https://doi.org/10.1109/TII.2022.3157319
  • [7] Gunawan, W., Arifin, A. Z., Indraswari, R., & Navastara, D. A. (2017). Fuzzy region merging using fuzzy similarity measurement on image segmentation. International Journal of Electrical and Computer Engineering, 7(6), 3402. https://doi.org/10.11591/ijece.v7i6.pp3402-3410
  • [8] Jung, C., Liu, J., Sun, T., Jiao, L., & Shen, Y. (2014). Automatic image segmentation using constraint learning and propagation. Digital Signal Processing, 24, 106-116. https://doi.org/10.1016/j.dsp.2013.09.006
  • [9] Makhlouf, Z., Meraoumia, A., Lakhdar, L., & Haouam, M. Y. (2024). Enhancing medical data security in e-health systems using biometric-based watermarking. Applied Computer Science, 20(1), 28-55. https://doi.org/10.35784/acs-2024-03
  • [10] Mikhailov, I., Chauveau, B., Bourdel, N., & Bartoli, A. (2024). A deep learning-based interactive medical image segmentation framework with sequential memory. Computer Methods and Programs in Biomedicine, 245, 108038. https://doi.org/10.1016/j.cmpb.2024.108038
  • [11] Militello, C., Rundo, L., Dimarco, M., Orlando, A., Conti, V., Woitek, R., D’Angelo, I., Bartolotta, T. V., & Russo, G. (2022). Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering. Biomedical Signal Processing and Control, 71, 103113. https://doi.org/10.1016/j.bspc.2021.103113
  • [12] Nguyen, T. N. A., Cai, J., Zheng, J., & Li, J. (2013). Interactive object segmentation from multi-view images. Journal of Visual Communication and Image Representation, 24(4), 477-485. https://doi.org/10.1016/j.jvcir.2013.02.012
  • [13] Ning, J., Zhang, L., Zhang, D., & Wu, C. (2010). Interactive image segmentation by maximal similarity based region merging. Pattern Recognition, 43(2), 445-456. https://doi.org/10.1016/j.patcog.2009.03.004
  • [14] Ôn Vũ Ngọc, M., Carlinet, E., Fabrizio, J., & Géraud, T. (2023). The Dahu graph-cut for interactive segmentation on 2D/3D images. Pattern Recognition, 136, 109207. https://doi.org/10.1016/j.patcog.2022.109207
  • [15] Sankoh, A. S., Arifin, A. Z., & Wijaya, A. Y. (2016). Extracted pixels similarity features (EPSF) using interactive image segmentation techniques. International Journal of Computer Applications, 136(2), 5-12. https://doi.org/10.5120/ijca2016908236
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b7888d22-26b8-4eac-bc50-cd7e09cfeec9
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