FUZZY REGION MERGING WITH HIERARCHICAL CLUSTERING TO FIND OPTIMAL INITIALIZATION OF FUZZY REGION IN IMAGE SEGMENTATION
Wawan GUNAWAN
wawan.gunawan@radenintan.ac.idUniversitas Islam Negeri Raden Intan Lampung: Bandar Lampung (Indonesia)
https://orcid.org/0000-0002-9034-459X
Abstract
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.
Keywords:
Image segmentation, Interactive image segmentation, Initialization fuzzy regionsReferences
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
Google Scholar
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
Google Scholar
Arifin, A. Z., & Asano, A. (2005). Image thresholding by measuring the fuzzy sets. Information Dan Technology Seminar (pp. 189-194).
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Authors
Wawan GUNAWANwawan.gunawan@radenintan.ac.id
Universitas Islam Negeri Raden Intan Lampung: Bandar Lampung Indonesia
https://orcid.org/0000-0002-9034-459X
Statistics
Abstract views: 52PDF downloads: 12
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Nasir ALAWAD, Afaf ALSEADY, FUZZY CONTROLLER OF MODEL REDUCTION DISTILLATION COLUMN WITH MINIMAL RULES , Applied Computer Science: Vol. 16 No. 2 (2020)
- Venkatesh BHANDAGE, Manohara PAI M. M., SEMANTIC SEGMENTATION OF ALGAL BLOOMS ON THE OCEAN SURFACE USING SENTINEL 3 CHL_NN BAND IMAGERY , Applied Computer Science: Vol. 20 No. 3 (2024)
- Sri INDRA MAIYANTI, Anita DESIANI, Syafrina LAMIN, P PUSPITAHATI, Muhammad ARHAMI, Nuni GOFAR, Destika CAHYANA, ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION , Applied Computer Science: Vol. 19 No. 3 (2023)
- Anusha NALLAPAREDDY, DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE , Applied Computer Science: Vol. 18 No. 1 (2022)
- Andrzej Jardzioch, Wioletta Marczak, APPLICATION OF A FUZZY CONTROLLER IN THE PROCESS OF AUTOMATED POLYETHYLENE FILM THICKNESS CONTROL , Applied Computer Science: Vol. 17 No. 3 (2021)
- Edyta ŁUKASIK, Emilia ŁABUĆ, ANALYSIS OF THE POSSIBILITY OF USING THE SINGULAR VALUE DECOMPOSITION IN IMAGE COMPRESSION , Applied Computer Science: Vol. 18 No. 4 (2022)
- Nancy WOODS, Charles ROBERT, ENCAPSULATION OF IMAGE METADATA FOR EASE OF RETRIEVAL AND MOBILITY , Applied Computer Science: Vol. 15 No. 1 (2019)
- Józef MATUSZEK, Tomasz SENETA, Aleksander MOCZAŁA, FUZZY ASSESSMENT OF MANUFACTURABILITY DESIGN FOR MACHINING , Applied Computer Science: Vol. 15 No. 3 (2019)
- Sara SALEHI, FUZZY MULTIPLE CRITERIA GROUP DECISION-MAKING IN PERFORMANCE EVALUATION OF MANUFACTURING COMPANIES , Applied Computer Science: Vol. 19 No. 3 (2023)
- Muayed S AL-HUSEINY, Ahmed S SAJIT, BREAST CANCER CAD SYSTEM BY USING TRANSFER LEARNING AND ENHANCED ROI , Applied Computer Science: Vol. 18 No. 1 (2022)
You may also start an advanced similarity search for this article.
