Image Segmentation Techniques: An In-Depth Review and Analysis
DOI:
https://doi.org/10.29304/jqcsm.2024.16.21613Keywords:
Image Segmentation, Medical Image Analysis, Segmentation Algorithms, Deep Learning - Based Segmentation, Semantic SegmentationAbstract
In the fields of computer vision and image processing, image segmentation is a very important task. The image is partitioned into segments or regions then the visual data can be understood, analyzed and used easily. In this article, a comprehensive review of image segmentation methods is presented. It covers both the strengths and the advantages of some techniques as well as the weaknesses and limitations of other algorithms. The techniques used in image segmentation can be divided into four groups based on thresholding, region, edge and deep learning techniques. The fundamental principles, popularity of algorithms and evaluation metrics are mentioned. Also, the applications domains and future directions with current challenges are presented.
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Y. Wang, U. Ahsan, H. Li, and M. Hagen, “A Comprehensive Review of Modern Object Segmentation Approaches,” 2022, Now Publishers Inc. doi: 10.1561/0600000097.
Y. Yu et al., “Techniques and Challenges of Image Segmentation: A Review,” Mar. 01, 2023, MDPI. doi: 10.3390/electronics12051199.
S. Abdulateef and M. Salman, “A Comprehensive Review of Image Segmentation Techniques,” Iraqi Journal for Electrical and Electronic Engineering, vol. 17, pp. 166–175, Dec. 2021, doi: 10.37917/ijeee.17.2.18.
R. W. Palmatier, M. B. Houston, and J. Hulland, “Review articles: purpose, process, and structure,” J Acad Mark Sci, vol. 46, no. 1, pp. 1–5, 2018, doi: 10.1007/s11747-017-0563-4.
A. Lateef and M. Albukhnefis, “NUCLEI AND NUCLEOLI SEGMENTATION AND ANALYSIS,” 2016.
P. Daniel, R. Raju, and G. Neelima, “Image Segmentation by using Histogram Thresholding.”
E. P. Mandyartha, F. T. Anggraeny, F. Muttaqin, and F. A. Akbar, “Global and Adaptive Thresholding Technique for White Blood Cell Image Segmentation,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jul. 2020. doi: 10.1088/1742-6596/1569/2/022054.
Q. Dou et al., “3D deeply supervised network for automated segmentation of volumetric medical images,” Med Image Anal, vol. 41, pp. 40–54, Oct. 2017, doi: 10.1016/j.media.2017.05.001.
S. Abdulateef and M. Salman, “A Comprehensive Review of Image Segmentation Techniques,” Iraqi Journal for Electrical and Electronic Engineering, vol. 17, pp. 166–175, Dec. 2021, doi: 10.37917/ijeee.17.2.18.
“Adaptive Thresholding — skimage v0.12.2 docs.” Accessed: Jul. 25, 2024. [Online]. Available: https://scikit-image.org/docs/0.12.x/auto_examples/segmentation/plot_threshold_adaptive.html
M. Elsayed Abd Elaziz et al., “An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-level Thresholding: Real World Example of COVID-19 CT Image Segmentation,” IEEE Access, vol. PP, p. 1, Jul. 2020, doi: 10.1109/ACCESS.2020.3007928.
S. C. Satapathy, N. Sri Madhava Raja, V. Rajinikanth, A. S. Ashour, and N. Dey, “Multi-level image thresholding using Otsu and chaotic bat algorithm,” Neural Comput Appl, vol. 29, no. 12, pp. 1285–1307, 2018, doi: 10.1007/s00521-016-2645-5.
U. Khan et al., “Internet of Medical Things–based decision system for automated classification of Alzheimer’s using three-dimensional views of magnetic resonance imaging scans,” Int J Distrib Sens Netw, vol. 15, no. 3, Mar. 2019, doi: 10.1177/1550147719831186.
M. Elsayed Abd Elaziz et al., “An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-level Thresholding: Real World Example of COVID-19 CT Image Segmentation,” IEEE Access, vol. PP, p. 1, Jul. 2020, doi: 10.1109/ACCESS.2020.3007928.
M. abd elaziz, A. A. Ewees, and A. E. Hassanien, “Whale Optimization Algorithm and Moth-Flame Optimization for Multilevel Thresholding Image Segmentation,” Expert Syst Appl, 2017, [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0957417417302671
M. Amiriebrahimabadi, Z. Rouhi, and N. Mansouri, “A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing,” Archives of Computational Methods in Engineering, 2024, doi: 10.1007/s11831-024-10093-8.
C. L Srinidhi, P. Aparna, and J. Rajan, “Recent Advancements in Retinal Vessel Segmentation,” J Med Syst, vol. 41, no. 4, p. 70, 2017, doi: 10.1007/s10916-017-0719-2.
M. Wang and D. Li, “An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm,” Diagnostics, vol. 12, no. 12, 2022, doi: 10.3390/diagnostics12122971.
“Week 6: Region Growing and Clustering Segmentation).” Accessed: Jul. 25, 2024. [Online]. Available: https://sbme-tutorials.github.io/2019/cv/notes/6_week6.html
H. Wang et al., “Improvement of region-merging image segmentation accuracy using multiple merging criteria,” Remote Sens (Basel), vol. 13, no. 14, Jul. 2021, doi: 10.3390/rs13142782.
A. Kornilov, I. Safonov, and I. Yakimchuk, “A review of watershed implementations for segmentation of volumetric images,” J Imaging, vol. 8, no. 5, p. 127, 2022.
Y. Xue, J. Zhao, and M. Zhang, “A watershed-segmentation-based improved algorithm for extracting cultivated land boundaries,” Remote Sens (Basel), vol. 13, no. 5, p. 939, 2021.
Y. Wu and Q. Li, “The algorithm of watershed color image segmentation based on morphological gradient,” Sensors, vol. 22, no. 21, p. 8202, 2022.
B. Preim and C. Botha, “Image Analysis for Medical Visualization,” Visual Computing for Medicine, pp. 111–175, 2014, doi: 10.1016/B978-0-12-415873-3.00004-3.
W. Phornphatcharaphong and N. Eua-Anant, “Edge-based color image segmentation using particle motion in a vector image field derived from local color distance images,” J Imaging, vol. 6, no. 7, Jul. 2020, doi: 10.3390/jimaging6070072.
E. A. Awalludin, W. N. J. H. W. Yussof, Z. Bachok, M. A. F. Aminudin, M. S. C. Din, and M. S. Hitam, “Monitoring climate change effects on coral reefs using edge-based image segmentation,” International Journal of Electrical and Computer Engineering, vol. 14, no. 1, pp. 398–408, Feb. 2024, doi: 10.11591/ijece.v14i1.pp398-408.
“Edge detection using in-built function in MATLAB - GeeksforGeeks.” Accessed: Jul. 31, 2024. [Online]. Available: https://www.geeksforgeeks.org/edge-detection-using-in-built-function-in-matlab/
“31_Piekar_3(1).pdf”.
C. Bontozoglou and P. Xiao, “Applications of capacitive imaging in human skin texture and hair analysis,” Applied Sciences (Switzerland), vol. 10, no. 1, Jan. 2020, doi: 10.3390/app10010256.
M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” Int J Comput Vis, vol. 1, no. 4, pp. 321–331, 1988, doi: 10.1007/BF00133570.
J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1454–1466, 2001, doi: 10.1109/83.951532.
S. Biswas and R. Hazra, “Robust edge detection based on Modified Moore-Neighbor,” Optik (Stuttg), vol. 168, pp. 931–943, 2018, doi: https://doi.org/10.1016/j.ijleo.2018.05.011.
Z. Zhu, X. He, G. Qi, Y. Li, B. Cong, and Y. Liu, “Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI,” Inf. Fusion, vol. 91, no. C, pp. 376–387, Mar. 2023, doi: 10.1016/j.inffus.2022.10.022.
H. Mittal, A. C. Pandey, M. Saraswat, S. Kumar, R. Pal, and G. Modwel, “A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets,” Multimed Tools Appl, vol. 81, no. 24, pp. 35001–35026, Oct. 2022, doi: 10.1007/s11042-021-10594-9.
R. M. Ribeiro, S. J. F. Guimarães, and Z. K. G. Patrocínio, “Hierarchical graph-based segmentation in detection of object-related regions,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2019, pp. 124–132. doi: 10.1007/978-3-030-13469-3_15.
N. Dhanachandra, K. Manglem, and Y. J. Chanu, “Image Segmentation Using K-means Clustering Algorithm and Subtractive Clustering Algorithm,” in Procedia Computer Science, Elsevier, 2015, pp. 764–771. doi: 10.1016/j.procs.2015.06.090.
G. Cheng and L. Liu, “Survey of image segmentation methods based on clustering,” in 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA), 2020, pp. 1111–1115. doi: 10.1109/ICIBA50161.2020.9277287.
Q. Ye, W. Gao, and W. Zeng, “Color image segmentation using density-based clustering,” in Proceedings - IEEE International Conference on Multimedia and Expo, IEEE Computer Society, 2003, pp. II401–II404. doi: 10.1109/ICME.2003.1221638.
H. Mittal, A. C. Pandey, M. Saraswat, S. Kumar, R. Pal, and G. Modwel, “A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets,” Multimed Tools Appl, vol. 81, no. 24, pp. 35001–35026, Oct. 2022, doi: 10.1007/s11042-021-10594-9.
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image Segmentation Using Deep Learning: A Survey,” Jan. 2020, [Online]. Available: http://arxiv.org/abs/2001.05566
Y. Guo, Y. Liu, T. Georgiou, and M. S. Lew, “A review of semantic segmentation using deep neural networks,” Int J Multimed Inf Retr, vol. 7, no. 2, pp. 87–93, Jun. 2018, doi: 10.1007/s13735-017-0141-z.
J. Jeong, T. S. Yoon, and J. B. Park, “Towards a meaningful 3D map using a 3D lidar and a camera,” Sensors (Switzerland), vol. 18, no. 8, Aug. 2018, doi: 10.3390/s18082571.
Y. Zhang, J. Chu, L. Leng, and J. Miao, “Mask-refined R-CNN: A network for refining object details in instance segmentation,” Sensors (Switzerland), vol. 20, no. 4, Feb. 2020, doi: 10.3390/s20041010.
J. Zeng, H. Ouyang, M. Liu, L. U. Leng, and X. Fu, “Multi-scale YOLACT for instance segmentation,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 9419–9427, Nov. 2022, doi: 10.1016/j.jksuci.2022.09.019.
A. M. Hafiz and G. M. Bhat, “A survey on instance segmentation: state of the art,” Int J Multimed Inf Retr, vol. 9, no. 3, pp. 171–189, Sep. 2020, doi: 10.1007/s13735-020-00195-x.
J. Y. Cha, H. I. Yoon, I. S. Yeo, K. H. Huh, and J. S. Han, “Panoptic segmentation on panoramic radiographs: Deep learning-based segmentation of various structures including maxillary sinus and mandibular canal,” J Clin Med, vol. 10, no. 12, Jun. 2021, doi: 10.3390/jcm10122577.
T. Al-Fatlawi, R. M.hamza, and A. Albukhnefis, “Video Structure Analysis: A survey,” Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 15, Nov. 2023, doi: 10.29304/jqcsm.2023.15.21304.
B. Cheng et al., “Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation.”
S. Xie and Z. Tu, “Holistically-Nested Edge Detection,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1395–1403. doi: 10.1109/ICCV.2015.164.
X. Liu, L. Song, S. Liu, and Y. Zhang, “A review of deep-learning-based medical image segmentation methods,” Sustainability (Switzerland), vol. 13, no. 3, pp. 1–29, Feb. 2021, doi: 10.3390/su13031224.
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image Segmentation Using Deep Learning: A Survey,” Jan. 2020, [Online]. Available: http://arxiv.org/abs/2001.05566
Y. Wang, U. Ahsan, H. Li, and M. Hagen, “A Comprehensive Review of Modern Object Segmentation Approaches,” 2022, Now Publishers Inc. doi: 10.1561/0600000097.
J. Pont-Tuset and F. Marques, “Supervised Evaluation of Image Segmentation and Object Proposal Techniques,” IEEE Trans Pattern Anal Mach Intell, vol. 38, no. 7, pp. 1465–1478, 2016, doi: 10.1109/TPAMI.2015.2481406.
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