IMAGE SEGMENTATION AND NCUTS
Main Article Content
Abstract
In the past decades, many studies have been conducted in computer vision and image segmentation. Image segmentation is the process of image preprocessing in most image processing applications. We summarize and evaluate image segmentation techniques and categorize them, including edge detection, thresholding, partial differential equation, clustering, and graph-based. Next, we present the pros and cons of the Ncuts algorithm, which is typical of graph-based image segmentation. The Ncuts algorithm was introduced in 2000 and showed optimal results for image processing and other applications.
Keywords
eigenvalue, graph-cut, Ncuts
Article Details
References
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Weickert, J. (2001). Efficient image segmentation using partial differential equations and morphology. Pattern Recognition, 34(9), 1813-1824.
Yang, X., Shen, X., Long, J., & Chen, H. (2012). An Improved Median-based Otsu Image Thresholding Algorithm. AASRI Procedia, 3, 468-473.
Zaitoun, N. M., & Aqel, M. J. (2015). Survey on Image Segmentation Techniques. International Conference on Communication. Management and Information Technology (ICCMIT 2015), 797-806.
Zhang, L., Zhang, D., & Peng, B. (2013). A survey of graph theoretical approaches to image segmentation. Pattern Recognition, 46(3), 1020-1038.
Zhu, H., Zhang, J., Xu, G., & Deng, L. (2021). Tensor Field Graph-Cut for Image Segmentation: A Non-Convex Perspective. IEEE Transactions on Circuits and Systems for Video Technology, 31(3), 1103-1113.
Bai, X., Zhang, Y., Liu, H., & Chen, Z. (2019). Similarity Measure-Based Possibilistic FCM with Label Information for Brain MRI Segmentation. IEEE Transactions on Cybernetics, 49,
2618-2630.
Bejar, H. H. C., Guimaraes, S. J., & Miranda, P. A. V. (2020). Efficient hierarchical graph partitioning for image segmentation by optimum oriented cuts. Pattern Recognition Letters, 131, 185-192.
Bezdek, J. C. (2013). Pattern Recognition with Fuzzy Objective Function Algorithms. Springer Science & Business Media.
Chandra, S. K., & Bajpai, M. K. (2019). Mesh free alternate directional implicit method based three dimensional super-diffusive model for benign brain tumor segmentation. Computers & Mathematics with Applications, 77(12), 3212-3223.
Cheng, H. D., Jiang, X. H., Sun, Y., & Wang, J. L. (2001). Color image segmentation: advances and prospects, Pattern Recognition, 34, 2259-2281.
Chen, Y., Zhang, H., Zheng, Y., Jeon, B., & Wu, Q. M. J. (2016). An improved anisotropic hierarchical fuzzy c-means method based on multivariate student t-distribution for brain MRI segmentation. Pattern Recognition, 60, 778-792.
Dass, R., Priyanka, & Devi, S. (2012). Image Segmentation Techniques. International Journal on Electronics & Communication Technology, 3(1).
Dhankhar, P., & Sahu, N. (2013). A Review and Research of Edge Detection Techniques for Image Segmentation. International Journal of Computer Science and Mobile Computing, 2(7), 86-92.
Ding, Y., & Fu, X. (2016). Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing, 188, 233-238.
Dimauro, G., & Simone, L. (2020). Novel Biased Normalized Cuts Approach for the Automatic Segmentation of the Conjunctiva. MDPI Multidisciplinary Digital Publishing Institute, 9(6).
Ganesan, P., & Sajiv, G. (2017). A comprehensive study of edge detection for image processing applications. International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS 2017), 2018, 1-6.
Golub, G. H. & Loan, C. F. V., (2013). Matrix Computations. John Hopkins University Press.
Guo, F., Ng, M., Goubran, M., Petersen, S. E., Piechnik, S. K., Neubauer, S., & Wright, G. (2020). Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach. Medical Image Analysis, 61.
Hawas, A. R., Guo, Y., Du, C., Polat, K., & Ashour, A. S. (2020). OCE-NGC: A neutrosophic graph cut algorithm using optimized clustering estimation algorithm for dermoscopic skin lesion segmentation. Applied Soft Computing, 86.
Hoang, N. D., & Nguyen, Q. L. (2018). Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms. Advances in Civil Engineering, 2018.
Huang, H., Meng, F., Zhou, S., Jiang, F., & Manogaran, G. (2019). Brain Image Segmentation Based on FCM Clustering Algorithm and Rough Set. IEEE Access, 7, 12386-12396.
Huttenlocher, D. P., & Felzenszwalb, P. F. (2004). Efficient graph based image segmetnation. International Journal of Computer Vision, 59(2), 167-181.
Jain, A. K., Prabhakar, S., Member, S., & Hong, L. (1999). A Multichannel Approach to Fingerprint Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4), 348-359.
Kamil, M. Y., & Salih, A. M. (2019). Mammography Images Segmentation via Fuzzy C-mean and K-mean. International Journal of Intelligent Engineering and Systems, 12(1).
Khairuzzaman, A. K. M., & Chaudhury, S. (2017). Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Systems with Applications, 86, 64-76.
Kirti, & Bhatnagar, A. (2017). Image Segmentation Using Canny Edge Detection Technique. International Journal of Techno-Management Research, 4(4).
Kouhi, A., Seyedarabi, H., & Aghagolzadeh, A. (2020). Robust FCM clustering algorithm with combined spatial constraint and membership matrix local information for brain MRI segmentation. Expert Systems with Applications, 146.
Luo, S., Tai, X. Ch., Huo, L., Wang, Y., & Glowinski, R. (2019). Convex Shape Prior for Multi-object Segmentation Using a Single Level Set Function. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 613-621.
Manic, K. S., Priya, R. K., & Rajinikanth, V. (2016). Image Multithresholding based on Kapur/Tsallis Entropy and Firefly Algorithm. Indian Journal of Science and Technology, 9(12).
Misal, A., & Singh, M. (2013). A survey paper on various visual image segmentation techniques. International Journal of Computer Science and Management Research, 2.
Ruthotto, L., & Haber, E. (2020). Deep Neural Networks Motivated by Partial Differential Equations. Journal of Mathematical Imaging and Vision, 62, 352-364.
Senthilkumaran, N. & Rajesh, R. (2009). Edge Detection Techniques for Image Segmentation A Survey of Soft Computing Approaches. International Journal of Recent Trends in Engineering, 1(2).
Sezgin, M., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146-165.
Shi, J., & Malik, J. (2000). Normalized cuts and Image Segmentation. Pattern Analysis and Machine Intelligence. IEEE Trans., 22, 888-905.
Wang, Z., Jensen, J. R., & Im, J. (2010). An automatic region-based image segmentation algorithm for remote sensing applications. Environmental Modelling & Software, 25, 1149-1165.
Wang, J., Kong, J., Lu, Y., Qi, M., & Zhang, B. (2008). A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Computerized Medical Imaging and Graphics, 8, 685-698.
Wang, S., & Siskind, J. M. (2001). Image Segmentation with Minimum Mean Cut. IEEE International Conference on Computer Vision (ICCV 2001), 1, 517-524.
Wang, C., Oda, M., Hayashi, Y., Yoshino, Y., Yamamoto, T., Frangi, A. F., & Mori, K. (2020). Tensor-cut: A tensor-based graph-cut blood vessel segmentation method and its application to renal artery segmentation. Medical Image Analysis, 60.
Wang, S., & Siskind, J. M. (2003). Image Segmentation with Ratio Cut. Pattern Analysis and Machine Intelligence, IEEE Trans., 25(6), 675-690.
Wang, C., Lin, X., & Chen, C. (2019). Gravel Image Auto-Segmentation Based on an Improved Normalized Cuts Algorithm. Journal of Applied Mathematics and Physics, 7(3).
Weickert, J. (2001). Efficient image segmentation using partial differential equations and morphology. Pattern Recognition, 34(9), 1813-1824.
Yang, X., Shen, X., Long, J., & Chen, H. (2012). An Improved Median-based Otsu Image Thresholding Algorithm. AASRI Procedia, 3, 468-473.
Zaitoun, N. M., & Aqel, M. J. (2015). Survey on Image Segmentation Techniques. International Conference on Communication. Management and Information Technology (ICCMIT 2015), 797-806.
Zhang, L., Zhang, D., & Peng, B. (2013). A survey of graph theoretical approaches to image segmentation. Pattern Recognition, 46(3), 1020-1038.
Zhu, H., Zhang, J., Xu, G., & Deng, L. (2021). Tensor Field Graph-Cut for Image Segmentation: A Non-Convex Perspective. IEEE Transactions on Circuits and Systems for Video Technology, 31(3), 1103-1113.