Image Segmentation Techniques: A Survey

Authors

  • Sannihit dahiya State Institute of Engg. and Tech. Nilokheri, India https://orcid.org/0000-0001-7594-1292
  • Saurav Puri Dept. of Civil Engineering, State Institute of Engg. and Tech. Nilokheri, India
  • Surender Singh Dept. of CSE, Chandigarh University, Mohali, India

Keywords:

Survey, Image, Fuzzy, PDE, ANN, Segmentation, clustering, threshold CN

Abstract

Segmenting an image utilizing diverse strategies is the primary technique of Image Processing. The technique is broadly utilized in clinical image handling, face acknowledgment, walker location, and so on. Various objects in an image can be recognized using image segmentation methods. Researchers have come up with various image segmentation methods for effective analysis. This paper presents a survey and sums up the designs process of essential image segmentation methods broadly utilized with their advantages and weaknesses.

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References

M. Xess, S. A. Agnes, “Survey on Clustering Based Color Image Segmentation And Novel Approaches To Fcm Algorithm”, IJRET: International Journal of Research in Engineering and Technology, pp. 346-349.

Kavitha, P., & Saraswathi, P. V. (2020). Segmentation for Content Based Satellite Image Retrieval using Fuzzy Clustering. International Journal of Advanced Science and Technology, 29.

Manisha, P., Jayadevan, R., &Sheeba, V. S. (2020, April). Content-based image retrieval through semantic image segmentation. In AIP Conference Proceedings (Vol. 2222, No. 1, p. 030008). AIP Publishing LLC.

Mistry, Y. D. (2020). Textural and color descriptor fusion for efficient content-based image retrieval algorithm. Iran Journal of Computer Science, 3(3), 169-183.

Sultana, F., Sufian, A., & Dutta, P. (2020). Evolution of image segmentation using deep convolutional neural network: a survey. Knowledge-Based Systems, 201, 106062.

Ali, H. M., Kaiser, M. S., & Mahmud, M. (2019, December). Application of convolutional neural network in segmenting brain regions from mri data. In International Conference on Brain Informatics (pp. 136-146). Springer, Cham.

Xu, Y., Wang, Y., Yuan, J., Cheng, Q., Wang, X., & Carson, P. L. (2019). Medical breast ultrasound image segmentation by machine learning. Ultrasonics, 91, 1-9.

Bi, L., Kim, J., Ahn, E., Kumar, A., Feng, D., & Fulham, M. (2019). Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern recognition, 85, 78-89.

Skourt, B. A., El Hassani, A., &Majda, A. (2018). Lung CT image segmentation using deep neural networks. Procedia Computer Science, 127, 109-113.

Remeseiro, B., & Bolon-Canedo, V. (2019). A review of feature selection methods in medical applications. Computers in biology and medicine, 112, 103375.

Hedberg, "A survey of various image segmentation techniques, "Dept. of Electroscience, Box, vol. 118, 2010.

Liang, Y., Zhang, M., & Browne, W. N. (2014, December). Image segmentation: a survey of methods based on evolutionary computation. In Asia-Pacific Conference on Simulated Evolution and Learning (pp. 847-859). Springer, Cham.

D Kaur and Yadwinder Kaur “Various Image Segmentation techniques: A Review, in International Journal of Computer Science and Mobile Computing (IJCSMC), vol. 3, pp 809- 814, 2014.

Manoharan, S. (2020). Performance analysis of clustering-based image segmentation techniques. Journal of Innovative Image Processing (JIIP), 2(01), 14-24.

Wang, Z., Wang, E., & Zhu, Y. (2020). Image segmentation evaluation: a survey of methods. Artificial Intelligence Review, 53(8), 5637-5674.

S Inderpal and K Dinesh, “A Review on Different Image segmentation Techniques” IJAR, vol. 4, 2014.

F. C. Monteiro and A. Campilho, "Watershed framework to region-based image segmentation," in Proc. International Conference on Pattern Recognition, ICPR 19th, pp. 1-4, 2008.

M. Hameed, M. Sharif, M. Raza, S. W. Haider, and M. Iqbal, "Framework for the comparison of classifiers for medical image segmentation with transform and moment-based features," Research Journal of Recent Sciences, vol. 2277, p. 2502, 2012.

R. Patil and K. Jondhale, "Edge based technique to estimate number of clusters in k- means color image segmentation," in Proc. 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp. 117-121, 2010.

Fabijanska, "Variance filter for edge detection and edge-based image segmentation," in Proc. International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), pp. 151-154, 2011.

M. J. Islam, S. Basalamah, M. Ahmadi, and M. A. S. hmed, "Capsule image segmentation in pharmaceutical applications using edge-based techniques," IEEE International Conference on Electro/Information Technology (EIT), pp. 1- 5, 2011.

M. SHARIF, M. RAZA, and S. MOHSIN, "Face recognition using edge information and DCT, "Sindh Univ. Res. Jour.(Sci. Ser.), vol. 43, no. 2, pp. 209-214,2011.

Gupta, D., & Anand, R. S. (2017). A hybrid edge-based segmentation approach for ultrasound medical images. Biomedical Signal Processing and Control, 31, 116-126.

Šostak, A., Uljane, I., & Eklund, P. (2020, June). Fuzzy Relational Mathematical Morphology: Erosion and Dilation. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 712-725). Springer, Cham.

Balado, J., Van Oosterom, P., Díaz-Vilariño, L., &Meijers, M. (2020). Mathematical morphology directly applied to point cloud data. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 208-220.

Srinivasan, A., &Sadagopan, S. (2020). Rough fuzzy region based bounded support fuzzy C-means clustering for brain MR image segmentation. Journal of Ambient Intelligence and Humanized Computing, 1-14.

S. Kobashi and J. K. Udupa, "Fuzzy object model based fuzzy connectedness image segmentation of newborn brain MR images," in Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1422-1427, 2012.

Nafisuddin Khan., & Arya, K. V. (2020). A new fuzzy rule-based pixel organization scheme for optimal edge detection and impulse noise removal. Multimedia Tools and Applications, 1-27.

M. R. Khokher, A. Ghafoor, and A. M. Siddiqui, "Image segmentation using fuzzy rule-based system and graph cuts," in Proc. 12th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 1148-1153, 2012.

M. Sharif, S. Mohsin, M. J. Jamal, and M. Raza, "Illumination normalization preprocessing for face recognition," in Proc. International Conference on Environmental Science and Information Application Technology (ESIAT), pp. 44-47, 2010.

Guo, R., Shen, X., & Kang, H. (2020). Image segmentation algorithm based on partial differential equation. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-7.

F. Zhang, S. Guo, and X. Qian, "Segmentation for finger vein image based on PDEs denoising," in Proc. 3rd International Conference on Biomedical Engineering and Informatics (BMEI), pp. 531-535, 2010.

Yuan and S. Liang, "Segmentation of color image based on partial differential equations," in Proc. Fourth International Symposium on Computational Intelligence and Design (ISCID), pp. 238-240,2011.

W. Zhao, J. Zhang, P. Li, and Y. Li, "Study of image segmentation algorithm based on textural features and neural network," in International Conference on Intelligent Computing and Cognitive Informatics (ICICCI), pp. 300-303, 2010.

M. Sharif, M. Y. Javed, and S. Mohsin, "Face recognition based on facial features," Research Journal of Applied Sciences, Engineering and Technology, vol. 4, pp. 2879-2886, 2012.

M. Yasmin, M. Sharif, and S. Mohsin, "Neural networks in medical imaging applications: A survey, "World Applied Sciences Journal, vol. 22, pp. 85-96, 2013.

Mohan, D., & Raj, M. G. (2020). Quality Analysis of Rice Grains using ANN and SVM. Journal of Critical Reviews, 7(1), 395-402.

S. A. Ahmed, S. Dey, and K. K. Sarma, "Image texture classification using Artificial Neural Network (ANN)," in Proc. 2nd National Conference on Emerging Trends and Applications in Computer Science (NCETACS), pp. 1-4, 2011.

M. Sharif, M. Raza, S. Mohsin, and J. H. Shah, "Microscopic feature extraction method, "Int. J. Advanced Networking and Applications, vol. 4, pp. 1700-1703, 2013.

Irum, M. Raza, and M. Sharif, "Morphological techniques for medical images: A review, "Research Journal of Applied Sciences, vol. 4, 2012.

Gonzalez Rafel C, Richard E Woods, “Digital Image Processing” Pearson Education, 3rd Edition, 2007.

Pare, S., Kumar, A., Singh, G. K., & Bajaj, V. (2020). Image segmentation using multilevel thresholding: a research review. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44(1), 1-29.

Xu, L. Wang, S. Feng, and Y. Qu, "Threshold-based level set method of image segmentation on Intelligent Networks and Intelligent Systems (ICINIS), pp. 703-706, 2010.

M. Yasmin, M. Sharif, S. Masood, M. Raza, and S. Mohsin, "Brain image enhancement-A survey," World Applied Sciences Journal, vol. 17, pp. 1192-1204, 2012.

Jiang, M. R. Frater, and M. Pickering, "Threshold-based image segmentation through an improved particle swarm optimization," in Proc. International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 1-5, 2012.

D. Barbosa, T. Dietenbeck, J. Schaerer, J. D'hooge, D. Friboulet, and O. Bernard, "B- spline explicit active surfaces: An efficient framework for real-time 3-D region-based segmentation," IEEE Transactions on Image Processing, vol. 21, pp. 241-251, 2012.

Baziyad, M., Rabie, T., & Kamel, I. (2020, April). Achieving stronger compaction for dct-based steganography: A region-growing approach. In World Conference on Information Systems and Technologies (pp. 251-261). Springer, Cham.

S. M. M. Sharif, M. J. Jamal, M. Y. Javed, and M. Raza, "Face recognition for disguised variations using gabor feature extraction, "Australian Journal of Basic and Applied Sciences, vol. 5, pp. 1648-1656, 2011.

M. Sharif, S. Mohsin, M. Y. Javed, and M. A. Ali, "Single image face recognition using Laplacian of Gaussian and discrete cosine transforms," Int. Arab J. Inf. Technol., vol. 9, pp. 562-570, 2012.

Manoharan, S. (2020). Performance analysis of clustering-based image segmentation techniques. Journal of Innovative Image Processing (JIIP), 2(01), 14-24.

Hassan, M. R., Ema, R. R., & Islam, T. (2017). Color image segmentation using automated K-means clustering with RGB and HSV color spaces. Global Journal of Computer Science and Technology.

Basar, S., Ali, M., Ochoa-Ruiz, G., Zareei, M., Waheed, A., & Adnan, A. (2020). Unsupervised color image segmentation: A case of RGB histogram-based K-means clustering initialization. Plos one, 15(10), e0240015.

Celebi M E, Kingravi H A and Vela P A,” A comparative study of efficient initialization methods for the k-means clustering algorithm”. Expert Systems with Applications”, 2013, 40(1) pp. 200-210.

Reddy, M., Makara, V., & Satish, R. U. V. N. (2017). Divisive Hierarchical Clustering with K-means and Agglomerative Hierarchical Clustering. Int J of Comp Science Trands and Tech (IJCST), 5(5), 5-11.

Tokuda, E. K., Comin, C. H., & Costa, L. D. F. (2020). Revisiting Agglomerative Clustering. arXiv preprint arXiv:2005.07995.

Ghufron, G., Surarso, B., &Gernowo, R. (2020). The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering. JurnalIlmiah KURSOR, 10(3).

Xiao, J., Lu, J., & Li, X. (2017). Davies Bouldin Index based hierarchical initialization K-means. Intelligent Data Analysis, 21(6), 1327-1338.

Gupta, T., & Panda, S. P. (2019, February). Clustering validation of CLARA and K-means using silhouette & DUNN measures on Iris dataset. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 10-13). IEEE.

Ncir, C. E. B., Hamza, A., &Bouaguel, W. (2021). Parallel and scalable Dunn Index for the validation of big data clusters. Parallel Computing, 102751.

Sarma, R., & Gupta, Y. K. (2021). A comparative study of new and existing segmentation techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 1022, No. 1, p. 012027). IOP Publishing.

Agrawal, A. P., & Tyagi, N. (2020). REVIEW ON DIGITAL IMAGE SEGMENTATION TECHNIQUES. Journal of Critical Reviews, 7(3), 779-784.

Jeevitha, K., Iyswariya, A., RamKumar, V., Basha, S. M., & Kumar, V. P. (2020). A REVIEW ON VARIOUS SEGMENTATION TECHNIQUES IN IMAGE PROCESSSING. European Journal of Molecular & Clinical Medicine, 7(4), 1342-1348.

Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., &Terzopoulos, D. (2020). Image segmentation using deep learning: A survey. arXiv preprint arXiv:2001.05566

Manoharan, S. (2020). Performance analysis of clustering-based image segmentation techniques. Journal of Innovative Image Processing (JIIP), 2(01), 14-24.

Hassan, M. R., Ema, R. R., & Islam, T. (2017). Color image segmentation using automated K-means clustering with RGB and HSV color spaces. Global Journal of Computer Science and Technology.

Basar, S., Ali, M., Ochoa-Ruiz, G., Zareei, M., Waheed, A., & Adnan, A. (2020). Unsupervised color image segmentation: A case of RGB histogram-based K-means clustering initialization. Plos one, 15(10), e0240015.

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Published

2021-05-25

How to Cite

[1]
S. dahiya, S. . Puri, and S. . Singh, “Image Segmentation Techniques: A Survey”, International Journal of Engineering and Applied Physics, vol. 1, no. 2, pp. 127–135, May 2021.

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