Review on Region-Based Segmentation Using Watershed and Region Growing Techniques and their Applications in Different Fields

  • Noor khalid Computer science department-collage of science, Mustansiriyah University, Baghdad, Iraq
Keywords: Segmentation process, Watershed, Region growing, Seeded Region Growing, Unseeded Region Growing


In digital image processing and computer vision, segmentation operation for an image refers to dividing an image into multiple image segments, and the significant purpose of segmentation operation is to depict an image in a way so that the analysis process of the objects of interest is easier and more accurate. The region-based segmentation scheme act for finding similarities between adjacent pixels to detect each region that constructs the image. Similarity scales have based on different features, in a grayscale image, the scale may be referred to as textures and other spatial appearances, and also the variance in intensity of a region and so on. Significantly, many applications in different fields involved region-based segmentation for instance remote sensing, medical application, and others for recognizing interesting objects in an image. In this paper, two techniques for segmentation operation in region-based which are region growing and watershed are reviewed.


Acharjya, P. P., & Ghoshal, D. (2012). Watershed segmentation based on distance transform and edge detection techniques. International Journal of Computer Applications, 52(13).‏

Amoda, N., & Kulkarni, R. K. (2013). Image Segmentation and Detection using Watershed Transform and Region Based Image Retrieval. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2(2), 89-94.

Bhatia, S., & Saxena, K. (2007). Satellite Image Segmentation using Watershed based Algorithms. In International Conference on Soft computing and Intelligent Systems.‏

Brilliant, M., Irianto, S. Y., Karnila, S., & Aziz, R. A. (2020). Land Cover Changes Detection Using Region Growing Segmentation. In Journal of Physics: Conference Series , 1529( 2), 022066. IOP Publishing.‏

Chaturvedi, A., Khanna, R., & Kumar, V. (2016). An Analysis of Region Growing Image Segmentation Schemes. International Journal of Computer Trends and Technology (IJCTT), 34(1), 46-51.

Chen, Q., Yang, X., & Petriu, E. M. (2004, October). Watershed segmentation for binary images with different distance transforms. In Proceedings of the 3rd IEEE international workshop on haptic, audio and visual environments and their applications, 2, 111-116.‏

Chen, Y., Du, H., Yun, Z., Yang, S., Dai, Z., Zhong, L., ... & Yang, W. (2020). Automatic segmentation of individual tooth in dental CBCT images from tooth surface map by a multi-task FCN. IEEE Access, 8, 97296-97309.‏

Chen, Y., He, Y., Wang, J., Li, W., Xing, L., Gao, F., & Shi, G. (2020). Automated cone photoreceptor cell segmentation and identification in adaptive optics scanning laser ophthalmoscope images using morphological processing and watershed algorithm. IEEE Access, 8, 105786-105792.‏

Emre Celebi, M., Kingravi, H.A., Iyatomi, H., Alp Aslandogan, Y., Stoecker, W.V., Moss, R.H., Malters, J.M., Grichnik, J.M., Marghoob, A.A., Rabinovitz, H.S., & Menzies, S.W. (2008). Border detection in dermoscopy images using statistical region merging. Skin Research and Technology, 14(3), 347–353.

Gómez, O., González, J.A., Morales, E.F. (2007). Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning. Rueda, L., Mery, D., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, 4756, 192-201.

Hamdi, M. A. (2011). Modified Algorithm marker-controlled watershed transform for Image segmentation Based on Curvelet Threshold. Canadian Journal on Image Processing and Computer Vision, 2(8).

Jasim, W.N. & Mohammed, R. J. (2021). A Survey on Segmentation Techniques for Image Processing. Iraqi Journal for Electrical and Electronic Engineering, 17(2), 73-93.

Jiang, X., Guo, Y., Chen, H., Zhang, Y., & Lu, Y. (2019). An adaptive region growing based on neutrosophic set in ultrasound domain for image segmentation. IEEE Access, 7, 60584-60593.‏

Kaur, A., Verma, A., & Ssiet, D. (2013). The marker-based watershed segmentation-a review. International Journal of Engineering and Innovative Technology (IJEIT), 3(3), 171-174.‏

Lalaoui, L., Mohamadi, T., & Djaalab, A. (2015). New method for image segmentation . Procedia-Social and Behavioral Sciences, 195, 1971-1980.‏

Li, L., Zhang, X., Pu, L., Pu, L., Tian, B., Zhou, L., & Wei, S. (2019). 3D SAR image background separation based on seeded region growing. IEEE Access, 7, 179842-179863.‏

Manda, M. P., & Kim, H. S. A. (2019). A Simple and Novel Automatic Marker Generation Algorithm for the Watershed Image Segmentation. International Journal of Engineering Research and Technology, 12(12), 2574-2580

Nock, R., & Nielsen, F. (2004). Statistical region merging. IEEE Transactions on pattern analysis and machine intelligence, 26(11), 1452-1458.‏

Roerdink, J. B., & Meijster, A. (2001). The watershed transform: definition, algorithms and parallelization strategies. Fundamental Information, 41(1), 187-228.

Salman, N. (2006). Image Segmentation Based on Watershed and Edge Detection Techniques. The International Arab Journal of Information Technology, 3(2), 104-110.

Seal, A., Das, A., & Sen, P. (2015). Watershed: an image segmentation approach. International Journal of Computer Science and Information Technologies, 6(3), 2295-2297.‏

Shih, F. Y., & Cheng, S. (2005). Automatic seeded region growing for color image segmentation. Image and Vision Computing, 23(10), 877–886.

Singh, I. (2016). Performance Evaluation of Watershed Segmentation Algorithm for Noisy and Noise Free Images Using Adaptive Thresholding and Masking. International Journal of Advances in Electronics and Computer Science, 3(1).

Xu, Y., Mao, Z., Liu, C., & Wang, B. (2018). Pulmonary vessel segmentation via stage-wise convolutional networks with orientation-based region growing optimization. IEEE Access, 6, 71296-71305.‏

Xue, Y., Zhao, J., & Zhang, M. (2021). A watershed-segmentation-based improved algorithm for extracting cultivated land boundaries. Remote Sensing, 13(5), 939.‏

Yang, J., Kang, Z., Cheng, S., Yang, Z., & Akwensi, P. H. (2020). An individual tree segmentation method based on watershed algorithm and three-dimensional spatial distribution analysis from airborne LiDAR point clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1055-1067.‏

Yohannes, E., & Utaminingrum, F. (2016). Building segmentation of satellite image based on area and perimeter using region growing. Indonesian Journal of Electrical Engineering and Computer Science, 3(3), 579-585.‏

Zaitoun, N. M., & Aqel, M. J. (2015). Survey on image segmentation techniques. Procedia Computer Science, 65, 797-806.‏

How to Cite
Noor khalid. (2022). Review on Region-Based Segmentation Using Watershed and Region Growing Techniques and their Applications in Different Fields. Journal La Multiapp, 3(5), 241-249.