Enhance watershed algorithms using principal component analysis capabilities

Mohamed, Ahmed F; Soliman, Fayez Wanis Zaki; Mahmoud Ashour, Mohamed

doi:10.30772/qjes.2024.145116.1055

	Enhance watershed algorithms using principal component analysis capabilities
Al-Qadisiyah Journal for Engineering Sciences
Article 2, Volume 17, Issue 1, March 2024, Pages 5-15 PDF (663.8 K)
Document Type: Research Paper
DOI: 10.30772/qjes.2024.145116.1055
Authors
Ahmed F Mohamed^* ; Fayez Wanis Zaki Soliman; Mohamed Mahmoud Ashour
Electronics and Communications Engineering Department, Faculty of Engineering, Mansour University, Dakhlia, Egypt
Abstract
Traditional image segmentation algorithms have many drawbacks, such as over-segmentation and image distortion due to reflected light. The Watershed algorithm is one of the most popular image segmentation algorithms. Over-segmentation errors caused by overlapping targets in the image, as well as noise and glare, must be removed. In this article, we apply image processing using the watershed algorithm and propose to improve the algorithm based on principal component analysis. PCA is a popular technique for analyzing large datasets with many advantages per observation. PCA improves data interpretability while maximizing information content, enabling visualization of multidimensional data by finding image component gradients in a new space called the principle component that is unaffected by noise and reflected light. In contrast, the components mainly containing noise will eliminate with negligible information. This paper introduces three primary steps. The process involves applying the watershed algorithm to the image in the first phase, using the proposed approach (applying the watershed algorithm and suggesting an improvement based on principal component analysis) to the image in the second step, and comparing the outcomes of the two previous processes. Test results show that the suggested technique can achieve accurate and durable target shapes.
Keywords
Image processing; image segmentation; Principal component analysis; Watershed segmentation; Watershed algorithm; Over-segmentation

References
Haq, I.; Ullah, N.; Mazhar, T.; Malik, M.A.; Bano, I. A Novel Brain Tumor Detection and Coloring Technique from 2D MRI Images. Appl. Sci. 2022, 12, 5744. https://doi.org/10.3390/app12115744. Santoshachandra Rao Karanam, Y. Srinivas and M. Vamshi Krishna, Study on image processing using deep learning techniques, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.09.536. G Sumanth Prasad. Implementation of Image Segmentation Algorithms in Digital Image Processing using MATLAB. International Journal for Interdisciplinary Sciences and Engineering Applications. IJISEA - An International Peer- Reviewed Journal 2020, Volume 1 Issue 1. Yu, Y.; Wang, C.; Fu, Q.; Kou, R.; Huang, F.; Yang, B.; Yang, T.;Gao, M. Techniques and Challenges of Image Segmentation: A Review. Electronics 2023, 12, 1199. https://doi.org/10.3390/electronics12051199 Anwesh, K.; Pal, D.; Ganguly, D.; Chatterjee, K.; Roy, S. Number plate recognition from enhanced super-resolution using generative adversarial network. Multimed. Tools Appl. 2022, 1–17. Jin, B.; Cruz, L.; Gonçalves, N. Deep Facial Diagnosis: Deep Transfer Learning from Face Recognition to Facial Diagnosis. IEEE Access 2020, 8, 123649–123661. Zhao, M.; Liu, Q.; Jha, R.; Deng, R.; Yao, T.; Mahadevan-Jansen, A.; Tyska, M.J.; Millis, B.A.; Huo, Y. VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning. In Proceedings of the International Workshop on Machine Learning in Medical Imaging, Strasbourg, France, 27 September 2021; Volume 12966, pp. 437–446. Yao, T.; Qu, C.; Liu, Q.; Deng, R.; Tian, Y.; Xu, J.; Jha, A.; Bao, S.; Zhao, M.; Fogo, A.B.; et al. Compound Figure Separation of Biomedical Images with Side Loss. In Proceedings of the Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, Strasbourg, France, 1 October 2021; Volume 13003, pp. 173–183. Wu, Y.; Li, Q. The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient. Sensors 2022, 22, 8202. https://doi.org/10.3390/s22218202 Anton S. Kornilov and Ilia V. Safonov, An Overview of Watershed Algorithm Implementations in Open Source Libraries. J. Imaging 2018, 4, 123. J. Imaging 2018, 4, 123; mdpi.com/journal/jimaging. doi:10.3390/jimaging4100123. Yu, Y.; Wang, C.; Fu, Q.; Kou, R.; Huang, F.; Yang, B.; Yang, T.; Gao, M. Techniques and Challenges of Image Segmentation: A Review. Electronics 2023, 12, 1199. https://doi.org/10.3390/electronics12051199 Silpa Joseph, IMAGE PROCESSING TECHNIQUES AND ITS APPLICATIONS: AN OVERVIEW, Vol-4 Issue-3 2018 IJARIIE-ISSN(O)-2395-4396, 8745 www.ijariie.com 2168. P. Narkhede, Review of Image Segmentation Techniques, Vol-4 Issue-3 2018 IJARIIE-ISSN(O)-2395-4396, 8745 www.ijariie.com 2168. International Journal of Science and Modern Engineering (IJISME) ISSN: 2319-6386, Volume-1 Issue-8, July 2013 Shubha Majumder, Brain Tumor Extraction from MRI images using Prominent Image Segmentation Methods, Spring 2018 Jardim, S.; António, J; Mora, C. Graphical Image Region Extraction with K-Means Clustering and Watershed. J. Imaging 2022, 8,163. https://doi.org/10.3390/jimaging8060163. Murali Mohan Babu, 2Dr. M.V. Subramanyam, 3Dr. M.N. Giri Prasad. PCA based image denoising. Signal & Image Processing: An International Journal (SIPIJ) Vol.3, No.2, April 2012. http://DOI:10.5121/sipij.2012.3218. National Research Nuclear University MEPhI, Kashirskoye sh. 31, 115409 Moscow, Russia; J. Imaging 2018, 4, 123; doi:10.3390/jimaging4100123 mdpi.com/journal/jimaging. Safonov, I.V.; Mavrin, G.N.; Kryzhanovsky, K.A. Segmentation of Convex Cells with Partially Undefined Edges. Pattern Recognit. Image Anal. 2008, 18, 112–117. Chang, X. Li, “Adaptive Image Region Growing”, IEEE Trans. On Image Processing, Vol. 3, No. 6, 1994. Wu, Y.; Li, Q. The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient. Sensors 2022, 22, 8202. https://doi.org/10.3390/s22218202. . Khan, Z.; Yang, J.; Zheng, Y. Efficient clustering approach for adaptive unsupervised colour image segmentation. Sensors 2019, 13, 1763–1772. [ https:// doi/10.1049/iet-ipr.2018.5976 ]. Kurmi, Y.; Chaurasia, V. Multifeature-based medical image segmentation. Sensors 2018, 12, 1491–1498. [ http://doi.org/10.1049/iet-ipr.2017.1020 ]. . Ahmad EL ALLAOUI1 and M’barek NASRI11 - LABO MATSI, ESTO, B.P 473, University Mohammed I OUJDA, MOROCCO. The International Journal of Multimedia & Its Applications (IJMA) Vol.4, No.3, June 2012. DOI :10.5121/ijma.2012.4301. . Anuj Kumar, Dr. Umesh Chandra, Comparative Analysis of Image Segmentation using Edge-Region Based Technique and Watershed, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) Volume VII, Issue V, May 2018. Amandeep Kaur, Aayushi, Image Segmentation using Watershed Transform, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-4, Issue-1, March 2014. Farheen K. Siddiqui, An Efficient Image Segmentation Approach through Enhanced Watershed Algorithm, Computer Engineering and Intelligent Systems iiste.org ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online), Vol.4, No.6, 2013. Vincent and P. Soille. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Patt. Anal. Mach. Intell. 13(6):583-598, 1991. . Michael Greenacre, Patrick J. F. Groenen, Trevor Hastie, Alfonso Iodice d’Enza, Angelos Markos, and Elena Tuzhilina, Principal Component Analysis, Nature Reviews Methods Primers, January 2023. Ayad Al-Rumaithi (2023). Mode Decomposition using Principal Component Analysis (https://www.mathworks.com/matlabcentral/fileexchange/71878-mode-decomposition-using-principal-component-analysis), MATLAB Central File Exchange. Retrieved June 26, 2023. Murali Mohan Babu, 2Dr. M.V. Subramanyam, 3Dr. M.N. Giri Prasad, PCA based image denoising, Signal & Image Processing: An International Journal (SIPIJ) Vol.3, No.2, April 2012, DOI : 10.5121/sipij.2012.3218. Lei Zhang , Weisheng Dong , David Zhang , Guangming Shi, “Two-stage image denoising by principal component analysis with local pixel grouping” Elsevier-Pattern Recognition,vol-43, 2010, 1531-15
Statistics Article View: 1,057 PDF Download: 636