• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 17, Pages: 778-789

Original Article

A Novel Image Denoising and Segmentation Using Machine Learning with SRM Strategy

Received Date:26 November 2021, Accepted Date:24 March 2022, Published Date:10 May 2022


Objectives: In the last decade, artificial intelligence (AI) and machine learning (ML) have a significant impact on image analysis and segmentation. The most demanding aspect in digital image processing is efficient segmentation to extract the desired features or recognize the hidden patterns of the digital images, which are one of the major challenges. Even in existing segmentation techniques, due to under segmentation issues, particularly in remote or arial image analysis, necessary object features and residual details are not properly segmented. Therefore, the study presents a new method for image segmentation using machine learning, which addresses these limitations. Methods: This article presents a novel approach for arial image denoising and segmentation using unsupervised learning. The Discrete Wavelet Transform (DWT) is primarily used as a pre-processing tool, while edge details of the decomposed image are automatically preserved with the help of machine understanding. The use of a fast Non-Local (NL)-means filter provides a better visual effect for low-frequency image features, and the filtered image is partitioned into the number of clusters by Minimum- Spanning Tree (MST) clustering algorithm. Further, Statistical Region Merging (SRM) is used to eliminate the unwanted regions of the clustered image to give meaningful image details. Finally, the segmented image is projected at the wavelet restoration level. Here, the standard images are extracted from the SIPI database, and the system does not require training prototypes but works in an ”unsupervised” way. Findings: The proposed system segment 1014 regions in the first phase and the segmented image features efficiently recognize the hidden patterns of the arial image (7.1.06). However, to track the required object features, statistical Region Merging (SRM) is utilized, a drastic variation in the merging process eliminates the annoying details and brings effective outcomes, which involves 29 essential features with retained residual details at level-2 decomposition and is compared with leading segmentation techniques. The denoising performance of the suggested study provides a 21.73 Peak Signal to Noise Ratio (PSNR) value in 17.96 elapsed time. simulation results have proven to produce better segmentation and denoising with less iteration time. A qualitative evaluation through visual examination justifies the proposed study and is superior to some of the popular methods. Novelty/Applications: The utilization of wavelet transform to preserve edge features during segmentation is one of the key features of this method, and it significantly overcomes the under segmentation patterns. The combination of minimum-spanning tree clustering (MST) and statistical region merging (SRM) is the major strategic step in segmenting and detecting the essential object features of arial images.

Keywords: DWT; Fast NLmeans filter; Wavelet denoising; MST; SRM


  1. Lv X, Ma Y, He X, Huang H, Yang J. CciMST: A Clustering Algorithm Based on Minimum Spanning Tree and Cluster Centers. Mathematical Problems in Engineering. 2018;2018:1–14. Available from: https://dx.doi.org/10.1155/2018/8451796
  2. Wang M, Dong Z, Cheng Y, Li D. Optimal Segmentation of High-Resolution Remote Sensing Image by Combining Superpixels With the Minimum Spanning Tree. IEEE Transactions on Geoscience and Remote Sensing. 2018;56(1):228–238. Available from: https://dx.doi.org/10.1109/tgrs.2017.2745507
  3. Wang Z. Unsupervised Wavelet-Feature Markov Clustering Algorithm for Remotely Sensed Images. 2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). 2021. doi: 10.1109/ISSPIT51521.2020.9408754
  4. Verma AK, Vamsi I, Saurabh P, Sudha R, Sabareesh GR, Rajkumar S. Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing. Expert Systems with Applications. 2021;185:115650. Available from: https://dx.doi.org/10.1016/j.eswa.2021.115650
  5. Tania M, Afroze D, Akhter J, Sayed MA, Rahaman M, Islam MI. Image recognition using machine learning with the aid of MLR. Image Graphics and Signal Processing. 2021;6:12–22. doi: 10.5815/ijigsp.2021.06.02
  6. Arumugadevi S, Seenivasagam V. Comparison of Clustering Methods for Segmenting Color Images. Indian Journal of Science and Technology. 2015;8(7):670. Available from: https://dx.doi.org/10.17485/ijst/2015/v8i7/62862
  7. Jena S, Mohanty MD, Mohanty MN. Biomedical Image Segmentation using Optimized Fuzzy C-mean Algorithm. Indian Journal of Science and Technology. 2017;10(35):1–6.
  8. Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & Intelligent Systems. 2021;p. 1–23. Available from: https://dx.doi.org/10.1007/s40747-021-00563-y
  9. jaina L, Singh BP. A novel wavelet thresholding rule for speckle reduction from ultrasound images . Journal of King Saud University - Computer and Information Sciences. 2020. Available from: https://doi.org/10.1016/j.jksuci.2020.10.009
  10. Felzenszwalb PF, Huttenlocher DP. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision. 2004;59(2):167–181. Available from: https://dx.doi.org/10.1023/b:visi.0000022288.19776.77
  11. Huchan JJ, Jiang BO. Wavelet transform and morphology image segmentation algorism for blood cell. Industrial Electronics and Applications (ICIEA). 2009;978(1). doi: 10.1109/ICIEA.2009.5138265
  12. Raj A, Radhakrishnan AK, B. A comparative study on target detection in military field using various digital image processing techniques. International Journal of Computer Science and Network (IJCSN). 2016;5(1):2277–5420.
  13. Dhanachandra N, Chanu YJ. A new image segmentation method using clustering and region merging techniques. Advances in Intelligent Systems and Computing, Applications of Artificial Intelligence Techniques in Engineering. 2019;p. 603–614. doi: 10.1007/978-981-13-1819-1_57
  14. Nija KS, Anupama CP, Gopi VP, Anitha VS. Automated segmentation of optic disc using statistical region merging and morphological operations. Physical and Engineering Sciences in Medicine. 2020;43(3):857–869. Available from: https://dx.doi.org/10.1007/s13246-020-00883-2
  15. Li Z, Zhang W, Yang H. Color Image Segmentation Based on Wavelet Transform and Fuzzy Kernel Clustering. International Conference on Virtual Reality and Intelligent Systems (ICVRIS). 2020;2020:411–414. doi: 10.1109/ICVRIS51417.2020.00103
  16. Ijitona TB, Ren J, Hwang PB. SAR sea ice image segmentation using watershed with intensity-based region merging. Computer and Information Technology IEEE. 2014;6(14):978–979. doi: 10.1109/CIT.2014.19
  17. Mukhopadhyay J, Choudhuri S, Sengupta S. ANFIS based speed and current control with torque ripple minimization using hybrid SSD-SFO for switched reluctance motor. Sustainable Energy Technologies and Assessments. 2022;49:101712. Available from: https://dx.doi.org/10.1016/j.seta.2021.101712
  18. Pun CM, An NY, Chen CLP. Region-based Image Segmentation by Watershed Partition and DCT Energy Compaction. International Journal of Computational Intelligence Systems. 2012;5(1):53. Available from: https://dx.doi.org/10.1080/18756891.2012.670521
  19. Angelina S, Suresh LP, Veni SHK. Image segmentation based on genetic algorithm for region growth and region merging. International Conference on Computing, Electronics and Electrical Technologies (ICCEET). 2012. doi: 10.1109/ICCEET.2012.6203833
  20. Peng B, Zhang L, Zhang D. Automatic Image Segmentation by Dynamic Region Merging. IEEE Transactions on Image Processing. 2011;20(12):3592–3605. Available from: https://dx.doi.org/10.1109/tip.2011.2157512
  21. Pun CM, An NY. Image segmentation using effective region merging strategy. International Journal of Digital Technology and its Application. 2011;5(8):59–69. doi: 10.4156/jdcta.vol5.issue8.8
  22. Basavaprasad B, Hegadi RS. Automatic multi stage image segmentation using normalized cut in gradient image. Advances in Computational Sciences and Technology. 2017;10(1):37–51.
  23. Pitchai R, Supraja P, Victoria AH, Madhavi M. Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images. Neural Processing Letters. 2021;53(4):2519–2532. Available from: https://dx.doi.org/10.1007/s11063-020-10326-4


© 2022 Narayan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)


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