![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
A Review Paper on Image Segmentation, Enhancement, and Classification
Image processing is a relatively recent field in which much research has been conducted. Image processing is a signal processing technique that receives an image as input and produces an image or a group of characteristics or qualities connected to images. Image processing aims to improve image quality by removing undesirable features. There are approaches for image segmentation, enhancement, classification, restoration, pattern recognition, extraction, and more. We studied some image processing approaches in depth in this paper, including image segmentation, image enhancement, and image classification, as well as numerous research works on image processing techniques employing various deep learning algorithms.
Keywords
No Keywords.
User
Font Size
Information
- N. M. Zaitoun and M. J. Aqel, “Survey on Image Segmentation Techniques,” Procedia Comput. Sci., vol. 65, no. Iccmit, pp. 797–806, 2015, doi: 10.1016/j.procs.2015.09.027.
- O. Lézoray, C. Charrier, H. Cardot, and S. Lefévre, “Machine learning in image processing,” EURASIP J. Adv. Signal Process., vol. 2008, 2008, doi: 10.1155/2008/927950.
- “Introduction to What Is Analog Design?,” Analog Circuit Des., p. 21, 1991, doi: 10.1016/b978-0-08-049907-9.50011-8.
- Maria Riasat, “Research on various image processing techniques,” Open Access Res. J. Chem. Pharm., vol. 1, no. 1, pp. 005–012, 2021, doi: 10.53022/oarjcp.2021.1.1.0029.
- H. Manickam, “Fruit Classification using image processing techniques International Journal of Computer Sciences and Engineering Open Access,” no. March, 2021.
- A. Kumar Pathak and M. . Parsai, “A Study of Various Image Fusion Techniques,” Int. J. Eng. Trends Technol., vol. 15, no. 2, pp. 59–62, 2014, doi: 10.14445/22315381/ijett-v15p213.
- L. Jiao and J. Zhao, “A Survey on the New Generation of Deep Learning in Image Processing,” IEEE Access, vol. 7, pp. 172231–172263, 2019, doi: 10.1109/ACCESS.2019.2956508.
- H. K. Ranota and P. Kaur, “Review and Analysis of Image Enhancement Techniques,” Int. J. Inf. Comput. Technol., vol. 4, no. 6, pp. 583–590, 2014, [Online]. Available: http://www.irphouse.com
- “Image Segmentation: Part 1. Mathematical and practical… | by Mrinal Tyagi | Towards Data Science.” https://towardsdatascience.com/image-segmentation-part-1-9f3db1ac1c50 (accessed Aug. 15, 2022).
- R. Sarma and Y. K. Gupta, “A comparative study of new and existing segmentation techniques,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1022, no. 1, 2021, doi: 10.1088/1757-899X/1022/1/012027.
- “Image Segmentation By Clustering - GeeksforGeeks.” https://www.geeksforgeeks.org/image-segmentation-by-clustering/ (accessed Aug. 15, 2022).
- Q. Yang, H. Zhang, J. Xia, and X. Zhang, “Evaluation of magnetic resonance image segmentation in brain low-grade gliomas using support vector machine and convolutional neural network,” Quant. Imaging Med. Surg., vol. 11, no. 1, pp. 300–316, 2021, doi: 10.21037/QIMS-20-783.
- X. Ma et al., “Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields,” PLoS One, vol. 14, no. 4, pp. 1–13, 2019, doi: 10.1371/journal.pone.0215676.
- A. Pal, U. Garain, A. Chandra, R. Chatterjee, and S. Senapati, “Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network,” Comput. Methods Programs Biomed., vol. 159, pp. 59–69, 2018, doi: 10.1016/j.cmpb.2018.01.027.
- M. U. Rehman, S. Cho, J. Kim, and K. T. Chong, “Brainseg-net: Brain tumor mr image segmentation via enhanced encoder–decoder network,” Diagnostics, vol. 11, no. 2, pp. 1–13, 2021, doi: 10.3390/diagnostics11020169.
- M. Dash, N. D. Londhe, S. Ghosh, A. Semwal, and R. S. Sonawane, “PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network,” Biomed. Signal Process. Control, vol. 52, pp. 226–237, 2019, doi: 10.1016/j.bspc.2019.04.002.
- X. Li, H. Chen, X. Qi, Q. Dou, C. W. Fu, and P. A. Heng, “H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes,” IEEE Trans. Med. Imaging, vol. 37, no. 12, pp. 2663–2674, 2018, doi: 10.1109/TMI.2018.2845918.
- K. D. Q. G. Kdu et al., “0Hodqrpd ’ Ldjqrvlv Iurp ’ Huprvfrs , Pdjhv,” pp. 3–7.
- P. Sharma, Y. P. S. Berwal, and W. Ghai, “Performance analysis of deep learning CNN models for disease detection in plants using image segmentation,” Inf. Process. Agric., vol. 7, no. 4, pp. 566–574, 2020, doi: 10.1016/j.inpa.2019.11.001.
- S. Mukhopadhyay, M. Paul, R. Pal, and D. De, “Tea leaf disease detection using multi-objective image segmentation,” Multimed. Tools Appl., vol. 80, no. 1, pp. 753–771, 2021, doi: 10.1007/s11042-020-09567-1.
- B. Shareef, M. Xian, and A. Vakanski, “Stan: Small Tumor-Aware Network for Breast Ultrasound Image Segmentation,” Proc. - Int. Symp. Biomed. Imaging, vol. 2020-April, pp. 1469–1473, 2020, doi: 10.1109/ISBI45749.2020.9098691.
- P. Manisha, R. Jayadevan, and V. S. Sheeba, “Content-based image retrieval through semantic image segmentation,” AIP Conf. Proc., vol. 2222, no. April, 2020, doi: 10.1063/5.0004087.
- F. Sultana, A. Sufian, and P. Dutta, “Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey,” Knowledge-Based Syst., vol. 201–202, p. 106062, 2020, doi: 10.1016/j.knosys.2020.106062.
- “Histogram Equalization | by Shreenidhi Sudhakar | Towards Data Science.” https://towardsdatascience.com/histogram-equalization-5d1013626e64 (accessed Aug. 15, 2022).
- T. Rahman et al., “Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images,” Comput. Biol. Med., vol. 132, no. March, p. 104319, 2021, doi: 10.1016/j.compbiomed.2021.104319.
- R. A. Pramunendar, S. Wibirama, P. I. Santosa, P. N. Andono, and M. A. Soeleman, “A robust image enhancement techniques for underwater fish classification in marine environment,” Int. J. Intell. Eng. Syst., vol. 12, no. 5, pp. 116–129, 2019, doi: 10.22266/ijies2019.1031.12.
- M. Harichandana, V. Sowmya, V. V. Sajithvariyar, and R. Sivanpillai, “Comparison of Image Enhancement Techniques for Rapid Processing of Post Flood Images,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLIV-M-2–2, no. June, pp. 45–50, 2020, doi: 10.5194/isprs-archives-xliv-m-2-2020-45-2020.
- J. Dabass, S. Arora, R. Vig, and M. Hanmandlu, “Mammogram Image Enhancement Using Entropy and CLAHE Based Intuitionistic Fuzzy Method,” 2019 6th Int. Conf. Signal Process. Integr. Networks, SPIN 2019, pp. 24–29, 2019, doi: 10.1109/SPIN.2019.8711696.
- R. A. Pramunendar, S. Wibirama, and P. I. Santosa, “A novel approach for underwater image enhancement based on improved dark channel prior with colour correction,” J. Eng. Sci. Technol., vol. 13, no. 10, pp. 3220–3237, 2018.
- R. Goel, “The Implementation of Image Enhancement Techniques Using Matlab,” SSRN Electron. J., 2021, doi: 10.2139/ssrn.3884967.
- J. Cai, S. Gu, and L. Zhang, “Learning a deep single image contrast enhancer from multi-exposure images,” IEEE Trans. Image Process., vol. 27, no. 4, pp. 2049–2062, 2018, doi: 10.1109/TIP.2018.2794218.
- J. Cepeda-Negrete, R. E. Sanchez-Yanez, F. E. Correa-Tome, and R. A. Lizarraga-Morales, “Dark Image Enhancement Using Perceptual Color Transfer,” IEEE Access, vol. 6, no. c, pp. 14935–14945, 2017, doi: 10.1109/ACCESS.2017.2763898.
- R. Sivakumar and E. Mohan, “High Resolution Satellite Image Enhancement Using Discrete Wavelet Transform,” Int. J. Appl. Eng. Res., vol. 13, no. 11, pp. 9811–9815, 2018.
- L. Yongxue, Z. Min, and S. Dihua, “Fast enhancement algorithm of highway tunnel image based on constraint of imaging model,” IET Image Process., vol. 12, no. 10, pp. 1730–1735, 2018, doi: 10.1049/iet-ipr.2017.0902.
- J. Ma, X. Fan, S. X. Yang, X. Zhang, and X. Zhu, “Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement,” Int. J. Pattern Recognit. Artif. Intell., vol. 32, no. 7, pp. 1–26, 2018, doi: 10.1142/S0218001418540186.
- U. A. Nnolim, “An adaptive RGB colour enhancement formulation for logarithmic image processing-based algorithms,” Optik (Stuttg)., vol. 154, pp. 192–215, 2018, doi: 10.1016/j.ijleo.2017.09.102.
- A. M. Patil, M. D. Patil, and G. K. Birajdar, “White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis,” Irbm, vol. 42, no. 5, pp. 378–389, 2021, doi: 10.1016/j.irbm.2020.08.005.
- N. Sharma, V. Jain, and A. Mishra, “An Analysis of Convolutional Neural Networks for Image Classification,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 377–384, 2018, doi: 10.1016/j.procs.2018.05.198.
- H. Wu and Z. Zhou, “Using Convolution Neural Network for Defective Image Classification of Industrial Components,” Mob. Inf. Syst., vol. 2021, 2021, doi: 10.1155/2021/9092589.
- P. Kumar and U. Dugal, “Tensorflow Based Image Classification using Advanced Convolutional Neural Network,” Int. J. Recent Technol. Eng., vol. 8, no. 6, pp. 994–998, 2020, doi: 10.35940/ijrte.f7543.038620.
- X. Li, L. Dong, and M. Li, “Improved Image Classification Algorithm Based on Convolutional Neural Network,” OALib, vol. 08, no. 12, pp. 1–9, 2021, doi: 10.4236/oalib.1108228.
- B. Xu, X. He, and Z. Qu, “Asian Food Image Classification Based on Deep Learning,” J. Comput. Commun., vol. 09, no. 03, pp. 10–28, 2021, doi: 10.4236/jcc.2021.93002.
- M. M. Krishna, M. Neelima, M. Harshali, and M. V. G. Rao, “Image classification using Deep learning,” Int. J. Eng. Technol., vol. 7, no. March, pp. 614–617, 2018, doi: 10.14419/ijet.v7i2.7.10892.
- N. Arunkumar, M. A. Mohammed, S. A. Mostafa, D. A. Ibrahim, J. J. P. C. Rodrigues, and V. H. C. de Albuquerque, “Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks,” Concurr. Comput. Pract. Exp., vol. 32, no. 1, pp. 1–9, 2020, doi: 10.1002/cpe.4962.
- A. Nasirahmadi et al., “Automatic scoring of lateral and sternal lying posture in grouped pigs using image processing and Support Vector Machine,” Comput. Electron. Agric., vol. 156, no. September 2018, pp. 475–481, 2019, doi: 10.1016/j.compag.2018.12.009.
- M. A. Abu, N. H. Indra, A. H. A. Rahman, N. A. Sapiee, and I. Ahmad, “A study on image classification based on deep learning and tensorflow,” Int. J. Eng. Res. Technol., vol. 12, no. 4, pp. 563–569, 2019.
- N. Handa, Y. Kaushik, N. Sharma, M. Dixit, and M. Garg, “Image Classification Using Convolutional Neural Networks,” Commun. Comput. Inf. Sci., vol. 1393, no. January, pp. 510–517, 2021, doi: 10.1007/978-981-16-3660-8_48.
- W. A. Ezat, M. M. Dessouky, and N. A. Ismail, “Multi-class Image Classification Using Deep Learning Algorithm,” J. Phys. Conf. Ser., vol. 1447, no. 1, 2020, doi: 10.1088/1742-6596/1447/1/012021.
- M. Pritt and G. Chern, “Satellite image classification with deep learning,” Proc. - Appl. Imag. Pattern Recognit. Work., vol. 2017-Octob, pp. 1–7, 2018, doi: 10.1109/AIPR.2017.8457969.
- J. Zhang, W. Wang, C. Lu, J. Wang, and A. K. Sangaiah, “Lightweight deep network for traffic sign classification,” Ann. des Telecommun. Telecommun., vol. 75, no. 7–8, pp. 369–379, 2020, doi: 10.1007/s12243-019-00731-9.
- R. Yan et al., “Breast cancer histopathological image classification using a hybrid deep neural network,” Methods, vol. 173, no. 2019, pp. 52–60, 2020, doi: 10.1016/j.ymeth.2019.06.014.
- R. P. Nikhil and K. P. Sankar, “A Review on Image Segmentation Techniques,” Pattern Recognit., vol. 26, no. 9, pp. 1277–1294, 1993.
- M. Egmont-Petersen, D. De Ridder, and H. Handels, “Image processing with neural networks- A review,” Pattern Recognit., vol. 35, no. 10, pp. 2279–2301, 2002, doi: 10.1016/S0031-3203(01)00178-9.
- R. Yadav and M. Pandey, “Image Segmentation Techniques: A Survey,” Lect. Notes Data Eng. Commun. Technol., vol. 90, no. 2, pp. 231–239, 2022, doi: 10.1007/978-981-16-6289-8_20.
- I. Journal, D. D. Patil, S. G. Deore, and S. Bhusawal, “Medical Image Segmentation: A Review International Journal of Computer Science and Mobile Computing Medical Image Segmentation: A Review,” Ijcsmc, vol. 2, no. 1, pp. 22–27, 2013, [Online]. Available: www.ijcsmc.com
- M. Castelluccio, G. Poggi, C. Sansone, and L. Verdoliva, “Land Use Classification in Remote Sensing Images by Convolutional Neural Networks,” pp. 1–11, 2015, [Online]. Available: http://arxiv.org/abs/1508.00092
- K. Dilpreet and K. Yadwinder, “Various Image Segmentation Techniques: A Review,” Int. J. Comput. Sci. Mob. Comput., vol. 3, no. 5, pp. 809–814, 2014.
- Y. Qi et al., “A Comprehensive Overview of Image Enhancement Techniques,” Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 583–607, 2022, doi: 10.1007/s11831-021-09587-6.
- A. Nelikanti, “Image Enhancement Using Image Fusion and Image Processing Techniques,” An Int. J. Adv. Comput. Technol., vol. 3, no. 10, p. 2320, 2014, [Online]. Available: https://ijact.in/index.php/ijact/article/viewFile/191/157
- R. M, “Statistical Restoration Techniques on Melanoma Images,” Helix, vol. 8, no. 6, pp. 4610–4615, 2018, doi: 10.29042/2018-4610-4615.
- S. Joshi, R. Arindom, T. Dikshit, B. Anish, A. G. Deep, and P. Pallav, “Conceptual paper on factors affecting the attitude of senior citizens towards purchase of smartphones,” Indian J. Sci. Technol., vol. 8, no. 12, pp. 83–89, 2015, doi: 10.17485/ijst/2015/v8i.
- A. Haris and A. A. Almisreb, “A Review on Image Enhancement Techniques. Southeast Europe Journal of Soft Computing Available online : http://scjournal.ius.edu.ba,” vol. 8, no. April, 2019.
- U. Sesadri, B. S. Sankar, and C. Nagaraju, “Type2 Fuzzy Soft Computing Technique for Image Enhancement,” IAES Int. J. Artif. Intell., vol. 4, no. 3, p. 97, 2015, doi: 10.11591/ijai.v4.i3.pp97-104.
- G. S. Rajput and Z. Rahman, “Hazard detection on runways using image processing techniques,” Enhanc. Synth. Vis. 2008, vol. 6957, p. 69570D, 2008, doi: 10.1117/12.782569.
- O. D. Rq et al., “5hvhdufk rq ,pdjh &odvvlilfdwlrq 0hwkrg %dvhg rq ’&11,” pp. 5–8.
- E. Pasolli, F. Melgani, D. Tuia, F. Pacifici, and W. J. Emery, “SVM active learning approach for image classification using spatial information,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 4, pp. 2217–2223, 2014, doi: 10.1109/TGRS.2013.2258676.
- M. Korytkowski, L. Rutkowski, and R. Scherer, “Fast image classification by boosting fuzzy classifiers,” Inf. Sci. (Ny)., vol. 327, pp. 175–182, 2016, doi: 10.1016/j.ins.2015.08.030.
- S. Roychowdhury, M. Diligenti, and M. Gori, “Image Classification Using Deep Learning and Prior Knowledge,” Work. Thirty-Second AAAI Conf. Artif. Intell., pp. 336–342, 2018.
- D. R. Panuju, D. J. Paull, and A. L. Griffin, “Change detection techniques based on multispectral images for investigating land cover dynamics,” Remote Sens., vol. 12, no. 11, pp. 1–36, 2020, doi: 10.3390/rs12111781.
- D. Fraix-Burnet, “Grand Challenges in Image Processing,” Front. Astron. Sp. Sci., vol. 7, no. April, pp. 1–6, 2020, doi: 10.3389/frsip.2021.675547.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 163
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0