The problem of image segmentation and de-noising methods and various approaches to its solution

  • Authors

    • Ghazi H.Shakah Ajloun National University
    https://doi.org/10.14419/ijet.v7i4.28039

    Received date: February 27, 2019

    Accepted date: March 10, 2019

    Published date: March 22, 2019

  • Image Segmentation, Image De-Noising, Medical Images, Assessment Methods, Structural Similarity, Non-Linear Method.

  • Abstract

    Image segmentation and de-noising are required to be used in digital image processing according to the recent since researches in this field, At present image de-noising and segmentation take part in real-world applications such as medical fields, computer vision, computer graphic, satellite, magnetic resonance imaging, computed tomography, single photon emission and computed tomography etc. These two methods are used for different images but mainly focus on medical images. In this paper provides an overview of the main classes of methods for segmentation of images, analysis of the effectiveness of their application and development prospects for the implementation of methods adaptive segmentation for the conditions of significant variations in the parameters of images. After that we present the comparison between segmentation techniques based on some specific parameters and find out suitable one.

  • References

    1. M. Rehman, M. Iqbal, M. Sharif, "Content based image retrieval: survey," World Applied Sciences Journal, vol. 19, (2012) 404-412.
    2. M.Khan.A Survey: Image Segmentation Techniques, International Journal of Future Computer and Communication, Vol. 3, No.2, (2014) 89-93. https://doi.org/10.7763/IJFCC.2014.V3.274.
    3. W. Kang, Q.Yang, and R. P. Liang, "The comparative research on image segmentation algorithms," in Proc. First International Work-shop on Education Technology and Computer Science, ETCS'09. ( 2009) 703-707.
    4. B. Baral, S. Gonnade, T. Verma, Image Segmentation and Various Segmentation Techniques – A Review,International Journal of Soft Computing and Engineering (IJSCE), Vol.4, Iss.1, (2014)
    5. N. Bahadure, A. Ray, H. Thethi, Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM, International Journal of Biomedical Imag-ing Volume (2017) 1-12
    6. J. Liu, M. Li, J.Wang, F.Wu, T. Liu, and Y. Pan, “A survey of MRI based brain tumor segmentation methods,” Tsinghua Science and Technology, vol. 19, no. 6, (2014). 578–595, https://doi.org/10.1109/TST.2014.6961028.
    7. A.Sadri,etc,Impluse Noise Cancellatlation of Medical Image Using Wavelet Networks and Median Filter.Journal of Medical Sig-nals,(2012) 25-57.
    8. P.Ndajah,Hisakazu Kikuchi, Masahiro Yukawa, Hidenori Watanabe, Shogo Muramatsu, An Investigation on The Quality of Denoised Images, International Journal Of Circuits, Systems And Signal Pro-cessing,vol.5, pp.423-434.2011.
    9. N. Prabha, S.Saritha, A comprehensive review: Segmentation of MRI images—brain tumor, International Journal of Imaging Systems and Technology,vol.26, (2016) 295-304. https://doi.org/10.1002/ima.22201.
    10. V.Kollu and et al, Fusion of MRI and CT images using guided im-age filter and image statistics, International Journal of Imaging Sys-tems and Technology, (2017) 227-237.
    11. Avcibas I., Sankur B., Sayood K. Statistical evaluating of image quality measures // Journal of Electronic Imaging. –. – Vol.11, № 2 April (2002) 206-223.
    12. A.Sharma, R.Sharma, Quality Assesment Of Gray And Color Imag-es Through Image Fusion Technique, International Journal of Elec-trical & Electronics Engineering, vol.1, 2014.
    13. W .Wilder, Subjective Relevant Error Criteria for Pictorial Data Pro-cessing // Purdue University, School of Electrical Engineering, Re-port TR-EE December (1973) 34-72.
    14. D.J. Withey and Z. Koles. Medical Image Segmentation: Methods and Software proceedings of NFSI & ICFBI, IEEE, Hangzhou, Chi-na, (2007) 140-143.
    15. A. Kokoshkin, V. Korotkov, Comparison Of Objective Methods For Quality Assessment Of Digital Images, Journal of Radio Electronics, jre.cplire.ru/mac/jun15/15/text.html, 2015.
    16. Z.Wang and A.Bovik. Mean Squared Error: Love it ot Leave it? IEEE Signal Processing Magazine, January 2009.
    17. Real Image De-noising,https://competitions.codalab.org/competitions/21258(2019).
    18. N. Sharma and M .Lalit. Aggarwal.Automated medical image seg-mentation techniques, Journal of Medical Physics, (2010) 3-14. https://doi.org/10.4103/0971-6203.58777.
    19. L. Kaur, S. Gupta, R.hauhan, “Image denoising using wavelet thresholding”, Indian Conference on computer Vision, Graphics and Image Processing, Ahmedabad, Dec. 2002.
    20. G. Shakah,et al. Classification of big data: Machine learning prob-lems and challenges in network intrusion prediction, International Journal of Engineering & Technology,pp. 3865-38697. (4) .2018.
    21. A. Yu. Zrazhevsky, S. Titov, “Improving the quality of radio imag-es”. “Nonlinear World”, No. 9, , pp. 582-590. 2010.
    22. S. Anisha,,Comparison of various filters for noise removal in MRI brain image,International Conference on Futuristic Trends in Compu-ting and Communication, (2015) 68-73.
    23. V. Dey, Y. Zhang and M. Zhong, “a review on image segmentation techniques with Remote sensing perspective”, ISPRS, Vienna, Aus-tria, Vol. XXXVIII, July 2010.
    24. G. Shakah, A New Method for Solving Hard Diagnosis Problems, Computer Engineering and Intelligent Systems, Vol 10. (2019) 13-20.
    25. R .Krishna,Integration of modified Particle Swarm Optimization with fuzzy entropy based segmentation provides the maximum entropy while segmenting tumors in brain with less computation time Interna-tional Journal of Imaging Systems and Technology, (2013) 281-288.
    26. X, Liang, and et al.Transition region algorithm based on weighted gradient operator. Image Recognition and Automatization (1), 2001.
    27. Yu.Jin.Zhang. A survey on evaluation methods for image segmenta-tion. Pattern Recognition, 29(8): (1996) 1335–1346. https://doi.org/10.1016/0031-3203(95)00169-7.
    28. A. M. Khan, Ravi. S,Image Segmentation Methods: A Comparative Study , International Journal of Soft Computing and Engineering, Vol.3, Issue-4,( 2013) pp.84-92.
    29. Z. Wang, A. Bovik, A universal image quality index, IEEE Signal Processing Letters , Volume: 9, Issue: 3, 2002, pp-81-84.
    30. S. Neeraj, Automated medical image segmentation techniques, Jour-nal of medical physics.Vol. 35, (2010) 3-14. https://doi.org/10.4103/0971-6203.58777.
    31. Z. KH1, W.SK, et. Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index, The National Center for Biotechnology Information advances, 2004, 12-22.
    32. A.P. Zijdenbos, B.M. Dawant ,etc. Morphometric analysis of white matter lesions in MR images: method and validation, IEEE Transac-tions on Medical Imaging; (1994) 716-24. https://doi.org/10.1109/42.363096.
    33. M .Pavani, an approach for segmentation of medical images using pillar K-means algorithm/ M. Pavani, S. Balaji //International Journal of Computer Trends and Technology (IJCTT). Vol. 4. Issue 4, 2013. PP. 636-641.
    34. J .Mikulka, Comparison of Segmentation Methods in MR Image Processing, Progress In Electromajgnetics Research Symposium Pro-ceedings, 2012, PP. 429-432.
    35. Y..Jin. Zhang. Image segmentation evaluation in this century. Ency-clopedia of Information Science and Technology. Beijing, Tsinghua University, China, (2009) 1812–1817.
    36. D.Balaji and S. Terry , US National library of Medicine National In-stitutes of Heal,thscience and health by providing access to biomedi-cal and genomic information.(2004) 178–189.
    37. Y. Ismail..Maolood, Abdulridha Al-Salhi, Songfeng Lu. Threshold-ing for medical image segmentation for cancer using fuzzy entropy with level set algorithm. Open Med. (2018) 374-383.
    38. R. Muthukrishnan, M.Radha. Edge Detection Techniques For Image Segmentation. (2011) 259.267.
    39. S.Bhowmik, V. Datta, A Survey on Clustering Based Image Seg-mentation, International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, (2012).
    40. S. Haykin, Neural Networks: A comprehensive foundation, Macmil-lan College Publishing Company, Inc., USA, 1994.
    41. Ghazi.H.Shakah,etc. Performance Evalution of Edge Detection Us-ing Sobel,Homogeneity and Prewitt Algorithms. Journal of Software Engineering and Application. (2018) 537- 551.
    42. A. Farag, and T. Moriarty, A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE trans med img, (2002) 193-199.

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    H.Shakah, G. (2019). The problem of image segmentation and de-noising methods and various approaches to its solution. International Journal of Engineering and Technology, 7(4), 5297-5301. https://doi.org/10.14419/ijet.v7i4.28039