Problems in Digital Image Processing: A Survey

AUTHORS

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ABSTRACT

This paper depicts the fundamental mechanical parts of Digital Image Processing with different reference to satellite picture preparing. Fundamentally, all satellite picture handling. The previous manages beginning preparing crude picture information to remedy for geometric mutilation, to align the information radiometrically and to take out clamour show in the information. The improvement methods are connected to picture information keeping in mind the end goal to adequately show the information for resulting visual understanding. It includes methods for expanding the visual qualification between highlights in a scene. The goal of the data extraction activities is to supplant visual investigation of the picture information with quantitative strategies for mechanising the recognisable proof of highlights in a scene. This includes the examination of multispectral picture information and the use of measurable based choice guidelines for deciding the land cover character of every pixel in a picture. In this survey paper, an examination of their issues and also their calculation will be exhibited.

 

KEYWORDS

multilayer perceptron (MLP), decision feedback adaptive equaliser (DFE), signal to error ratio (SER), phase lock loops (PLL), Resonance Imaging (MRI), Digital image processing (DIP)

REFERENCES

[1]     Sukhada Aloni, “Content-Based Image Retrieval in Biomedical Images Using SVM Classification with Relevance Feedback”. International Journal of Scientific and Research Publications, Vol.3, No.11, November (2013).
[2]     Megha Soni, Anand Khare et al., “A survey of digital image processing techniques and its problems” International Journal of Scientific and Research Publications, Vol.4, No.2, pp.1-6. (2014)
[3]     Sankar, D., Thomas, “Fractal Modeling of Mammograms based on Mean and Variance for the Detection of Microcalcifications”. In: Proceedings of the 2007 International Conference on Computational Intelligence and Multimedia Applications, Sivakasi, India, December 2007, pp. 334-338 (2007). [DOI: 10.1109/ICCIMA.2007.66](CrossRef)(Google Scholar)
[4]     Elter, M., Held, “Semiautomatic segmentation for the computer-aided diagnosis of clustered microcalcifications”. In: Proc. SPIE, San Diego, CA, USA, February 2008, vol. 6915, 691524-691524-8 (2008). [DOI: 10.1117/12.770146](CrossRef)(Google Scholar)
[5]     Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer Jr., “The Digital Database for Screening Mammography”. In: Proceedings of the 5th International Workshop on Digital Mammography, Canada, June 11-14, pp. 212-218. Medical Physics Publishing (2001).
[6]     Lopez-Aligue, F.J., Poveda-Pierola, A., Acevedo-Sotoca, I., Garcia-Urra, “Detection of Microcalcifications in Digital Mammograms”. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, Lyon, France, August 22-26, pp. 3906–3909 (2007). [DOI: 10.1109/IEMBS.2007.4353187](CrossRef)(Google Scholar)
[7]     Thangavel, K., Karnan, “Computer Aided Diagnosis in Digital Mammograms: Detection of Microcalcifications by Meta-Heuristic Algorithms”. GVIP Journal Vol.5, No.7, 41-55 (2005). [DOI: 10.1142/9781860948534_0017](CrossRef)(Google Scholar)
[8]     Mustra, M., Grgic, M., Delac, “Efficient Presentation of DICOM Mammography Images using Matlab”. In: Proceedings of the 15th International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia, June 25-28, pp. 13-16 (2008).
[9]     Nishikawa, R.M., Giger, K.L., Doi, K., Vyborny, C.J., Schmidt, R.A.“Computer-aided detection of clustered microcalcifications on digital mammograms”. Medical & Biological Engineering & Computing, Vol.33, No.2, 174-178 (1995). [DOI: 10.1007/bf02523037](CrossRef)(Google Scholar)
[10]  Neiber, H., Müller, T., Stotzka, “Local Contrast Enhancement for the Detection of Microcalcifications”. In: IWDM 2000, Canada, pp. 598-604 (2000).
[11]  Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, “Computer-aided detection and classification of microcalcifications in mammograms: a survey”. Pattern Recognition, Vol.36, No.12, pp. 2967-2991 (2003). [DOI: 10.1016/s0031-3203(03)00192-4](CrossRef)(Google Scholar)
[12]  Schie, G., Karssemeijer, “Detection of Microcalcifications Using a Nonuniform Noise Model”. Krupinski, E.A. (ed.) IWDM 2008. LNCS, Vol. 5116, pp. 378-384. Springer, Heidelberg (2008). [DOI: 10.1007/978-3-540-70538-3_53](CrossRef)(Google Scholar)
[13]  De Santo, M., Molinari, M., Tortorella, F., Vento, “Automatic classification of clustered microcalcifications by multiple expert systems”. Pattern Recognition, Vol.36, No.7, pp.1467-1477 (2003) [DOI: 10.1016/s0031-3203(03)00004-9](CrossRef)(Google Scholar)
[14]  Vapnik VN, “Statistical Learning Theory”. Wiley, New York (1998). [DOI: 10.1007/978-1-4419-1428-6_5864](CrossRef)(Google Scholar)
[15]  Wei, L., Yang, Y., Nishikawa, R.M., Jiang, “A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications”. IEEE Transactions on Medical Imaging, Vol.24, No.3, pp.371-380 (2005). [DOI: 10.1109/TMI.2004.842457](CrossRef)(Google Scholar)
[16]  Yang, Y., Wei, L., Nishikawa, R.M. “Microcalcification Classification Assisted by Content-Based Image Retrieval for Breast Cancer Diagnosis”. In: IEEE International Conference on Image Processing 2007, ICIP 2007, September 16-19, Vol. 5, pp. 1-4 (2007). [DOI: 10.1109/ICIP.2007.4379750](CrossRef)(Google Scholar)
[17]  Dr.D.Selvathi, 2J.Dharani 978-1-4673-4862-1/13/$31.00 ©2013 IEEE.
[18]  P. Mangaiyarkarasi S. Arulselvi 978-1-4244-7926-9/11/$26.00 ©2011 IEEE.
[19]  A. Soma Sekhar, Dr.M.N.Giri Prasad 978-1-4244-8679-3/11/$26.00 ©2011 IEEE.
[20]  Zhu Weiqing, Zhu Min, Wang Junwei, Huang Haiyun, Yang Bo, Xu Lijun, Zhao Liang 1-4244-0635-8/07/$20.00 ©2007 IEEE.
[21]  Juarez, L.C., Ponomaryov, V., Sanchez, R.J.L. “Detection of Microcalcifications in Digital Mammograms Images Using Wavelet Transform”. In: Electronics, Robotics and Automotive Mechanics Conference, September 2006, Vol. 2, pp. 58-61 (2006). [DOI: 10.1109/CERMA.2006.36](CrossRef)(Google Scholar)
[22]  Song, L., Wang, Q., Gao, “Microcalcification detection using a combination of wavelet transform and morphology”. In: Proceedings of the 8th International Conference on Signal Processing, ICSP 2006, Vol. 4, pp. 16-20 (2006).
[23]  Heinlein, P., Drexel, J., Schneider, “Integrated Wavelets for Enhancement of Microcalcifications in Digital Mammo graph”. IEEE Transactions on Medical Imaging, Vol.22, No.3, pp.402-413 (2003).
[24]  Mencattini, A., Salmeri, M., Lojacono, R., Frigerio, M., Caselli, “Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing”. IEEE Transactions on Instrumentation and Measurement, Vol.57, No.7, pp.1422-1430 (2008). [DOI: 10.1109/tim.2007.915470](CrossRef)(Google Scholar)

CITATION

  • APA:
    ,(2019). Problems in Digital Image Processing: A Survey . International Journal of Image and Signal Systems Engineering, 3(1), 7-14. http://dx.doi.org/10.21742/IJISSE.2019.3.1.02
  • Harvard:
    ,(2019). "Problems in Digital Image Processing: A Survey ". International Journal of Image and Signal Systems Engineering, 3(1), pp.7-14. doi:http://dx.doi.org/10.21742/IJISSE.2019.3.1.02
  • IEEE:
    [1], "Problems in Digital Image Processing: A Survey ". International Journal of Image and Signal Systems Engineering, vol.3, no.1, pp.7-14, Oct. 2019
  • MLA:
    . "Problems in Digital Image Processing: A Survey ". International Journal of Image and Signal Systems Engineering, vol.3, no.1, Oct. 2019, pp.7-14, doi:http://dx.doi.org/10.21742/IJISSE.2019.3.1.02

ISSUE INFO

  • Volume 3, No. 1, 2019
  • ISSN(p):2207-4600
  • ISSN(o):2207-4619
  • Published:Oct. 2019

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