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Image Processing Applied to Medical Science for the Study of Liver Cancer Using Segmentation in Magnetic Resonance Imaging

Received: 17 July 2019     Accepted: 13 August 2019     Published: 28 May 2020
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Abstract

The objective to develop some algorithms with new techniques of image processing for the automatic segmentation of the liver using magnetic resonance images. The methodology is based in a descriptive description was proposed that allows to combine the information of multiple channels using statistical models that have as a central point the multivariate and multisequence gaussian distribution. In this way, we will approach the spatial distribution having as a central point the intensity values in the different sequences and, therefore, we will be able to capture the variability of the data in each sequence at that moment. The results are based on the segmentation references and the proposed evaluation metrics to be able to validate the development different methods for segmentation were applied as inputs and what was obtained as a result of the segmentation originated a group of images that correspond to each of the cuts that have maximum resolution in the obtained sequences. All the images obtained here including the segmentation referred to, must be binary and their pixels must be marked with 1 (liver) or 0 (without liver). In conclusions the segmentation method that we propose here will consist of an active contour modeling in 2D and 3D and that will be developed in images that will be produced based on a new developed descriptor, having as an important point to minimize the image with an Approximation that is dual to the variational problem which should give us good results in the segmentation process.

Published in International Journal of Information and Communication Sciences (Volume 5, Issue 1)
DOI 10.11648/j.ijics.20200501.12
Page(s) 1-4
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Segmentation, Magnetic Resonance (MRI), Image, Metrics, Processing

References
[1] F. López-Mir, V. Naranjo, J. Angulo, M. Alcañiz, and L. Luna, “Liver segmentation in MRI: A fully automatic method based on stochastic partitions.,” Comput. Methods Programs Biomed., vol. 114, no. 1, pp. 11–28, Apr. 2014.
[2] J. Oh, D. R. Martin, and X. Hu, “Partitioned edge-function-scaled region-based active contour (p-ESRAC): Automated liver segmentation in multiphase contrast-enhanced MRI.,” Med. Phys., vol. 41, no. 4, p. 041914, Apr. 2014.
[3] Antonidoss, K. P., Kaliyamurthie, (2014). Segmentation from Images Using Adaptive Threshold. Middle-East J. Sci. Res. 20, 479–484. doi: 10.5829/idosi.mejsr.2014. 20. 04. 21037.
[4] P. Bao., L. Zhang., (2003). Noise reduction for magnetic resonance images via adaptive multiscale products thresholding. IEEE Trans. Med. Imaging 22, 1089–99. doi: 10.1109/TMI.2003.816958.
[5] O. Basset., Z. Sun., J. L, Mestas. G. Gimenez., (1993). Texture analysis of ultrasonic images of the prostate by means of co-ocurrence matrices. Ultrason. Imaging 15, 218–237.
[6] R. Beichel., S. Mitchell., E. Sorantin., (2001). Shape-and appearance-based segmentation of volumetric medical images. ICIP 2, 589–592.
[7] Jia, X., Bian, Z., He, J., Wang, Y., Huang, J., Zeng, D., Ma, J. (2016). Texture-preserved low-dose CT reconstruction using region recognizable patch-priors from previous normal-dose CT images. 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD). doi: 10.1109/nssmic.2016.8069559.
[8] L. Zhong et al., (2016). Predict CT image from MRI data using KNN-regression with learned local descriptors, Proc. IEEE 13th Int. Symp. Biomed. Imag., pp. 743-746.
[9] Yang, W., Zhong, L., Chen, Y., Lin, L., Lu, Z., Liu, S., Chen, W. (2018). Predicting CT Image from MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors. IEEE Transactions on Medical Imaging, 37 (4), 977–987. doi: 10.1109/tmi.2018.2790962.
[10] Y. Boykov., G. Funka-Lea., (2006). Graph Cuts and Efficient N-D Image Segmentation. Int. J. Comput. Vis. 70, 109–131. doi: 10.1007/s11263-006-7934-5.
[11] A. Foruzan., C. Yen-Wei., (2013). Segmentation of Liver in Low-Contrast Images Using K-Means Clustering and Geodesic Active Contour Algorithms. IEICE Trans. INF SYST. E96-D, 798–807.
[12] O. Gloger., J. Kühn., A. Stanski., H. Völzke., R. Puls., (2010). A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MRI images. Magn. Reson. Imaging 28, 882–897. doi: http://dx.doi.org/10.1016/j.mri.2010.03.010.
[13] O. Gloger., K. Toennies., J. P. Kuehn., (2011). Fully Automatic Liver Volumetry Using 3D Level Set Segmentation for Differentiated Liver Tissue Types in Multiple Contrast MR Datasets, in: Heyden, A., Kahl, F. (Eds.), Image Analysis SE-48, Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 512–523. doi: 10.1007/978-3-642-21227-7_48.
[14] E. Göçeri., M. N Gürcan., O. Dicle., (2014). Fully automated liver segmentation from SPIR image series. Comput. Biol. Med. 53, 265–278. doi: 10.1016/j.compbiomed.2014.08.009.
[15] H. T. Huynh., I. Karademir., A. Oto., K. Suzuki., (2014). Computerized liver volumetry on MRI by using 3D geodesic active contour segmentation. AJR. Am. J. Roentgenol. 202, 152–9. doi: 10.2214/AJR.13.10812.
[16] A. P. James., B. V. Dasarathy., (2014). Medical image fusion: A survey of the state of the art. Inf. Fusion 19, 4–19. doi: 10.1016/j.inffus.2013.12.002.
[17] F. López-Mir., V. Naranjo., J. Angulo., M. Alcañiz., L. Luna., (2014). Liver segmentation in MRI: A fully automatic method based on stochastic partitions. Comput. Methods Programs Biomed. 114, 11–28. doi: 10.1016/j.cmpb.2013.12.022.
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  • APA Style

    Jose Ramon Iglesias Gamarra, Omaira Luz Tapias Diaz. (2020). Image Processing Applied to Medical Science for the Study of Liver Cancer Using Segmentation in Magnetic Resonance Imaging. International Journal of Information and Communication Sciences, 5(1), 1-4. https://doi.org/10.11648/j.ijics.20200501.12

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    ACS Style

    Jose Ramon Iglesias Gamarra; Omaira Luz Tapias Diaz. Image Processing Applied to Medical Science for the Study of Liver Cancer Using Segmentation in Magnetic Resonance Imaging. Int. J. Inf. Commun. Sci. 2020, 5(1), 1-4. doi: 10.11648/j.ijics.20200501.12

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    AMA Style

    Jose Ramon Iglesias Gamarra, Omaira Luz Tapias Diaz. Image Processing Applied to Medical Science for the Study of Liver Cancer Using Segmentation in Magnetic Resonance Imaging. Int J Inf Commun Sci. 2020;5(1):1-4. doi: 10.11648/j.ijics.20200501.12

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  • @article{10.11648/j.ijics.20200501.12,
      author = {Jose Ramon Iglesias Gamarra and Omaira Luz Tapias Diaz},
      title = {Image Processing Applied to Medical Science for the Study of Liver Cancer Using Segmentation in Magnetic Resonance Imaging},
      journal = {International Journal of Information and Communication Sciences},
      volume = {5},
      number = {1},
      pages = {1-4},
      doi = {10.11648/j.ijics.20200501.12},
      url = {https://doi.org/10.11648/j.ijics.20200501.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20200501.12},
      abstract = {The objective to develop some algorithms with new techniques of image processing for the automatic segmentation of the liver using magnetic resonance images. The methodology is based in a descriptive description was proposed that allows to combine the information of multiple channels using statistical models that have as a central point the multivariate and multisequence gaussian distribution. In this way, we will approach the spatial distribution having as a central point the intensity values in the different sequences and, therefore, we will be able to capture the variability of the data in each sequence at that moment. The results are based on the segmentation references and the proposed evaluation metrics to be able to validate the development different methods for segmentation were applied as inputs and what was obtained as a result of the segmentation originated a group of images that correspond to each of the cuts that have maximum resolution in the obtained sequences. All the images obtained here including the segmentation referred to, must be binary and their pixels must be marked with 1 (liver) or 0 (without liver). In conclusions the segmentation method that we propose here will consist of an active contour modeling in 2D and 3D and that will be developed in images that will be produced based on a new developed descriptor, having as an important point to minimize the image with an Approximation that is dual to the variational problem which should give us good results in the segmentation process.},
     year = {2020}
    }
    

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    T1  - Image Processing Applied to Medical Science for the Study of Liver Cancer Using Segmentation in Magnetic Resonance Imaging
    AU  - Jose Ramon Iglesias Gamarra
    AU  - Omaira Luz Tapias Diaz
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    PY  - 2020
    N1  - https://doi.org/10.11648/j.ijics.20200501.12
    DO  - 10.11648/j.ijics.20200501.12
    T2  - International Journal of Information and Communication Sciences
    JF  - International Journal of Information and Communication Sciences
    JO  - International Journal of Information and Communication Sciences
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    PB  - Science Publishing Group
    SN  - 2575-1719
    UR  - https://doi.org/10.11648/j.ijics.20200501.12
    AB  - The objective to develop some algorithms with new techniques of image processing for the automatic segmentation of the liver using magnetic resonance images. The methodology is based in a descriptive description was proposed that allows to combine the information of multiple channels using statistical models that have as a central point the multivariate and multisequence gaussian distribution. In this way, we will approach the spatial distribution having as a central point the intensity values in the different sequences and, therefore, we will be able to capture the variability of the data in each sequence at that moment. The results are based on the segmentation references and the proposed evaluation metrics to be able to validate the development different methods for segmentation were applied as inputs and what was obtained as a result of the segmentation originated a group of images that correspond to each of the cuts that have maximum resolution in the obtained sequences. All the images obtained here including the segmentation referred to, must be binary and their pixels must be marked with 1 (liver) or 0 (without liver). In conclusions the segmentation method that we propose here will consist of an active contour modeling in 2D and 3D and that will be developed in images that will be produced based on a new developed descriptor, having as an important point to minimize the image with an Approximation that is dual to the variational problem which should give us good results in the segmentation process.
    VL  - 5
    IS  - 1
    ER  - 

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Author Information
  • Department of Electronic Engineering, Faculty of Engineering, Popular University of Cesar, Valledupar, Colombia

  • Department of Electronic Engineering, Faculty of Engineering, Popular University of Cesar, Valledupar, Colombia

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