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Opto-Electronics Review

Editor-in-Chief: Jaroszewicz, Leszek

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1896-3757
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Volume 19, Issue 2

Issues

Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions

M. Ogiela / S. Bodzioch
Published Online: 2011-04-08 | DOI: https://doi.org/10.2478/s11772-011-0001-y

Abstract

This paper presents a new approach to gallbladder ultrasonic image processing and analysis towards automatic detection and interpretation of disease symptoms on processed US images. First, in this paper, there is presented a new heuristic method of filtering gallbladder contours from images. A major stage in this filtration is to segment and section off areas occupied by the said organ. This paper provides for an inventive algorithm for the holistic extraction of gallbladder image contours, based on rank filtration, as well as on the analysis of line profile sections on tested organs. The second part concerns detecting the most important lesion symptoms of the gallbladder. Automating a process of diagnosis always comes down to developing algorithms used to analyze the object of such diagnosis and verify the occurrence of symptoms related to given affection. The methodology of computer analysis of US gallbladder images presented here is clearly utilitarian in nature and after standardising can be used as a technique for supporting the diagnostics of selected gallbladder disorders using the images of this organ.

Keywords: medical pattern interpretation; image processing; gallbladder lesion detection; ultrasound image recognition

  • [1] S.A. Cover, N.F. Ezquerra, J.F. O’Brien, R. Rowe, T. Gadacz, and E. Palm, “Interactively deformable models for surgery simulation”, IEEE Comput. Graph. 13, 68–75 (1993). http://dx.doi.org/10.1109/38.252559CrossrefGoogle Scholar

  • [2] J. Singh, and S.S. Bhatti, “System theoretic modelling and simulation of gall-bladder bile”, IEEE Int. Sys. Man Cybern. 1, 940–944 (1997). Google Scholar

  • [3] P.A. Vernkatachalam, A.F. Mohd Hani, U.K. Ngah, and E.E. Lim, “Processing of abdominal ultrasound images using seed based region growing method”, Proc. Int. Conf. on Intelligent Sensing and Information Processing, 57–62 (2004). Google Scholar

  • [4] A. Meyer-Baese, Pattern Recognition in Medical Imaging, Elsevier, San-Diego, 2003. Google Scholar

  • [5] R. Tadeusiewicz, and M.R. Ogiela, Medical Image Understanding Technology, Springer, Berlin, 2004. Google Scholar

  • [6] T.C. Noone, R.C. Semelka, D.M. Chaney, and C. Reinhold, “Abdominal imaging studies: comparison of diagnostic accuracies resulting from ultrasound, computed tomography, and magnetic resonance imaging in the same individual”, Magn. Reson. Imaging. 22, 19–24 (2004). http://dx.doi.org/10.1016/j.mri.2003.01.001CrossrefGoogle Scholar

  • [7] W.A. Lu, Y.Y. Lin Wang, and W.K. Wang, “Pulse analysis of patients with severe liver problems. Studying pulse spectrums to determine the effects on other organs”, IEEE Eng. Med. Biol. 18, 73–75 (1999). http://dx.doi.org/10.1109/51.740985CrossrefGoogle Scholar

  • [8] S. Bodzioch and M.R. Ogiela, “Effective filtration techniques for gallbladder ultrasound images with variable contrast”, J. Signal Process. Sys. 54, 127–144 (2009). http://dx.doi.org/10.1007/s11265-008-0181-yWeb of ScienceCrossrefGoogle Scholar

  • [9] M.R. Ogiela, R. Tadeusiewicz, and M. Trzupek, “Picture grammars in classification and semantic interpretation of 3D coronary vessels visualisations”, Opto-Electron. Rev. 17, 200–210 (2009). http://dx.doi.org/10.2478/s11772-009-0004-0CrossrefWeb of ScienceGoogle Scholar

  • [10] L. Ogiela, “UBIAS systems for cognitive interpretation and analysis of medical images”, Opto-Electron. Rev. 17, 166–179 (2009). http://dx.doi.org/10.2478/s11772-008-0069-1CrossrefWeb of ScienceGoogle Scholar

  • [11] M.R. Ogiela and R. Tadeusiewicz, Modern Computational Intelligence Methods for the Interpretation of Medical Images, Springer-Verlag, Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75402-2CrossrefGoogle Scholar

  • [12] L. Ogiela, R. Tadeusiewicz, and M.R. Ogiela, “Cognitive categorizing in UBIAS intelligent medical information systems”, in Advanced Computational Intelligence Paradigms in Healthcare 3, Studies in Computational Intelligence 107, pp. 75–94, edited by M. Sordo, S. Vaidya, L.C. Jain, Springer-Verlag, Berlin, Heidelberg, 2008. Google Scholar

  • [13] T. Pavlidis, Algorithms for Graphics and Image Processing, Computer Science Press. Berlin, 1982. http://dx.doi.org/10.1007/978-3-642-93208-3CrossrefGoogle Scholar

  • [14] E.I. Bluth, C. Benson, P.W. Ralls, and M.J. Siegel, Ultrasound-A Practical Approach to Clinical Problems, Thieme, Stuttgart, 2000. Google Scholar

  • [15] C.P. Loizou, C.S. Pattichis, M. Pantziaris, T. Tyllis, and A. Nicolaides, “Snakes based segmentation of the common carotid artery intima media”, Med. Biol. Eng. Comput. 45, 35–49 (2007). http://dx.doi.org/10.1007/s11517-006-0140-3CrossrefWeb of ScienceGoogle Scholar

  • [16] V.R. Singh, S. Singh, and U. Dhawan, “Structural analysis of gall-bladder stones”, Proc. the 1st Joint BMES/EMBS Conf., Vol. 2, 821 (1999). Google Scholar

  • [17] P. Stetson, F. Sommer, and A. Macovski, “Lesion contrast enhancement in medical ultrasound imaging”, IEEE T. Med. Imaging. 16, 416–425 (1997). http://dx.doi.org/10.1109/42.611351CrossrefGoogle Scholar

About the article

Published Online: 2011-04-08

Published in Print: 2011-06-01


Citation Information: Opto-Electronics Review, Volume 19, Issue 2, Pages 155–168, ISSN (Online) 1896-3757, DOI: https://doi.org/10.2478/s11772-011-0001-y.

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© 2011 SEP, Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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