Neural networks for medical image processing

• Autorzy: Pieciak T. , Jaworek J. , Gorgon M.
• Języki publikacji: EN

Abstrakty

EN

The proposed article presents the most common types of artificial neural networks used to be performed in the field of medical imaging. The first section describes the use of artificial neural networks in the preprocessing stage, restoration of noisy and distorted images and in conjunction with morphological operations. The second part presents the artificial neural networks in image segmentation problem, particularly in adaptive binarization threshold level selection and as a complement to the active contour method.

Słowa kluczowe

EN

artificial neural networks; medical imaging; image processing; image restoration; morphological operations; segmentation; active contour; pulse-coupled neural networks; PCNN

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2011
• Tom: Vol. 7, no. 4
• Strony: 101--110
• Opis fizyczny: Bibliogr. 53 poz., rys., tab.

Twórcy

autor

Pieciak T.

• AGH University of Science and Technology Faculty of Electrical Engineering, Automatics, Computer Science and Electronics Department of Automatics, al. Mickiewicza 30, Kraków

autor

Jaworek J.

• AGH University of Science and Technology Faculty of Electrical Engineering, Automatics, Computer Science and Electronics Department of Automatics, al. Mickiewicza 30, Kraków

autor

Gorgon M.

• AGH University of Science and Technology Faculty of Electrical Engineering, Automatics, Computer Science and Electronics Department of Automatics, al. Mickiewicza 30, Kraków

Bibliografia

• 1. Mikrut Z., Tadeusiewicz R.: Sieci neuronowe w przetwarzaniu i rozpoznawaniu obrazów. In: Biocybernetyka i Inżynieria Biomedyczna 2000, t. 6: Sieci neuronowe. Warszawa: EXIT, 2000, pp. 459–493 (in Polish).
• 2. Shi Z., He L.: Application of Neural Networks in Medical Image Processing. Proceedings of Second International Symposium on Networking and Network Security, 2010, pp. 23-26.
• 3. Egmont-Petersena M., de Ridder D., Handels H.: Image processing with neural networks – a review. Pattern Recognition 2002, 35: 2279–2301.
• 4. Lindblad T., Kinser J.M.: Image Processing Using Pulse-Coupled Neural Networks. Heidelberg: Springer-Verlag, 2005.
• 5. Tadeusiewicz R., Korohoda P.: Komputerowa analiza i przetwarzanie obrazów. Kraków: Wydawnictwo Fundacji Postępu Telekomunikacji, 1997 (in Polish).
• 6. Socha M., Duda K., Zieliński T. P., Duplaga M.: Algorytmiczna korekcja zniekształceń geometrycznych kamery bronchoskopu. XV Sympozjum Modelowanie i Symulacja Systemów Pomiarowych, 2005, pp. 219-228 (in Polish).
• 7. Pawliczek P., Romanowska-Pawliczek A., Soltys Z.: Parallel Deconvolution of Large 3D Images Obtained by Confocal Laser Scanning Microscopy. Microscopy Research and Techniques 2010, 73: pp. 183-194.
• 8. Banham M. R., Katsaggelos A. K.: Digital Image Restoration. IEEE Signal Processing Magazine 1997, 14, 2: 24-41.
• 9. Gonzalez R. C., Woods R. E.: Digital Image Processing. Upper Saddle River, NJ: Pearson Prentice Hall, 2008.
• 10. Zhang J., Katsaggelos A. K.: Image Recovery Using EM Algorithm. In: Madisetti V. K., Williams D. B. (eds), Digital Signal Processing Handbook. Boca Raton, Fl: CRC Press, 1999, pp. 29-1 - 29-26.
• 11. Guan L., Perry S. W., Wong H. S.: Adaptive Image Processing: A Computational Intelligence Perspective. Boca Raton, FL: CRC Press, 2001.
• 12. Tadeusiewicz R., Śmietański J.: Pozyskiwanie obrazów medycznych oraz ich przetwarzanie, analiza, automatyczne rozpoznawanie i diagnostyczna interpretacja. Kraków: WSTN, 2011 (in Polish).
• 13. Osipowicz K., Budowa komórkowych sieci neuronowych i ich zastosowania do rozpoznawania obrazów. Software 2.0 2002, 2 (in Polish).
• 14. Duch W., Korbicz J., Rutkowski L., Tadeusiewicz R. (red.): Biocybernetyka i Inżynieria Biomedyczna 2000, t. 6: Sieci Neuronowe. Warszawa: EXIT 2000 (in Polish).
• 15. Yang T., Yang L.-B., Wu C. W., Chua L. O.: Fuzzy cellular neural networks: applications. 4th International Workshop on Cellular Neural Networks and Their Applications, Seville, Spain, 1996, pp. 225-230.
• 16. Yang L. B.: Implementation of binary mathematical morphology using Discrete-Time Cellular Neural Networks. 4th International Workshop on Cellular Neural Networks and Their Applications, Seville, Spain, 1996, pp. 7-12.
• 17. Mizutani E., Kozek T., Chua L.: Roadway lane marker extraction by motion detection CNNs. Proc. IJCNN, Alaska, 1998, Vol. 1, pp. 503-508.
• 18. Roska T., Kek L., CSL-CNN Software Library: Templates and Algorithms (Version 6.4), DNS-CADET-15, MTA SzTAKI, Hungarian Academy of Sciences, Budapest, 1995.
• 19. Ranganath H. S., Kuntimad G., Johnson J. L.: A neural network for image understanding, Handbook of Neural Computation. Bristol: IOP Publishining, 1997 .
• 20. Winkler G.: Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. A Mathematical Introduction, Berlin, Heidelberg: Springer-Verlag, 2006.
• 21. Zhou Y. T., Chellappa R. W., Vaid A., Jenkins B. K.: Image Restoration Using a Neural Network. IEEE Transactions on Acoustics, Speech and Signal Processing 1998, 36, 7: 1141–1151.
• 22. Suri J. S., Setarehdan S. K., Singh S.: Advanced Algorithmic Approaches to Medical Image Segmentation. State-of-the-Art Applications in Cardiology, Neurology, Mammography and Pathology, London: Springer, 2002.
• 23. Wismueller A.: Segmentation with Neural Networks, In: Bankman I.N. (ed.), Handbook of Medical Image Processing and Analysis. Amsterdam: Elsevier, 2009, pp. 113-143.
• 24. Chaira T., Ray A. K.: Fuzzy Image Processing and Applications with MATLAB. Boca Raton, FL: CRC Press 2009.
• 25. Ogiela M., Tadeusiewicz R.: Modern Computational Intelligence Method for the Interpretation of Medical Images. Berlin, Heidelberg: Springer-Verlag, 2008.
• 26. Papamarkos N.: A Technique for Fuzzy Document Binarization. 10th International Conference on Information and Knowledge Management, ACM Symposium on Document Engineering, Atlanta, 2001, pp. 152-156.
• 27. Ntogos N., Veintzas D.: A binarization algorithm for historical manuscripts. 12th WSEAS International Conference on COMMUNICATIONS, Greece 2008, pp. 43-51.
• 28. Just D., Ling D. T.: Neural networks for binarizing computer-generated holograms. Optics Communications 1991, 81, 1-2: 1-5.
• 29. Ke Z., Guizhong L.: Binarization processing for blurring edge image with Hopfield network. Journal of Electronics & Information Technology 1998, 20, 1: 38-43.
• 30. Chen T., Takagi M.: Image Binarization by Back Propagation Algorithm, ISPRS, 1991, pp. 345-349.
• 31. Cyganek B., Korohoda P.: Improved neural network for fast extraction of information from stereo-images. Proc. 4th Conf. Neural Networks and their Applications, Zakopane, 1999.
• 32. McInerney T., Terzopoulos D.: Deformable Models, In: Bankman I.N.(ed.), Handbook of Medical Image Processing and Analysis. Amsterdam: Elsevier, 2009, pp. 145-166.
• 33. Sczypiński P. M.: Modele deformowalne do ilościowej analizy i rozpoznawania obiektów w obrazach cyfrowych, Wydział Elektrotechniki i Elektroniki Politechniki Łódzkiej, Łódź 2000, http://www.eletel.p.lodz.pl/pms/Doktorat_2000.pdf (in Polish).
• 34. Kass M., Witkin A., Terzopoulos D.: Snakes: Active contour models. International Journal of Computer Vision 1988, 1: 321-331.
• 35. Xu C.: Deformable Models with Application to Human Cerebral Cortex Reconstruction from Magnetic Resonance Images. PhD thesis, The Johns Hopkins University, 1999, http://vismod.media.mit.edu/pub/elwin/this/cheyang_xu_thesis.pdf.
• 36. Shang Y., Yang X., Zhu M., Hin B., Liu M.: Prior Based Cardiac Valve Segmentation in Echocardiographic Sequences: Geodesic Active Contour Guided by Region and Shape Prior, Pattern Recognition and Image Analysis. Lecture Notes in Computer Science 2005, 3523: 447–454.
• 37. Silveira M., Marques J.: Automatic segmentation of the lungs using multiple active contours and outlier model. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp. 3122–3125.
• 38. Al-Diri B., A. Hunter A., Steel D.: An Active Contour Model for Segmenting and Measuring Retinal Vessels. IEEE Transactions on Medical Imaging 2009, 28, 9: 1488–1497.
• 39. Deklerck R., Nyssen E., Markova A., de Mey J., Yang X., Sun K.: Vascular Active Contour for Vessel Tree Segmentation. IEEE Transactions on Biomedical Engineering 2011, 58, 4: 1023–1032.
• 40. Pieciak T.: Myocardial Segmentation Based on Magnetic Resonance Sequences. Bio-Algorithms and Med-Systems 2010, 6, 12: 85-90.
• 41. Lee H.-Y., Codella N. C. F., Cham M. D., Weinsaft J. W., Wang Y.: Automatic Left Ventricle Segmentation Using Iterative Thresholding and an Active Contour Model with Adaptation on Short-Axis Cardiac MRI. IEEE Transactions on Biomedical Engineering 2010, 57, 4: 905–913.
• 42. Xu Ch., Prince J. L.: Snakes, Shapes and Gradient Vector Flow. IEEE Transactions on Image Processing 1998, 7, 3: 359-369.
• 43. Acton S. T., Ray N.: Biomedical Image Analysis. Segmentation, Synthesis Lectures on Image, Video & Multimedia Processing. San Rafael, CA: Morgan & Claypool, 2009.
• 44. Pluempitiwiriyawej Ch., Moura J. M. F., Lin Wu Y-J, Ho Ch.: STACS: New Active Contour Scheme for Cardiac MR Image Segmentation. IEEE Transactions on Medical Imaging 2005, 24, 5: 593–603.
• 45. Wu H-H., Liu J-Ch., Chui Ch.: A Wavelet-Frame Based Image Force Model for Active Contouring Algorithms. IEEE Transactions on Image Processing 2000, 9, 11: 1983–1988.
• 46. Krinidis S., Chatzis V.: Fuzzy Energy-Based Active Contours. IEEE Transactions on Image Processing 2009, 18, 12: 2747–2755.
• 47. Tsai C.-T., Sun Y.-N., Chung P.-C.: Minimising the energy of active contour model using a Hopfield network. IEE Proceedings-E Computers and Digital Techniques 1993, 140, 6: 297-303.
• 48. Eckhorn R., Bauer R., Jordan W., Brosch M., Kruse W., Munk M., Reitboeck H. J., Coherent Oscillations: A Mechanism of Feature Linking in the Visual Cortex? Biol. Cybern. 1988, 60: 121-130.
• 49. Chou N., Wu J., Bingren J.B., Qiu A., Chuang K-H, Robust Automatic Rodent Brain Extraction Using 3-D Pulse-Coupled Neural Networks (PCNN). IEEE Transactions on Image Processing 2011, 20, 9: 2554-2564.
• 50. Mao-jun S., Zhao-bin W., Hong-juan Z., Yi-de M., A new method for blood cell image segmentation and counting based on PCNN and autowave. 3rd International Symposium on Communications, Control and Signal Processing 2008, pp. 6-9.
• 51. Keller P.E., McKinnon, A.D., Segmentation of medical imagery with pulse-coupled neural networks. International Joint Conference on Neural Networks 1999, pp. 2659-2663.
• 52. Fua J.C., Chenb C.C., Chaib J.W., Wonge S.T.C., Li I.C., Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging. Computerized Medical Imaging and Graphics 2010, 34: 308–320.
• 53. Ma Y., Zhan K., Wang Z., Applications of Pulse-Coupled Neural Networks. Berlin, Heidelberg: Springer-Verlag, 2011.