Araştırma Makalesi
BibTex RIS Kaynak Göster

Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması

Yıl 2011, Cilt: 8 Sayı: 1, - , 01.05.2011

Öz

Image segmentation is an important step in many computer vision algorithms.
The objective of segmentation is to obtain an optimal region of convergence. Error in
this stage will impact all higher level activities. In this study, three types of complexvalued
classifier were compared to the segmentation of lung region. These classifiers are
complex-valued artificial neural network (CVANN), complex-valued wavelet artificial neural
network (CVWANN) and complex valued artificial neural network with complex wavelet
transform (CWT-CVANN). To test the performance of the proposed systems, Lung Image
Database Consortium (LIDC) dataset was used. Obtained results shown that lung region
segmentation done using CVWANN and CVANN with worst accuracy rates as 38.59% and
75.66%, respectively. On the other hand, CWT-CVANN structure segmented lung region with 100% accuracy rate. Moreover, this structure required only 4.5 second per image for
segmentation task. Thus, it is concluded that CWT-CVANN is a comprising method in
lung region segmentation problem.


Kaynakça

  • [1] I. N. Backman (Editor-in-Chief), Handbook of Medical Imaging, Academic Press 2000.
  • [2] J. C. Bezdek, L.O. Hall and L. P. Clarke, Review of MR image segmentation techniques using pattern recognition, Medical Physics 20 (1993), 1033–1048.
  • [3] K. S. Fu and J. K. Mui, A survey on image segmentation, Pattern Recognition 13 (1981), 3–16.
  • [4] R. M. Haralick and L. G. Shapiro, Survey: image segmentation techniques, Computer Vision, Graphics, and Image Processing 29 (1985), 100–132.
  • [5] A. Mitiche and J. K. Aggarwal, Image segmentation by conventional and informationintegrating techniques: a synopsis, Image and Visual Computing 3 (1985), 50–62.
  • [6] N. Shareef, D. L. Wand and R. Yagel, Segmentation of medical images using LEGION, IEEE Transactions on Medical Imaging 18 (1999), 74–91.
  • [7] G. J. Awcock and R. Thomas, Applied Image Processing, McGraw-Hill, New York 1996.
  • [8] P. Suetens, E. Bellon, D. Vandermeulen, M. Smet, G. Marchal, J. Nuyts and L. Mortelman, Image segmentation: methods and applications in diagnostic radiology and nuclear medicine, European Journal of Radiology 17 (1993), 14-21.
  • [9] J. C. Rajapakse, J. N. Giedd and J. L. Rapport, Statistical approach to segmentation of singlechannel cerebral MR images, IEEE Transactions on Medical Imaging 16 (1997), 176–186.
  • [10] A. P. Dhawan, Medical Image Analysis, Wiley-Interscience, USA 2003.
  • [11] A. P. Dhawan and L. Arata, Segmentation of medical images through competitive learning, Computer Methods and Programs in Biomedicine 40 (1993), 203–215.
  • [12] A. Sarwal and A. P. Dhawan, Segmentation of coronary arteriograms through radial basis function neural network, Journal of Computing and Information Technology 6 (1998), 135–148.
  • [13] M. Ozkan, B. M. Dawant and R. J. Maciunas, Neural-network based segmentation of multimodal medical images: a comparative and prospective study, IEEE Transactions on Medical Imaging 12 (1993), 534-544.
  • [14] D. D. Sha and J. P. Sutton, Towards automated enhancement, segmentation and classification of digital brain images using networks of networks, Information Sciences 138 (2001), 45-77.
  • [15] T. W. Nattkemper, H. Wersing, W. Schubert and H. Ritter, A neural network architecture for automatic segmentation of fluorescence micrographs, Neurocomputing 48 (2002), 357–367.
  • [16] A. Papadopoulos, D. I. Fotiadis and A. Likas, An automatic microcalcification detection system based on a hybrid neural network classifier, Artificial Intelligence in Medicine 25 (2002), 149–167.
  • [17] Z. Dokur and T. Olmez, Segmentation of ultrasound images by using a hybrid neural network, Pattern Recognition Letters 23 (2002), 1825–1836.
  • [18] D. L. Vilarino, D. Cabello, X. M. Pardo and V. M. Brea, Cellular neural networks and active contours: a tool for image segmentation, Image and Vision Computing 21 (2003), 189–204.
  • [19] I. Middleton and R. I. Damper, Segmentation of magnetic resonance images using a combination of neural networks and active contour models, Medical Engineering and Physics 26 (2004), 71–86.
  • [20] M. I. Rajab, M. S. Woolfson and S. P. Morgan, Application of region-based segmentation and neural network edge detection to skin lessions, Computerized Medical Imaging and Graphics 28 (2004), 61–68.
  • [21] L. Cinque, G. Foresti and L. Lombardi, A clustering fuzzy approach for image segmentation, Pattern Recognition 37 (2004), 1797–1807.
  • [22] J. C. Fu, S. K. Lee, S. T. C. Wong, J. Y. Yeh, A. H. Wang and H. K. Wu, Image segmentation feature selection and pattern classification for mammographic microcalcifications, Computerized Medical Imaging and Graphics 29 (2005), 419–429.
  • [23] Z. Dokur, A unified framework for image compression and segmentation by using an incremental neural network, Expert Systems with Applications 34 (2008), 611–619.
  • [24] M. N. Kurnaz, Z. Dokur and T. Olmez, Segmentation of remote-sensing images by incremental neural network, Pattern Recognition Letters 26 (2005), 1096–1104.
  • [25] M. N. Kurnaz, Z. Dokur and T. Olmez, An incremental neural network for tissue segmentation in ultrasound images, Computer Methods an Programs in Biomedicine 85 (2007), 187–195.
  • [26] A. Wismuller, F. Vietze, J. Behrens, A. Meyer-Baese, M. Reiser and H. Ritter, Fully automated biomedical iamge segmentation by self-organized model adaptation, Neural Networks 17 (2004), 1327–1344.
  • [27] S. H. Ong, N. C. Yeo, K. H. Lee, Y. V. Venkatesh and D. M. Cao, Segmentation of color images using a two-stage self-organizing network, Image and Visual Computing 20 (2002), 279–289.
  • [28] M. Ceylan, R. Ceylan, F. Dirgenali, S. Kara and Y. Ozbay, Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network, Computers in Biology and Medicine 37 (2007), 28–36.
  • [29] Y. Ozbay and M. Ceylan, Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network, Computers in Biology and Medicine 37 (2007), 287–295.
  • [30] A. Hirose (Editor), Complex-Valued Neural Networks: Theories and Applications, World Scientific 2003.
  • [31] A. Pande and V. Goel, Complex-valued neural network in image recognition: a study on the effectiveness of radial basis function, Proceedings of World Academy of Science, Engineering and Technology 26 (2007), 220–225.
  • [32] M. Ceylan, Y. Ozbay, O. N. U¸can and E. Yıldırım, A novel method for lung segmentation on chest CT images: complex-valued artificial neural network with complex wavelet transform, Turkish Journal of Electrical Engineering & Computer Sciences 18 (2010), 613–624.
  • [33] S. G. Armato 3rd, G. McLennan, M. F. McNitt-Gray, C. R. Meyer, D. Yankelevitz, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. Kazerooni, H. MacMahon, A. P. Reeves, B. Y. Croft and L. P. Clarke, Lung image database consortium: developing a resource for the medical imaging research community, Radiology 232 (2004), 739–748.
  • [34] P. D. Shukla, Complex Wavelet Transforms and Their Applications, PhD Thesis, The University of Strathclyde 2003.
  • [35] I. W. Selesnick, R. G. Baraniuk and N. Kingsbury, The Dual-Tree complex wavelet transform, IEEE Signal Processing Magazine 22 (2005), 123–151.
  • [36] T. Nitta, A back-propagation algorithm for complex numbered neural networks, Proceedings of 1993 International Joint Conference on Neural Networks (1993), 1649–1652.
  • [37] Y. Ozbay, S. Kara, F. Latifoglu, R. Ceylan and M. Ceylan, Complex-valued wavelet artificial neural network for Doppler signals classifying, Artifical Intelligence in Medicine 40 (2007), 143–156.
Yıl 2011, Cilt: 8 Sayı: 1, - , 01.05.2011

Öz

Kaynakça

  • [1] I. N. Backman (Editor-in-Chief), Handbook of Medical Imaging, Academic Press 2000.
  • [2] J. C. Bezdek, L.O. Hall and L. P. Clarke, Review of MR image segmentation techniques using pattern recognition, Medical Physics 20 (1993), 1033–1048.
  • [3] K. S. Fu and J. K. Mui, A survey on image segmentation, Pattern Recognition 13 (1981), 3–16.
  • [4] R. M. Haralick and L. G. Shapiro, Survey: image segmentation techniques, Computer Vision, Graphics, and Image Processing 29 (1985), 100–132.
  • [5] A. Mitiche and J. K. Aggarwal, Image segmentation by conventional and informationintegrating techniques: a synopsis, Image and Visual Computing 3 (1985), 50–62.
  • [6] N. Shareef, D. L. Wand and R. Yagel, Segmentation of medical images using LEGION, IEEE Transactions on Medical Imaging 18 (1999), 74–91.
  • [7] G. J. Awcock and R. Thomas, Applied Image Processing, McGraw-Hill, New York 1996.
  • [8] P. Suetens, E. Bellon, D. Vandermeulen, M. Smet, G. Marchal, J. Nuyts and L. Mortelman, Image segmentation: methods and applications in diagnostic radiology and nuclear medicine, European Journal of Radiology 17 (1993), 14-21.
  • [9] J. C. Rajapakse, J. N. Giedd and J. L. Rapport, Statistical approach to segmentation of singlechannel cerebral MR images, IEEE Transactions on Medical Imaging 16 (1997), 176–186.
  • [10] A. P. Dhawan, Medical Image Analysis, Wiley-Interscience, USA 2003.
  • [11] A. P. Dhawan and L. Arata, Segmentation of medical images through competitive learning, Computer Methods and Programs in Biomedicine 40 (1993), 203–215.
  • [12] A. Sarwal and A. P. Dhawan, Segmentation of coronary arteriograms through radial basis function neural network, Journal of Computing and Information Technology 6 (1998), 135–148.
  • [13] M. Ozkan, B. M. Dawant and R. J. Maciunas, Neural-network based segmentation of multimodal medical images: a comparative and prospective study, IEEE Transactions on Medical Imaging 12 (1993), 534-544.
  • [14] D. D. Sha and J. P. Sutton, Towards automated enhancement, segmentation and classification of digital brain images using networks of networks, Information Sciences 138 (2001), 45-77.
  • [15] T. W. Nattkemper, H. Wersing, W. Schubert and H. Ritter, A neural network architecture for automatic segmentation of fluorescence micrographs, Neurocomputing 48 (2002), 357–367.
  • [16] A. Papadopoulos, D. I. Fotiadis and A. Likas, An automatic microcalcification detection system based on a hybrid neural network classifier, Artificial Intelligence in Medicine 25 (2002), 149–167.
  • [17] Z. Dokur and T. Olmez, Segmentation of ultrasound images by using a hybrid neural network, Pattern Recognition Letters 23 (2002), 1825–1836.
  • [18] D. L. Vilarino, D. Cabello, X. M. Pardo and V. M. Brea, Cellular neural networks and active contours: a tool for image segmentation, Image and Vision Computing 21 (2003), 189–204.
  • [19] I. Middleton and R. I. Damper, Segmentation of magnetic resonance images using a combination of neural networks and active contour models, Medical Engineering and Physics 26 (2004), 71–86.
  • [20] M. I. Rajab, M. S. Woolfson and S. P. Morgan, Application of region-based segmentation and neural network edge detection to skin lessions, Computerized Medical Imaging and Graphics 28 (2004), 61–68.
  • [21] L. Cinque, G. Foresti and L. Lombardi, A clustering fuzzy approach for image segmentation, Pattern Recognition 37 (2004), 1797–1807.
  • [22] J. C. Fu, S. K. Lee, S. T. C. Wong, J. Y. Yeh, A. H. Wang and H. K. Wu, Image segmentation feature selection and pattern classification for mammographic microcalcifications, Computerized Medical Imaging and Graphics 29 (2005), 419–429.
  • [23] Z. Dokur, A unified framework for image compression and segmentation by using an incremental neural network, Expert Systems with Applications 34 (2008), 611–619.
  • [24] M. N. Kurnaz, Z. Dokur and T. Olmez, Segmentation of remote-sensing images by incremental neural network, Pattern Recognition Letters 26 (2005), 1096–1104.
  • [25] M. N. Kurnaz, Z. Dokur and T. Olmez, An incremental neural network for tissue segmentation in ultrasound images, Computer Methods an Programs in Biomedicine 85 (2007), 187–195.
  • [26] A. Wismuller, F. Vietze, J. Behrens, A. Meyer-Baese, M. Reiser and H. Ritter, Fully automated biomedical iamge segmentation by self-organized model adaptation, Neural Networks 17 (2004), 1327–1344.
  • [27] S. H. Ong, N. C. Yeo, K. H. Lee, Y. V. Venkatesh and D. M. Cao, Segmentation of color images using a two-stage self-organizing network, Image and Visual Computing 20 (2002), 279–289.
  • [28] M. Ceylan, R. Ceylan, F. Dirgenali, S. Kara and Y. Ozbay, Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network, Computers in Biology and Medicine 37 (2007), 28–36.
  • [29] Y. Ozbay and M. Ceylan, Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network, Computers in Biology and Medicine 37 (2007), 287–295.
  • [30] A. Hirose (Editor), Complex-Valued Neural Networks: Theories and Applications, World Scientific 2003.
  • [31] A. Pande and V. Goel, Complex-valued neural network in image recognition: a study on the effectiveness of radial basis function, Proceedings of World Academy of Science, Engineering and Technology 26 (2007), 220–225.
  • [32] M. Ceylan, Y. Ozbay, O. N. U¸can and E. Yıldırım, A novel method for lung segmentation on chest CT images: complex-valued artificial neural network with complex wavelet transform, Turkish Journal of Electrical Engineering & Computer Sciences 18 (2010), 613–624.
  • [33] S. G. Armato 3rd, G. McLennan, M. F. McNitt-Gray, C. R. Meyer, D. Yankelevitz, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. Kazerooni, H. MacMahon, A. P. Reeves, B. Y. Croft and L. P. Clarke, Lung image database consortium: developing a resource for the medical imaging research community, Radiology 232 (2004), 739–748.
  • [34] P. D. Shukla, Complex Wavelet Transforms and Their Applications, PhD Thesis, The University of Strathclyde 2003.
  • [35] I. W. Selesnick, R. G. Baraniuk and N. Kingsbury, The Dual-Tree complex wavelet transform, IEEE Signal Processing Magazine 22 (2005), 123–151.
  • [36] T. Nitta, A back-propagation algorithm for complex numbered neural networks, Proceedings of 1993 International Joint Conference on Neural Networks (1993), 1649–1652.
  • [37] Y. Ozbay, S. Kara, F. Latifoglu, R. Ceylan and M. Ceylan, Complex-valued wavelet artificial neural network for Doppler signals classifying, Artifical Intelligence in Medicine 40 (2007), 143–156.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Makaleler
Yazarlar

Murat Ceylan

Yüksel Özbay Bu kişi benim

Osman Nuri Uçan Bu kişi benim

Yayımlanma Tarihi 1 Mayıs 2011
Yayımlandığı Sayı Yıl 2011 Cilt: 8 Sayı: 1

Kaynak Göster

APA Ceylan, M., Özbay, Y., & Uçan, O. N. (2011). Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması. Cankaya University Journal of Science and Engineering, 8(1).
AMA Ceylan M, Özbay Y, Uçan ON. Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması. CUJSE. Mayıs 2011;8(1).
Chicago Ceylan, Murat, Yüksel Özbay, ve Osman Nuri Uçan. “Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması”. Cankaya University Journal of Science and Engineering 8, sy. 1 (Mayıs 2011).
EndNote Ceylan M, Özbay Y, Uçan ON (01 Mayıs 2011) Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması. Cankaya University Journal of Science and Engineering 8 1
IEEE M. Ceylan, Y. Özbay, ve O. N. Uçan, “Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması”, CUJSE, c. 8, sy. 1, 2011.
ISNAD Ceylan, Murat vd. “Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması”. Cankaya University Journal of Science and Engineering 8/1 (Mayıs 2011).
JAMA Ceylan M, Özbay Y, Uçan ON. Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması. CUJSE. 2011;8.
MLA Ceylan, Murat vd. “Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması”. Cankaya University Journal of Science and Engineering, c. 8, sy. 1, 2011.
Vancouver Ceylan M, Özbay Y, Uçan ON. Akciğer Bölgesinin Bölütlenmesinde Karmaşık Değerli Sınıflayıcıların Karşılaştırılması. CUJSE. 2011;8(1).