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BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ

Year 2015, Volume: 30 Issue: 3, 0 - , 30.09.2015
https://doi.org/10.17341/gummfd.93803

Abstract

Tıbbi görüntüleme ile anatomi hakkında ayrıntılı bilgi elde edinilebildiğinden, tanı amaçlı görüntüleme günümüzde birçok açıdan önem kazanmıştır. Görüntüleme cihazları tarafından sunulan verilerin fazlalığı ve çeşitliliği nedeniyle, tüm veri yerine görüntülerde sadece ilgilenilen dokunun belirlenerek ayrılması (Bölütlenmesi) sağlanabilir. Elcil yöntemler ile bölütleme yorucu, zaman alıcı ve deneyim gerektiren bir işlem olduğundan, otomatik yordamlara gereksinim duyulmaktadır. Geliştirilen yordamların klinik koşullarında kullanılabilmesi içinse yüksek başarıma sahip sonuçlar üretmeleri gerekmektedir. Manyetik Rezonans (MR) görüntülerinden batın bölgesindeki organlarının bölütlenmesi pek çok zorluk içeren bir uygulama alanıdır ve bu konudaki çalışmalar sınırlı sayıdadır. Batın bölgesinde yer alan, karaciğer, böbrekler, dalak, pankreas, safra kesesi gibi organların MR görüntüleri kullanılarak ileri seviye tıbbi analizi ve üç boyutlu incelenmesi pek çok tıbbi prosedür için mecburi olduğundan, bu çalışmada, ilgili organların bölütlenmesinde yukarıda belirtilen zorluklara karşı gürbüz, bölütlenecek organın özellikleri ve organların birbirleriyle olan ilişkilerini (konum vb.) göz önüne alan bir sistem geliştirilmiştir. Geliştirilen sistem farklı MR sekansları ile elde edilen görüntülere uygulanarak elde edilen sonuçlar tartışılmıştır.

References

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  • Park, H., Bland, P.H. ve Meyer, C.R., "Construction of an abdominal probabilistic atlas and its application in segmentation”, Medical Imaging, IEEE Transactions on , Cilt 22, No 4, 483-492, 2003.
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  • Barra, V. ve Boire, J.Y., “Segmentation of fat and muscle from MR images of the thigh by a possibilistic clustering algorithm”, Computer Methods and Programs in Biomedicine, Cilt 68, No 3, 185- 193 2002.
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  • de Bruijne, M. et al, “Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images”, Medical Image Computing and Computer-Assisted Intervention MICCAI 2003, Montreal, Canada, 2003.
  • Zhuge, F., Rubin, G. D. ve Sun, S., “An abdominal aortic aneurysm segmentation method: Level set with region and statistical information”, Napel Med. Phys. Cilt 33, 2006
  • Lapeer, R. J., Tan, A. C. ve Aldridge, R., “Active Watersheds: Combining 3D Watershed Segmentation and Active Contours to Extract Abdominal Organs from MR Images”, MICCAI 2002, Tokyo Japan, 2002.
  • Ballard, D., Shani, U. ve Schudy, R., “Anatomical models for medical images”, Computer Software and Applications Conference, 1979. Proceedings. COMPSAC 79. The IEEE Computer Society’s Third International, 1979.
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  • Zhou, Y., ve Bai, J., “Multiple abdominal organ segmentation: An atlas-based fuzzy connectedness approach”, Information Technology in Biomedicine, IEEE Transactions on, Cilt 11, No 3, 348 –352, 2007.
  • Park, H., Bland, P., ve Meyer, C., “Construction of an abdominal probabilistic atlas and its application in segmentation”, Medical Imaging, IEEE Transactions on, Cilt 22, No 4, 483 –492, 2003.
  • Kobashi, M. ve Shapiro, L.G. “Knowledge-based organ identification from ct images”, Pattern Recognition, Cilt 28, No 4, 475 – 491, 1995.
  • Yoo, S. W., Cho, J.-S., Noh, S.-M., Shin, K.-S. ve Park, J.-W., "Organ segmentation by comparing of gray value portion on abdominal CT image”, Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on , vol.2, no., pp.1201,1208 vol.2, 2000
  • Gu, L. ve Kaneko, T., “Organs extraction using three-dimensional mathematical morphology”, Signal Processing Proceedings, 1998. ICSP ’98. 1998 Fourth International Conference on, 1998.
  • Bae, K. T., Giger, M. L., Chen, C. T. ve Kahn, C. E., “Automatic segmentation of liver structure in CT images.” Medical physics, Cilt 20, No 1, 71–78, 1993.
  • Koss, J., Newman F., Johnson, T. ve Kirch, D., “Abdominal organ segmentation using texture transforms and a hopfield neural network”, Medical Imaging, IEEE Transactions on, Cilt 18, No 7, 640 –648, 1999.
  • Kurani, A., Xu, D., Furst, J. ve Raicu, D., “Co-occurrence matrices for volumetric data”, The Seventh IASTED International Conference on Computer Graphics and Imaging CGIM 2004, K. M. Hanson, Ed., 426–443, 2004,.
  • Ciurte A. ve Nedevschi, S., “Texture analysis within contrast enhanced abdominal ct images”, Intelligent Computer Communication and Processing, 2009. ICCP 2009. IEEE 5th International Conference on, 73 –78, 2009.
  • Selfridge, P., Judith, M., Prewitt, M., Dyer, C. ve Ranade S., “Segmentation algorithms for abdominal computerized tomography scans”, Computer Software and Applications Conference, Proc. IEEE Computer Society’s 3rd Intern., 1979.
  • Wu, J., Poehlman, S. , Noseworthy, M. ve Kamath, M., “Texture feature based automated seeded region growing in abdominal mri segmentation”, BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on, Cilt 2, 263 –267, 2008.
  • Campadelli, P., Casiraghi E. ve Pratissoli, S., “Fully automatic segmentation of abdominal organs from ct images using fast marching methods”, Computer-Based Medical Systems, CBMS, 21st IEEE International Symposium, pp. 554 –559, 2008. .
  • Jiang, H. ve Cheng, Q., “Automatic 3d segmentation of CT images based on active contour models”, Computer-Aided Design and Computer Graphics, 2009. CAD/Graphics ’09. 11th IEEE International Conference on, pp. 540 –543, 2009.
  • Siegel, E. L., Kolodner, R. M., (Eds.), Filmless Radiology, 1st ed., Springer, 1999.
  • Haacke, E. M., et al. "Magnetic resonance imaging." Physical principles and sequence design , 1999.
  • Lai, C.-C. ve Chang, C.-Y., “A hierarchical evolutionary algorithm for automatic medical image segmentation”, Expert Systems with Applications, Cilt 36, No 1, 248-259, 2009.
  • Husain, S.A., ve Shigeru, E., “Use of neural networks for feature based recognition of liver region on ct images”, Neural Networks for Signal Processing X, 2000. Proc. of the 2000 IEEE Signal Processing Society Workshop, Sydney, NSW 2000.
  • Chang, C.-Y. ve Chung, P.-C., “Medical image segmentation using a contextual-constraint-based hopfield neural cube”, Image and Vision Computing, Cilt 19, No 9-10, 669 – 678, 2001.
  • Lee, C.-C., Chung, P.-C. ve Tsai, H.-M., “Identifying multiple abdominal organs from ct image series using a multimodule contextual neural network and spatial fuzzy rules”, Information Tech. in Biomd, IEEE Trans. on, Cilt 7, No 3, 208 –217, 2003.
  • Chen, K. ve Chi, H., “A method of combining multiple probabilistic classifiers through soft competition on different feature sets”, Neurocomputing, Cilt 20, 227–252, 1998.
  • Kittler, J., Hatef, M., Duin, M. ve Matas, J., “On combining classifiers”, IEEE Trans. Pattern Anal. Mach. Intell., Cilt 20, No 3, 226 –239, 1998.
  • Ho, T. K., Hull J. J. ve Srihari S. N., “Decision combination in multiple classifier systems”, IEEE Trans. Pattern Anal. Mach. Intell., Cilt 16, 66–75, 1994.
  • Cao, J., Shridhar, M. ve Ahmadi, M., “Fusion of classifiers with fuzzy integrals”, in Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on, Cilt 1, 108–111 1995.
  • Chen, K., “On the use of different speech representations for speaker modeling”, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Trans. on, Cilt 35, No 3, 301–314, 2005.
  • Chen, K., Wang, L. ve Chi, H., “Methods of combining multiple classifiers with different features and their applications to text-independent speaker identification” Int. Jour. Pattern Recognition and Artificial Intelligence, Cilt 11, 417–445, 1997.
  • Xu, L., Krzyzak, A. ve Suen , C., “Methods of combining multiple classifiers and their applications to handwriting recognition”, Systems, Man and Cybernetics, IEEE Transactions on, Cilt 22, No 3, 418 –435, 1992.
  • Suen, C. Y., Legault, R., Nadal, C., Cheriet, M. ve Lam, L., “Building a new generation of handwriting recognition systems”, Pattern Recogn. Letters, Cilt 14, 303–315, 1993.
  • Huang, Y. S. ve Suen, C. Y., “A method of combining multiple experts for the recognition of unconstrained handwritten numerals”, IEEE Trans. Pattern Anal. Mach. Intell., Cilt 17, 90–94, January 1995.
  • Selver, M. A., Akay O., Alim F., Bardakçı, S. ve Ölmez, M., “An automated industrial conveyor belt system using image processing and hierarchical clustering for classifying marble slabs”, Robotics and Computer-Integrated Manufacturing, Cilt 27, No 1, 164 – 176, 2011.
  • Unser, M., ‘’Sum and difference histograms for texture classification,’’ IEEE Trans. Pattern Anal. Mach. Intell., Cilt PAMI-8, No 1, 118–125, 1986.
  • Selver, M. A., Kocaoğlu, A., Demir, G., Doğan, H., Dicle ve O., Güzeliş, C ., "Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation", Computers in Biology and Medicine, Cilt 38, No7, 765-784, 2008.
  • Selver, M.A., Kocaoglu, A., Akyar, H., Dicle, O., ve Guzelis, C., "Patient oriented neural networks to overcome challenges of abdominal organ segmentation in CT angiography studies”, ELECO, International Conf. on, pp.II-177-181, Nov 2009.
  • Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, 2. baskı, 1998.
  • Reed, R., Marks, R. J.: Neural Smithing: SupervisedLearning in Feed forward Artificial Neural Networks, MIT Press, 1999.
Year 2015, Volume: 30 Issue: 3, 0 - , 30.09.2015
https://doi.org/10.17341/gummfd.93803

Abstract

References

  • Wu, J., Kamath, MV., Noseworthy, MD., Boylan, C. ve Poehlman, S., “Segmentation of images of abdominal organs.” Crit Rev Biomed Eng, Cilt 36, No 5-6, 105-334, 2008.
  • Wang, H., Bai, J., Zhou, Y. ve Zhang, Y., “Abdominal atlas mapping in CT and MR volume images using a normalized abdominal coordinate system.” Comput Med Imaging Graph, Cilt 32, No 6, 442-451, 2008.
  • Zhou, Y. ve Bai, J. “Multiple abdominal organ segmentation: an atlas-based fuzzy connectedness approach.” IEEE Trans Inf Technol Biomed, Cilt 11, No 3, 348-352 2007.
  • Park, H., Bland, P.H. ve Meyer, C.R., "Construction of an abdominal probabilistic atlas and its application in segmentation”, Medical Imaging, IEEE Transactions on , Cilt 22, No 4, 483-492, 2003.
  • Koss, J.E., Newman, F.D., Johnson, T.K. ve Kirch, D.L., "Abdominal organ segmentation using texture transforms and a Hopfield neural network”, Medical Imaging, IEEE Transactions on , Cilt 18, No 7, 640-648, 1999
  • Campadelli, P., Casiraghi, E., Pratissoli, S. ve Lombardi, G., “Automatic abdominal organ segmentation from ct images”, Electronic Letters on Computer Vision and Image Analysis, Cilt 8, 1–14, 2009.
  • Sakashita, M., Kitasaka, T., Mori, K., Suenaga, Y. ve Nawano, S., “A method for extracting multiorgan from four-phase contrasted ct images based on ct value distribution estimation using em-algorithm”, Progress in biomedical optics and imaging, Cilt 8, No 1, 1 –12, 2007.
  • Linguraru, M. ve Summers, R., “Multi-organ automatic segmentation in 4d contrast-enhanced abdominal ct”, Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on, 45 –48, 2008.
  • Selver, M. A., "Segmentation of Abdominal Organs from CT using a Multi-Level, Hierarchical Neural Network Strategy", Computer Methods and Programs in Biomedicine, Cilt 113, 830-852, 2014..
  • Yuan, Z., Wang, Y., Yang J. ve Liu, Y., , "A novel automatic liver segmentation technique for MR images", Image and Signal Processing (CISP), 2010 3rd International Congress on , Cilt 3, No , 1282-1286, 2010.
  • Rafiee, A., Masoumi, H. ve Roosta, A., "Using neural network for liver detection in abdominal MRI images", Signal and Image Processing Applications (ICSIPA), 2009 IEEE International Conference on , Cilt , No , 21-26, 2009
  • Rajasvaran, L., Haw, T.W. ve Sarker, S.Z., “Liver isolation in abdominal MRI”, Journal of Medical Systems, Cilt 32, No 4, 259-268 2008.
  • Akyar, H., Selver, M.A. ve Demir, G.K., "Segmentation and registration of kidneys from contrast enhanced abdominal MR image", Signal Processing, Communication and Applications Conference, SIU 2008, IEEE 16th , Aydın, 1-4, 2008
  • Behrad, A.; Masoumi, H.; , "Automatic spleen segmentation in MRI images using a combined neural network and recursive watershed transform", Neural Network Applications in Electrical Engineering (NEUREL), 10th Symp., pp.63-67, Sept. 2010.
  • Barra, V. ve Boire, J.Y., “Segmentation of fat and muscle from MR images of the thigh by a possibilistic clustering algorithm”, Computer Methods and Programs in Biomedicine, Cilt 68, No 3, 185- 193 2002.
  • Terry, J. B., Weymouth, T. E., ve Meyer, C. R., “Multiple organ definition in ct using a bayesian approach for 3d model fitting”, Vision Geometry IV, Proc. SPIE, 244–251 1995.
  • de Bruijne, M. et al, “Automated Segmentation of Abdominal Aortic Aneurysms in Multi-spectral MR Images”, Medical Image Computing and Computer-Assisted Intervention MICCAI 2003, Montreal, Canada, 2003.
  • Zhuge, F., Rubin, G. D. ve Sun, S., “An abdominal aortic aneurysm segmentation method: Level set with region and statistical information”, Napel Med. Phys. Cilt 33, 2006
  • Lapeer, R. J., Tan, A. C. ve Aldridge, R., “Active Watersheds: Combining 3D Watershed Segmentation and Active Contours to Extract Abdominal Organs from MR Images”, MICCAI 2002, Tokyo Japan, 2002.
  • Ballard, D., Shani, U. ve Schudy, R., “Anatomical models for medical images”, Computer Software and Applications Conference, 1979. Proceedings. COMPSAC 79. The IEEE Computer Society’s Third International, 1979.
  • Karssemeijer, N., “A statistical method for automatic labeling of tissues in medical images”, Machine Vision and Applications, Cilt 3, 75–86, 1990.
  • Zhou, Y., ve Bai, J., “Multiple abdominal organ segmentation: An atlas-based fuzzy connectedness approach”, Information Technology in Biomedicine, IEEE Transactions on, Cilt 11, No 3, 348 –352, 2007.
  • Park, H., Bland, P., ve Meyer, C., “Construction of an abdominal probabilistic atlas and its application in segmentation”, Medical Imaging, IEEE Transactions on, Cilt 22, No 4, 483 –492, 2003.
  • Kobashi, M. ve Shapiro, L.G. “Knowledge-based organ identification from ct images”, Pattern Recognition, Cilt 28, No 4, 475 – 491, 1995.
  • Yoo, S. W., Cho, J.-S., Noh, S.-M., Shin, K.-S. ve Park, J.-W., "Organ segmentation by comparing of gray value portion on abdominal CT image”, Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on , vol.2, no., pp.1201,1208 vol.2, 2000
  • Gu, L. ve Kaneko, T., “Organs extraction using three-dimensional mathematical morphology”, Signal Processing Proceedings, 1998. ICSP ’98. 1998 Fourth International Conference on, 1998.
  • Bae, K. T., Giger, M. L., Chen, C. T. ve Kahn, C. E., “Automatic segmentation of liver structure in CT images.” Medical physics, Cilt 20, No 1, 71–78, 1993.
  • Koss, J., Newman F., Johnson, T. ve Kirch, D., “Abdominal organ segmentation using texture transforms and a hopfield neural network”, Medical Imaging, IEEE Transactions on, Cilt 18, No 7, 640 –648, 1999.
  • Kurani, A., Xu, D., Furst, J. ve Raicu, D., “Co-occurrence matrices for volumetric data”, The Seventh IASTED International Conference on Computer Graphics and Imaging CGIM 2004, K. M. Hanson, Ed., 426–443, 2004,.
  • Ciurte A. ve Nedevschi, S., “Texture analysis within contrast enhanced abdominal ct images”, Intelligent Computer Communication and Processing, 2009. ICCP 2009. IEEE 5th International Conference on, 73 –78, 2009.
  • Selfridge, P., Judith, M., Prewitt, M., Dyer, C. ve Ranade S., “Segmentation algorithms for abdominal computerized tomography scans”, Computer Software and Applications Conference, Proc. IEEE Computer Society’s 3rd Intern., 1979.
  • Wu, J., Poehlman, S. , Noseworthy, M. ve Kamath, M., “Texture feature based automated seeded region growing in abdominal mri segmentation”, BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on, Cilt 2, 263 –267, 2008.
  • Campadelli, P., Casiraghi E. ve Pratissoli, S., “Fully automatic segmentation of abdominal organs from ct images using fast marching methods”, Computer-Based Medical Systems, CBMS, 21st IEEE International Symposium, pp. 554 –559, 2008. .
  • Jiang, H. ve Cheng, Q., “Automatic 3d segmentation of CT images based on active contour models”, Computer-Aided Design and Computer Graphics, 2009. CAD/Graphics ’09. 11th IEEE International Conference on, pp. 540 –543, 2009.
  • Siegel, E. L., Kolodner, R. M., (Eds.), Filmless Radiology, 1st ed., Springer, 1999.
  • Haacke, E. M., et al. "Magnetic resonance imaging." Physical principles and sequence design , 1999.
  • Lai, C.-C. ve Chang, C.-Y., “A hierarchical evolutionary algorithm for automatic medical image segmentation”, Expert Systems with Applications, Cilt 36, No 1, 248-259, 2009.
  • Husain, S.A., ve Shigeru, E., “Use of neural networks for feature based recognition of liver region on ct images”, Neural Networks for Signal Processing X, 2000. Proc. of the 2000 IEEE Signal Processing Society Workshop, Sydney, NSW 2000.
  • Chang, C.-Y. ve Chung, P.-C., “Medical image segmentation using a contextual-constraint-based hopfield neural cube”, Image and Vision Computing, Cilt 19, No 9-10, 669 – 678, 2001.
  • Lee, C.-C., Chung, P.-C. ve Tsai, H.-M., “Identifying multiple abdominal organs from ct image series using a multimodule contextual neural network and spatial fuzzy rules”, Information Tech. in Biomd, IEEE Trans. on, Cilt 7, No 3, 208 –217, 2003.
  • Chen, K. ve Chi, H., “A method of combining multiple probabilistic classifiers through soft competition on different feature sets”, Neurocomputing, Cilt 20, 227–252, 1998.
  • Kittler, J., Hatef, M., Duin, M. ve Matas, J., “On combining classifiers”, IEEE Trans. Pattern Anal. Mach. Intell., Cilt 20, No 3, 226 –239, 1998.
  • Ho, T. K., Hull J. J. ve Srihari S. N., “Decision combination in multiple classifier systems”, IEEE Trans. Pattern Anal. Mach. Intell., Cilt 16, 66–75, 1994.
  • Cao, J., Shridhar, M. ve Ahmadi, M., “Fusion of classifiers with fuzzy integrals”, in Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on, Cilt 1, 108–111 1995.
  • Chen, K., “On the use of different speech representations for speaker modeling”, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Trans. on, Cilt 35, No 3, 301–314, 2005.
  • Chen, K., Wang, L. ve Chi, H., “Methods of combining multiple classifiers with different features and their applications to text-independent speaker identification” Int. Jour. Pattern Recognition and Artificial Intelligence, Cilt 11, 417–445, 1997.
  • Xu, L., Krzyzak, A. ve Suen , C., “Methods of combining multiple classifiers and their applications to handwriting recognition”, Systems, Man and Cybernetics, IEEE Transactions on, Cilt 22, No 3, 418 –435, 1992.
  • Suen, C. Y., Legault, R., Nadal, C., Cheriet, M. ve Lam, L., “Building a new generation of handwriting recognition systems”, Pattern Recogn. Letters, Cilt 14, 303–315, 1993.
  • Huang, Y. S. ve Suen, C. Y., “A method of combining multiple experts for the recognition of unconstrained handwritten numerals”, IEEE Trans. Pattern Anal. Mach. Intell., Cilt 17, 90–94, January 1995.
  • Selver, M. A., Akay O., Alim F., Bardakçı, S. ve Ölmez, M., “An automated industrial conveyor belt system using image processing and hierarchical clustering for classifying marble slabs”, Robotics and Computer-Integrated Manufacturing, Cilt 27, No 1, 164 – 176, 2011.
  • Unser, M., ‘’Sum and difference histograms for texture classification,’’ IEEE Trans. Pattern Anal. Mach. Intell., Cilt PAMI-8, No 1, 118–125, 1986.
  • Selver, M. A., Kocaoğlu, A., Demir, G., Doğan, H., Dicle ve O., Güzeliş, C ., "Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation", Computers in Biology and Medicine, Cilt 38, No7, 765-784, 2008.
  • Selver, M.A., Kocaoglu, A., Akyar, H., Dicle, O., ve Guzelis, C., "Patient oriented neural networks to overcome challenges of abdominal organ segmentation in CT angiography studies”, ELECO, International Conf. on, pp.II-177-181, Nov 2009.
  • Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, 2. baskı, 1998.
  • Reed, R., Marks, R. J.: Neural Smithing: SupervisedLearning in Feed forward Artificial Neural Networks, MIT Press, 1999.
There are 55 citations in total.

Details

Primary Language Turkish
Journal Section Makaleler
Authors

Eşref Selvi This is me

M. Alper Selver

Ali Kavur This is me

Cüneyt Güzeliş This is me

Oğuz Dicle This is me

Publication Date September 30, 2015
Submission Date September 30, 2015
Published in Issue Year 2015 Volume: 30 Issue: 3

Cite

APA Selvi, E., Selver, M. A., Kavur, A., Güzeliş, C., et al. (2015). BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 30(3). https://doi.org/10.17341/gummfd.93803
AMA Selvi E, Selver MA, Kavur A, Güzeliş C, Dicle O. BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ. GUMMFD. October 2015;30(3). doi:10.17341/gummfd.93803
Chicago Selvi, Eşref, M. Alper Selver, Ali Kavur, Cüneyt Güzeliş, and Oğuz Dicle. “BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 30, no. 3 (October 2015). https://doi.org/10.17341/gummfd.93803.
EndNote Selvi E, Selver MA, Kavur A, Güzeliş C, Dicle O (October 1, 2015) BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 30 3
IEEE E. Selvi, M. A. Selver, A. Kavur, C. Güzeliş, and O. Dicle, “BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ”, GUMMFD, vol. 30, no. 3, 2015, doi: 10.17341/gummfd.93803.
ISNAD Selvi, Eşref et al. “BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 30/3 (October 2015). https://doi.org/10.17341/gummfd.93803.
JAMA Selvi E, Selver MA, Kavur A, Güzeliş C, Dicle O. BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ. GUMMFD. 2015;30. doi:10.17341/gummfd.93803.
MLA Selvi, Eşref et al. “BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 30, no. 3, 2015, doi:10.17341/gummfd.93803.
Vancouver Selvi E, Selver MA, Kavur A, Güzeliş C, Dicle O. BATIN BÖLGESİ ORGANLARININ MR GÖRÜNTÜLERİNDEN ÇOK AŞAMALI HİYERARŞİK SINIFLAMA İLE BÖLÜTLENMESİ. GUMMFD. 2015;30(3).