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Çoklu Çözünürlük Analiz Yöntemleri Kullanılarak BT ve MR Karaciğer Görüntülerinin Füzyonu

Yıl 2021, Sayı: 30, 56 - 61, 15.12.2021
https://doi.org/10.31590/ejosat.1005858

Öz

Bilgisayarlı Tomografi (BT) ve Manyetik Rezonans (MR) teknikleri gibi çeşitli tıbbi görüntüleme teknikleri mevcuttur. Her iki teknik de görüntülenecek bölgenin kompleks özelliklerini vermektedir. Bu çalışma, tıbbi teşhis amacıyla mümkün olduğunca ayrıntılı görüntüler elde etmek için BT ve MR karaciğer görüntülerini birleştirmek için Çoklu Çözünürlük Analizi (ÇÇA) yöntemlerini kullanan bir yaklaşım önermektedir. Görüntülere ÇÇA yöntemleri uygulanarak dönüşüm katsayıları elde edilir. Bu dönüşüm katsayılarına 3 farklı füzyon kuralı uygulanarak görüntüler birleştirilir. Birleştirilmiş görüntüleri değerlendirmek için Tepe sinyal-gürültü oranı (PSNR), Yapısal benzerlik endeksi Ölçümü (SSIM) ve Ortalama Kare Hata (MSE) değerleri hesaplanmıştır. Kullanılan ÇÇA yöntemleri karşılaştırıldığında, en iyi sonuç kompleks-değerli curvelet dönüşümü kullanılarak elde edilmiştir.

Kaynakça

  • Ali, F. E., El-Dokany, I. M., Saad, A. A., & Abd El-Samie, F. E. S. (2008). Curvelet fusion of MR and CT images. Progress in Electromagnetics Research, 3, 215-224.
  • Ali, F. E., El-Dokany, I. M., Saad, A. A., & Abd El-Samie, F. E. (2010). A curvelet transform approach for the fusion of MR and CT images. Journal of Modern Optics, 57(4), 273-286.
  • Alzubi, S., Sharif, S., Islam, N. and Abbod, M. (2011). Multi-resolution analysis using curvelet and wavelet transforms for medical imaging, IEEE International Workshop on Medical Measurements and Applications Proceedings, Bari-Italy, 188-191.
  • Bhateja, V., Krishn, A., Patel, H., & Sahu, A. (2015). Medical image fusion in wavelet and ridgelet domains: A comparative evaluation. International Journal of Rough Sets and Data Analysis (IJRSDA), 2(2), 78-91.
  • Candes, E. and Donoho, D.L. (1999). Ridgelets: the key to high-dimensional intermittency, Philosophical Transactions of the Royal Society of London, 357 (1760), 2495-2509.
  • Candes, E. J., & Donoho, D. L. (2004). New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities, Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, 57(2), 219-266.
  • Candes, E.J., Demanet, L., Donoho, D.L. and Ying, L. (2006). Fast discrete curvelet transforms, Multiscale Modeling and Simulation, 5 (3), 861-899.
  • Chen, Y., Niu, K., Zeng, Z., & Pan, Y. (2020). A wavelet based deep learning method for underwater image super resolution reconstruction, IEEE Access, 8, 117759-117769.
  • Cihan, M. and Ceylan, M. (2021). Fusion and CNN Based Classification of Liver Focal Lesions Using Magnetic Resonance Imaging Phases, Sigma Journal of Engineering and Natural Sciences, accepted.
  • Fadili, J. and Starck, J. L. (2009). Curvelets and ridgelets, R.A. Meyers, ed. Encyclopedia of Complexity and Systems Science, Springer New York, 1718-1738.
  • Mojsilovic, A., Popovic, M. and Sevic, D. (1996). Classification of the ultrasound liver images with the 2N/spl times/1-D wavelet transform, In Proceedings of 3rd IEEE International Conference on Image Processing, 1, 367-370.
  • Morlet, J., Arehs, G., Forugeau, I. and Giard, D. (1982). Wave Propogation and Sampling Theory, Geophysics, 47 (2), 203-236.
  • Öztürk, A. E. and Ceylan, M. (2015). Fusion and ANN based classification of liver focal lesions using phases in magnetic resonance imaging, In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 415-419.
  • Öztürk, A.E., Ceylan, M. and Kıvrak, A.S. (2014). A new approach for liver classification using ridgelet / ripplet-II transforms, feature groups and ANN, 6th European Conference of the International Federation for Medical and Biological Engineering-MBEC 2014, Dubrovnik-Crotia, 130-133.
  • Pajares, G., & De La Cruz, J. M. (2004). A wavelet-based image fusion tutorial. Pattern recognition, 37(9), 1855-1872.

Fusion of CT and MR Liver Images Using Multiresolution Analysis Methods

Yıl 2021, Sayı: 30, 56 - 61, 15.12.2021
https://doi.org/10.31590/ejosat.1005858

Öz

There are various medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance (MR) techniques. Both techniques give complex features of the region to be imaged. This study proposes an approach that uses Multiresolution Analysis (MRA) methods to fuse CT and MR liver images to obtain as detailed images as possible for medical diagnostic purposes. The transform coefficients are obtained by applying MRA methods to the images. Images are combined by applying 3 different fusion rules to these transform coefficients. Peak Signal to Noise Rate (PSNR), Structural Similarity Index Measure (SSIM) and Mean Square Error (MSE) values are calculated to evaluate the fused images. When comparing the methods, the best result was obtained using complex-valued curvelet transform.

Kaynakça

  • Ali, F. E., El-Dokany, I. M., Saad, A. A., & Abd El-Samie, F. E. S. (2008). Curvelet fusion of MR and CT images. Progress in Electromagnetics Research, 3, 215-224.
  • Ali, F. E., El-Dokany, I. M., Saad, A. A., & Abd El-Samie, F. E. (2010). A curvelet transform approach for the fusion of MR and CT images. Journal of Modern Optics, 57(4), 273-286.
  • Alzubi, S., Sharif, S., Islam, N. and Abbod, M. (2011). Multi-resolution analysis using curvelet and wavelet transforms for medical imaging, IEEE International Workshop on Medical Measurements and Applications Proceedings, Bari-Italy, 188-191.
  • Bhateja, V., Krishn, A., Patel, H., & Sahu, A. (2015). Medical image fusion in wavelet and ridgelet domains: A comparative evaluation. International Journal of Rough Sets and Data Analysis (IJRSDA), 2(2), 78-91.
  • Candes, E. and Donoho, D.L. (1999). Ridgelets: the key to high-dimensional intermittency, Philosophical Transactions of the Royal Society of London, 357 (1760), 2495-2509.
  • Candes, E. J., & Donoho, D. L. (2004). New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities, Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, 57(2), 219-266.
  • Candes, E.J., Demanet, L., Donoho, D.L. and Ying, L. (2006). Fast discrete curvelet transforms, Multiscale Modeling and Simulation, 5 (3), 861-899.
  • Chen, Y., Niu, K., Zeng, Z., & Pan, Y. (2020). A wavelet based deep learning method for underwater image super resolution reconstruction, IEEE Access, 8, 117759-117769.
  • Cihan, M. and Ceylan, M. (2021). Fusion and CNN Based Classification of Liver Focal Lesions Using Magnetic Resonance Imaging Phases, Sigma Journal of Engineering and Natural Sciences, accepted.
  • Fadili, J. and Starck, J. L. (2009). Curvelets and ridgelets, R.A. Meyers, ed. Encyclopedia of Complexity and Systems Science, Springer New York, 1718-1738.
  • Mojsilovic, A., Popovic, M. and Sevic, D. (1996). Classification of the ultrasound liver images with the 2N/spl times/1-D wavelet transform, In Proceedings of 3rd IEEE International Conference on Image Processing, 1, 367-370.
  • Morlet, J., Arehs, G., Forugeau, I. and Giard, D. (1982). Wave Propogation and Sampling Theory, Geophysics, 47 (2), 203-236.
  • Öztürk, A. E. and Ceylan, M. (2015). Fusion and ANN based classification of liver focal lesions using phases in magnetic resonance imaging, In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 415-419.
  • Öztürk, A.E., Ceylan, M. and Kıvrak, A.S. (2014). A new approach for liver classification using ridgelet / ripplet-II transforms, feature groups and ANN, 6th European Conference of the International Federation for Medical and Biological Engineering-MBEC 2014, Dubrovnik-Crotia, 130-133.
  • Pajares, G., & De La Cruz, J. M. (2004). A wavelet-based image fusion tutorial. Pattern recognition, 37(9), 1855-1872.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Mücahit Cihan 0000-0002-1426-319X

Murat Ceylan 0000-0001-6503-9668

Yayımlanma Tarihi 15 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 30

Kaynak Göster

APA Cihan, M., & Ceylan, M. (2021). Fusion of CT and MR Liver Images Using Multiresolution Analysis Methods. Avrupa Bilim Ve Teknoloji Dergisi(30), 56-61. https://doi.org/10.31590/ejosat.1005858

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