Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 20 Sayı: 4, 524 - 531, 30.12.2019
https://doi.org/10.18038/estubtda.581811

Öz

Kaynakça

  • [1] Manikandan MS, Dandapat S. Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review. Biomed Signal Proces 2014; 14: 73-107.
  • [2] Thanapatay D, Suwansaroj C, Tahanawattano C. ECG beat classification method for ECG printout with Principal Components Analysis and Support Vector Machines. In: IEEE 2010 International Conference on Electronics and Information Engineering; 1-3 August 2010; Kyoto, Japan. pp. 72-75.
  • [3] International Atomic Energy Agency. Nuclear Cardiology: Its Role in Cost Effective Care. Human Health Series No.18, IAEA, Vienna, 2012.
  • [4] Petretta M, Cuocolo R, Acampa W, Cuocolo A. Quantification of myocardial perfusion: SPECT,” Curr Cardiovasc Imaging Rep 2012; 5: 144-150.
  • [5] Lindahl D, Toft J, Hesse B, Palmer J, Ali S, Lundin A, Edenbrandt L. Scandinavian test of artificial neural network for classification of myocardial perfusion images. Clin Physiol 2000; 20: 253-261.
  • [6] Johansson L, Edenbrandt L, Nakajima K, Lomsky M, Svensson SE, Tragardh E. Computer-aided diagnosis system outperforms scoring analysis in myocardial perfusion imaging. J Nucl Cardiol 2014; 21: 416-423.
  • [7] Garcia EV, Klein JL, Taylor AT. Clinical decision support systems in myocardial perfusion imaging J Nucl Cardiol 2014; 21: 427-439.
  • [8] Slomka PJ, Nishina H, Berman DS, Kang X, Friedman JD, Hayes SW, Aladl UE, Germano G. Automated quantification of myocardial perfusion stress-rest change: a new measure of ischemia. J Nucl Med 2004; 45: 183-191.
  • [9] Ficaro EP, Kritzman JN, Corbett JR. Corridor4DM: The Michigan method for quantitative nuclear cardiology. J Nucl Cardiol 2007; 14: 455-465.
  • [10] Garcia EV, Faber TL, Cooke CD, Folks RD, Chen J, Santana C. The increasing role of quantification in clinical nuclear cardiology: The Emory approach. J Nucl Cardiol 2007; 14: 420-432.
  • [11] Germano G, Kavanagh PB, Slomka PJ, Van Kriekinge SD, Pollard G, Berman DS. Quantitation in gated perfusion SPECT imaging: The Cedars-Sinai approach. J Nucl Cardiol 2007; 14: 433-454.
  • [12] Liu YH. Quantification of nuclear cardiac images: The Yale approach. J Nucl Cardiol 2007; 14: 483-491.
  • [13] Guner LA, Karabacak NI, Cakir T, Akdemir OU, Kocaman SA, Cengel A, Unlu M. Comparison of diagnostic performances of three different software packages in detecting coronary artery disease. Eur J Nucl Med Mol Imaging 2010; 37: 2070-2078.
  • [14] Wolak A, Slomka PJ, Fish MB, Lorenzo S, Acampa W, Berman DS, Germano G. Quantitative myocardial-perfusion SPECT: Comparison of three state-of-the-art software packages. J Nucl Cardiol 2008; 15: 27-34.
  • [15] Guner LA, Karabacak NI, Akdemir OU, Karagoz PS, Kocaman SA, Cengel A, Unlu M. An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT. J Nucl Cardiol 2010; 17: 405-413.
  • [16] Arsanjani R, Xu Y, Dey D, Vahistha V, Shalev A, Nakanishi R, Hayes S, Fish M, Berman D, Germano G, et al. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population. J Nucl Cardiol 2013; 20: 553-562.
  • [17] Arsanjani R, Xu Y, Dey D, Fish M, Dorbala S, Hayes S, Berman D, Germano G, Slomka P. Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm. J Nucl Med 2013; 54: 549-555.
  • [18] Topal C, Akinlar C. Edge Drawing: A combined real-time edge and segment detector. J Vis Commun Image R 2012; 23: 862-872.
  • [19] Topal C, Akinlar C. Edpf: A real-time parameter-free edge segment detector with a false detection control. Int J Pattern Recogn 2012; 26: xx.
  • [20] Akinlar C, Topal C. EDLines: A real-time line segment detector with a false detection control. Pattern Recogn Lett 2011; 32: 1633-1642.
  • [21] Akinlar C, Topal C. EDCircles: A real-time circle detector with a false detection control. Pattern Recogn 2013; 46: 725-740.

SEGMENTATION OF 2D MYOCARDIAL PERFUSION SPECT IMAGES

Yıl 2019, Cilt: 20 Sayı: 4, 524 - 531, 30.12.2019
https://doi.org/10.18038/estubtda.581811

Öz



Myocardial perfusion imaging (MPI) is
a widely used and non-invasive diagnostic method for the detection of patients
with suspected or known ischemic heart disease. MPI test is commonly realized
by single photon emission computed tomography (SPECT). This test provides
several images illustrating the function of the heart muscle. Appropriate
segmentation of those images play a crucial role for the diagnosis of heart
disease. Consequently, this paper proposes a segmentation method for 2D myocardial
perfusion SPECT images acquired in both stress and rest cases. In this way, an
expert can make visual assessment of the changes in the stress and rest images
easily. Hence, possible heart diseases would be identified based on those
changes without a need of using polar maps or reference databases.

Kaynakça

  • [1] Manikandan MS, Dandapat S. Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review. Biomed Signal Proces 2014; 14: 73-107.
  • [2] Thanapatay D, Suwansaroj C, Tahanawattano C. ECG beat classification method for ECG printout with Principal Components Analysis and Support Vector Machines. In: IEEE 2010 International Conference on Electronics and Information Engineering; 1-3 August 2010; Kyoto, Japan. pp. 72-75.
  • [3] International Atomic Energy Agency. Nuclear Cardiology: Its Role in Cost Effective Care. Human Health Series No.18, IAEA, Vienna, 2012.
  • [4] Petretta M, Cuocolo R, Acampa W, Cuocolo A. Quantification of myocardial perfusion: SPECT,” Curr Cardiovasc Imaging Rep 2012; 5: 144-150.
  • [5] Lindahl D, Toft J, Hesse B, Palmer J, Ali S, Lundin A, Edenbrandt L. Scandinavian test of artificial neural network for classification of myocardial perfusion images. Clin Physiol 2000; 20: 253-261.
  • [6] Johansson L, Edenbrandt L, Nakajima K, Lomsky M, Svensson SE, Tragardh E. Computer-aided diagnosis system outperforms scoring analysis in myocardial perfusion imaging. J Nucl Cardiol 2014; 21: 416-423.
  • [7] Garcia EV, Klein JL, Taylor AT. Clinical decision support systems in myocardial perfusion imaging J Nucl Cardiol 2014; 21: 427-439.
  • [8] Slomka PJ, Nishina H, Berman DS, Kang X, Friedman JD, Hayes SW, Aladl UE, Germano G. Automated quantification of myocardial perfusion stress-rest change: a new measure of ischemia. J Nucl Med 2004; 45: 183-191.
  • [9] Ficaro EP, Kritzman JN, Corbett JR. Corridor4DM: The Michigan method for quantitative nuclear cardiology. J Nucl Cardiol 2007; 14: 455-465.
  • [10] Garcia EV, Faber TL, Cooke CD, Folks RD, Chen J, Santana C. The increasing role of quantification in clinical nuclear cardiology: The Emory approach. J Nucl Cardiol 2007; 14: 420-432.
  • [11] Germano G, Kavanagh PB, Slomka PJ, Van Kriekinge SD, Pollard G, Berman DS. Quantitation in gated perfusion SPECT imaging: The Cedars-Sinai approach. J Nucl Cardiol 2007; 14: 433-454.
  • [12] Liu YH. Quantification of nuclear cardiac images: The Yale approach. J Nucl Cardiol 2007; 14: 483-491.
  • [13] Guner LA, Karabacak NI, Cakir T, Akdemir OU, Kocaman SA, Cengel A, Unlu M. Comparison of diagnostic performances of three different software packages in detecting coronary artery disease. Eur J Nucl Med Mol Imaging 2010; 37: 2070-2078.
  • [14] Wolak A, Slomka PJ, Fish MB, Lorenzo S, Acampa W, Berman DS, Germano G. Quantitative myocardial-perfusion SPECT: Comparison of three state-of-the-art software packages. J Nucl Cardiol 2008; 15: 27-34.
  • [15] Guner LA, Karabacak NI, Akdemir OU, Karagoz PS, Kocaman SA, Cengel A, Unlu M. An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT. J Nucl Cardiol 2010; 17: 405-413.
  • [16] Arsanjani R, Xu Y, Dey D, Vahistha V, Shalev A, Nakanishi R, Hayes S, Fish M, Berman D, Germano G, et al. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population. J Nucl Cardiol 2013; 20: 553-562.
  • [17] Arsanjani R, Xu Y, Dey D, Fish M, Dorbala S, Hayes S, Berman D, Germano G, Slomka P. Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm. J Nucl Med 2013; 54: 549-555.
  • [18] Topal C, Akinlar C. Edge Drawing: A combined real-time edge and segment detector. J Vis Commun Image R 2012; 23: 862-872.
  • [19] Topal C, Akinlar C. Edpf: A real-time parameter-free edge segment detector with a false detection control. Int J Pattern Recogn 2012; 26: xx.
  • [20] Akinlar C, Topal C. EDLines: A real-time line segment detector with a false detection control. Pattern Recogn Lett 2011; 32: 1633-1642.
  • [21] Akinlar C, Topal C. EDCircles: A real-time circle detector with a false detection control. Pattern Recogn 2013; 46: 725-740.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Selcan Kaplan Berkaya 0000-0001-6728-4050

Gulnihal Hale Kurt Bu kişi benim 0000-0001-8246-6960

İlknur Ak Sıvrıkoz 0000-0002-5133-9931

Serkan Gunal Bu kişi benim 0000-0002-9691-1575

Yayımlanma Tarihi 30 Aralık 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 20 Sayı: 4

Kaynak Göster

AMA Kaplan Berkaya S, Kurt GH, Ak Sıvrıkoz İ, Gunal S. SEGMENTATION OF 2D MYOCARDIAL PERFUSION SPECT IMAGES. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. Aralık 2019;20(4):524-531. doi:10.18038/estubtda.581811