Halima Dziri*, Mohamed Ali Cherni and Dorra Ben-Sellem Pages 623 - 633 ( 11 )
Background: In this paper, we propose a new efficient method of radionuclide ventriculography image segmentation to estimate the left ventricular ejection fraction. This parameter is an important prognostic factor for diagnosing abnormal cardiac function.
Methods: The proposed method combines the Chan-Vese and the mathematical morphology algorithms. It was applied to diastolic and systolic images obtained from the Nuclear Medicine Department of Salah AZAIEZ Institute. In order to validate our proposed method, we compare the obtained results to those of two methods present in the literature. The first one is based on mathematical morphology, while the second one uses the basic Chan-Vese algorithm. To evaluate the quality of segmentation, we compute accuracy, positive predictive value and area under the ROC curve. We also compare the left ventricle ejection fraction estimated by our method to that of the reference given by the software of the gamma-camera and validated by the expert, using Pearson’s correlation coefficient, ANOVA test and linear regression.
Results: Static results show that the proposed method is very efficient for the detection of the left ventricle. The accuracy was 98.60%, higher than that of the other two methods (95.52% and 98.50%).
Conclusion: Likewise, the positive predictive value was the highest (86.40% vs. 83.63% 71.82%). The area under the ROC curve was also the most important (0.998% vs. 0.926% 0.919%). On the other hand, Pearson's correlation coefficient was the highest (99% vs. 98% 37%). The correlation was significantly positive (p<0.001).
Image segmentation, systolic image, diastolic image, left ventricle ejection fraction, radionuclide ventriculography, cardiac function.
Universite de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Medicales (LRBTM), Tunis, Universite de Tunis, LR13 ES03 SIME, ENSIT, Montfleury 1008, Universite de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Medicales (LRBTM), Tunis