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
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).