Abstrait

Characterization of myocardium anomalies using watershed-based segmentation approaches in nuclear cardiology

Yousif Abdallah*, Galaleldin Ibrahim, Nouf Abuhadi, Ghazali M. Abdel Gadir & Elabbas Ebaid

Background: Nuclear cardiology can detect both ischemia and inflammation of the heart. It's difficult to distinguish adjacent tissues in a cardiac scintography image.

Objectives: The aim of the study is to characterize of myocardium anomalies using watershed-based segmentation approaches in nuclear cardiology. The researchers seek to detect heart tissue in nuclear medicine pictures by using watershed methods. The contrast is blurred, and the presence of fleck noise complicates interpretation.

Methods: Thus, color-based image processing can considerably boost the rate of cardiac detection in digital image processing. This study employed color-based k-means clustering. Color space conversion was carried out using scintographs. Following that, using color analysis tools, the image was segmented.

Results: On exhibit was an altogether new and crystal-clear rendition of the segmented scintograph. The proposed method precisely defines the cardiac tissues and their borders. We calculated both the accuracy rate and the recall reckoning. 98.9+9.01 (p>0.05) and 0.07+0.004 (p>0.05) were the results.

Conclusion: The proposed approach is used to identify cardiac tissue precisely.