Detection of Cardiac Artery Disease by Using the DCAD (b) Module

Abstract

Introduction: In patients with cardiac artery disease, a myocardial perfusion scan, which is a non-invasive method, is utilized. This study is conducted to develop an advantageous software applicable to quantitative myocardial SPECT perfusion. Material and Methods: Each cross-section of the left ventricle was segmented by applying a fuzzy clustering method. After obtaining the myocardial skeleton of the left ventricle from its short axis cross sections, we made use of fuzzy logic to decide whether the pixel belongs to the myocardial muscle and any perfusion perturbation or not. The reconstructed image was divided into 18 equivolume sectors. The features were extracted in each sector and, finally, were compared with a normal data bank. Results: Abnormal critical conditions in rest and stress studies and coronary artery disease diagnosis were investigated in a set of about 317 images. Measurement and allocation of different myocardial sectors to specific coronary arteries were accomplished by utilizing collected information about the patients (75 men and 62 women), and the validity of the artery obstruction diagnosis has been proven in 40 patients undergoing coronary angiography. Conclusion: Our developed software DCAD (b) has demonstrated a considerably good performance in the diagnosis of coronary artery occlusion and can be a promising method aiding nuclear medicine specialists in their diagnosis.

Keywords

• Cardiac Artery Disease
• Fuzzy Method
• FCM
• Image Processing
• Image Skeleton
• SPECT