DocumentCode
2967094
Title
Automatic Assessment of Cardiac Artery Disease by Using DCAD Module
Author
Khalilzad-Sharghi, V. ; Talebpour, A. ; Kamali-Asl, A. ; Hendijani, N.
Author_Institution
Dept. of Med. Radiat. Eng., Shahid Beheshti Univ., Tehran, Iran
fYear
2008
fDate
26-29 Sept. 2008
Firstpage
243
Lastpage
246
Abstract
In patients with cardiac artery disease myocardial perfusion scan, which is a non-invasive method, is utilized. This study is conducted to achieve an advantageous software applicable to quantitative myocardial SPECT perfusion. Each cross-section of the left ventricle is segmented by applying fuzzy clustering method. After obtaining the myocardial skeleton of the left ventricle from its short axis cross sections, we make use of fuzzy logic to decide whether the pixel belongs to myocardial muscle and any perfusion perturbation or not. Abnormal critical conditions in rest and stress studies and coronary artery disease diagnosis were investigated in a set of about 200 images. Measurement and allocation of different myocardial sectors to specific coronary arteries were accomplished by utilizing collected information about respectively 75 patient men and 62 patient women, and the validity of artery obstruction diagnosis has been proved in 40 patients having been under the coronary angiography. We named the achieved software DCAD(b) which has demonstrated a considerably good performance in coronary artery occlusion diagnosis and would be a promising method aiding nuclear medicine specialists in diagnosing these defections.
Keywords
angiocardiography; blood vessels; diseases; fuzzy logic; fuzzy set theory; image segmentation; medical image processing; muscle; pattern clustering; radioisotope imaging; single photon emission computed tomography; DCAD module; artery obstruction diagnosis; automatic cardiac artery disease assessment; cardiac artery disease patients; coronary angiography; coronary artery disease diagnosis; coronary artery occlusion diagnosis; defect diagnosis; fuzzy clustering; fuzzy logic; left ventricle segmentation; myocardial muscle; myocardial perfusion scan; myocardial skeleton; noninvasive method; nuclear medicine; perfusion perturbation; quantitative myocardial SPECT perfusion; Arteries; Cardiac disease; Cardiovascular diseases; Clustering methods; Coronary arteriosclerosis; Fuzzy logic; Muscles; Myocardium; Skeleton; Stress; Cardiac Artery Disease; Image Processing; Image Skeleton; SPECT;
fLanguage
English
Publisher
ieee
Conference_Titel
Symbolic and Numeric Algorithms for Scientific Computing, 2008. SYNASC '08. 10th International Symposium on
Conference_Location
Timisoara
Print_ISBN
978-0-7695-3523-4
Type
conf
DOI
10.1109/SYNASC.2008.57
Filename
5204818
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