DocumentCode :
1361860
Title :
A knowledge discovery approach to diagnosing myocardial perfusion
Author :
Cios, Krzyrztof J. ; Teresinska, Anna ; Konieczna, Stefania ; Potocka, Joanna ; Sharma, Sunil
Author_Institution :
Toledo Univ., OH, USA
Volume :
19
Issue :
4
fYear :
2000
Firstpage :
17
Lastpage :
25
Abstract :
Discusses applying a six-step discovery process to a database of SPECT bull\´s-eye maps of the heart. Visual assessment of clinical diagnostic images is observer-dependent. Thus, much effort is expended to computerize the process of diagnosis so it is less dependent on the observer, especially when the observer is not experienced. A large number of images to be evaluated (as in SPECT myocardial perfusion studies: approximately 15 oblique "slices," 15 oblique/sagittal, and 15 oblique/coronal, both in stress and rest, which comes to nearly 100 2-D images per patient) forced the creation of more "comprehensive" images; namely, the bull\´s-eye perfusion maps. Using these maps, the authors showed that it is possible to differentiate the patients with coronary artery disease (one- or two-vessel) from the patients with low probability of the disease (normals). In the future, features other than those used in this work will be used; for instance, a feature representing the area of "abnormal" myocardium, available in most previously mentioned algorithms for "normative" evaluation of bull\´s-eye maps. In the course of this work, the authors also came up with methods that can accurately extract the ROIs from an image where a thresholding method cannot be used.
Keywords :
cardiology; data mining; feature extraction; haemorheology; medical image processing; muscle; single photon emission computed tomography; visual databases; SPECT myocardial perfusion studies; diagnostic nuclear medicine; image ROIs extraction; knowledge discovery approach; medical diagnostic imaging; myocardial perfusion diagnosis; oblique slices; oblique/coronal slices; oblique/sagittal slices; rest images; stress images; thresholding method; Biomedical imaging; Computed tomography; Data mining; Electrocardiography; Heart; Magnetic resonance imaging; Medical diagnostic imaging; Myocardium; Positron emission tomography; Spatial databases; Artificial Intelligence; Biomedical Engineering; Case-Control Studies; Coronary Disease; Databases, Factual; Female; Heart; Humans; Image Processing, Computer-Assisted; Male; Signal Processing, Computer-Assisted; Tomography, Emission-Computed, Single-Photon;
fLanguage :
English
Journal_Title :
Engineering in Medicine and Biology Magazine, IEEE
Publisher :
ieee
ISSN :
0739-5175
Type :
jour
DOI :
10.1109/51.853478
Filename :
853478
Link To Document :
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