A survey of image registration techniques
ACM Computing Surveys (CSUR)
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Case-based reasoning in the health sciences: What's next?
Artificial Intelligence in Medicine
Case-based object recognition for airborne fungi recognition
Artificial Intelligence in Medicine
An ensemble of case-based classifiers for high-dimensional biological domains
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Paper: On using feedforward neural networks for clinical diagnostic tasks
Artificial Intelligence in Medicine
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
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Objective: This study compared the diagnostic accuracy of different approaches of case-based reasoning (CBR) for the assessment of coronary artery disease (CAD) using thallium-201 myocardial perfusion scintigraphy in comparison with coronary angiography. Methods and material: For each scintigraphic image set, regional myocardial tracer uptake was obtained by polar map analysis. CBR algorithms based on a similarity measure were employed to identify similar scintigraphic images within the case library, where each case contained the scintigraphic data together with results of coronary angiography. The angiographic data of retrieved cases were then used to determine whether significant CAD was present in one of the major coronary arteries. Three different approaches of CBR were compared: (1) case retrieval based on a global comparison of polar map data (GLOB), (2) case retrieval based on a territorial comparison of polar map data (TER), and (3) case retrieval based on a comparison of a given case with eight sub-libraries classified according to the involvement of the three major coronary vessels using a group similarity measure (GROUP). Two matching algorithms the best-match approach and an adapted retrieving approach were combined with all three case retrieval methods and their influence on the diagnostic accuracy were investigated. Results: For overall detection of significant CAD, the best-match approach of both TER and GROUP retrieval methods showed a higher diagnostic accuracy than the GLOB retrieval method (75% and 77% versus 70%, respectively). ROC analysis for the adapted retrieving approach showed a similar diagnostic accuracy for all three methods with an area under the curve of 0.79, 0.8, and 0.8 for GLOB, TER, and GROUP, respectively. Conclusion: The observed improvement in the diagnostic accuracy by the new approaches may lead to further improvements of CBR systems, which have the potential to offer valuable decision support for human readers, especially for less experienced investigators.