Integrating an automatic classification method into the medical image retrieval process.
Combining low-level features that represent the content of medical images with high level features that are saved with images would allow the expansion of text queries submitted to Content Based Image Retrieval (CBIR) systems. Expanding these text queries would allow CBIR systems to respond more effectively to specific queries when retrieving medical images. We hypothesized that adding an automatic classification method to the current retrieval process would help improve the performance of the University at Buffalo Medical Text and Images Retrieval System (UBMedTIRS). This paper illustrates the results of our approach and its implications for expanding query statements in medical image information retrieval (IR) systems.
Uwimana
Epaphrodite
E
State University of New York at Buffalo, NY, USA.
Ruiz
Miguel E
ME
D000465:Algorithms; D001185:Artificial Intelligence; D004282:Documentation / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008487:Medical History Taking / Q000379:methods*; D009323:Natural Language Processing; D009518:New York; D010363:Pattern Recognition, Automated / Q000379:methods*; D011873:Radiology Information Systems / Q000458:organization & administration*; D013358:Subject Headings; D014584:User-Computer Interface