Title :
CBIR for medical images - an evaluation trial
Author :
Marchiori, A. ; Brodley, C. ; Dy, J. ; Pavlopoulou, C. ; Kak, A. ; Broderick, L. ; Aisen, A.M.
Author_Institution :
Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
Abstract :
Content-based image retrieval (CBIR) has the potential to provide medical doctors with a powerful resource to help make accurate diagnoses. To aid in diagnosis, a CBIR system must retrieve similar images from the same (unknown) disease class as the patient. We have implemented a CBIR system that first predicts the disease class of the query image and then retrieves the n images nearest to the query image from the pool of images with the predicted disease class. With the cooperation of residents/radiologists at Indiana University Medical Center and the Department of Radiology at the University of Wisconsin we have recently completed an evaluation of our system. The results show that when using our system, the diagnostic accuracy of the group increased on average by 32% over diagnosis without any reference materials
Keywords :
content-based retrieval; image retrieval; medical image processing; visual databases; CBIR system; accurate diagnoses; content-based image retrieval; customized queries; diagnostic accuracy; disease class; medical CBIR; medical doctors; medical images; patient; predicted disease class; query image; similar image retrieval; unknown disease class; Biomedical imaging; Diseases; Image databases; Image retrieval; Information retrieval; Lungs; Medical diagnostic imaging; Pathology; Radiology; Spatial databases;
Conference_Titel :
Content-Based Access of Image and Video Libraries, 2001. (CBAIVL 2001). IEEE Workshop on
Conference_Location :
Kauai, HI
Print_ISBN :
0-7695-1354-9
DOI :
10.1109/IVL.2001.990861
