Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results.
OBJECTIVE
To develop a system to facilitate the retrieval of radiologic images that contain similar-appearing lesions and to perform a preliminary evaluation of this system with a database of computed tomographic (CT) images of the liver and an external standard of image similarity.


METHODS
Institutional review board approval was obtained for retrospective analysis of deidentified patient images. Thereafter, 30 portal venous phase CT images of the liver exhibiting one of three types of liver lesions (13 cysts, seven hemangiomas, 10 metastases) were selected. A radiologist used a controlled lexicon and a tool developed for complete and standardized description of lesions to identify and annotate each lesion with semantic features. In addition, this software automatically computed image features on the basis of image texture and boundary sharpness. Semantic and computer-generated features were weighted and combined into a feature vector representing each image. An independent reference standard was created for pairwise image similarity. This was used in a leave-one-out cross-validation to train weights that optimized the rankings of images in the database in terms of similarity to query images. Performance was evaluated by using precision-recall curves and normalized discounted cumulative gain (NDCG), a common measure for the usefulness of information retrieval.


RESULTS
When used individually, groups of semantic, texture, and boundary features resulted in various levels of performance in retrieving relevant lesions. However, combining all features produced the best overall results. Mean precision was greater than 90% at all values of recall, and mean, best, and worst case retrieval accuracy was greater than 95%, 100%, and greater than 78%, respectively, with NDCG.


CONCLUSIONS
Preliminary assessment of this approach shows excellent retrieval results for three types of liver lesions visible on portal venous CT images, warranting continued development and validation in a larger and more comprehensive database.
Napel
Sandy A
SA
Department of Radiology, Stanford University School of Medicine, James H. Clark Center S323, Stanford, CA 94305-5450, USA. snapel@stanford.edu
Beaulieu
Christopher F
CF
Rodriguez
Cesar
C
Cui
Jingyu
J
Xu
Jiajing
J
Gupta
Ankit
A
Korenblum
Daniel
D
Greenspan
Hayit
H
Ma
Yongjun
Y
Rubin
Daniel L
DL
D001331:Automation; D003560:Cysts / Q000000981:diagnostic imaging; D006391:Hemangioma / Q000000981:diagnostic imaging; D006801:Humans; D008107:Liver Diseases / Q000000981:diagnostic imaging*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging / Q000556:secondary; D011169:Portal Vein / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012015:Reference Standards; D012189:Retrospective Studies; D012984:Software; D009626:Terminology as Topic; D014057:Tomography, X-Ray Computed