A web-accessible content-based cervicographic image retrieval system

Abstract

Content-based image retrieval (CBIR) is the process of retrieving images by directly using image visual characteristics. In this paper, we present a prototype system implemented for CBIR for a uterine cervix image (cervigram) database. This cervigram database is a part of data collected in a multi-year longitudinal effort by the National Cancer Institute (NCI), and archived by the National Library of Medicine (NLM), for the study of the origins of, and factors related to, cervical precancer/cancer. Users may access the system with any Web browser. The system is built with a distributed architecture which is modular and expandable; the user interface is decoupled from the core indexing and retrieving algorithms, and uses open communication standards and open source software. The system tries to bridge the gap between a user's semantic understanding and image feature representation, by incorporating the user's knowledge. Given a user-specified query region, the system returns the most similar regions from the database, with respect to attributes of color, texture, and size. Experimental evaluation of the retrieval performance of the system on "groundtruth" test data illustrates its feasibility to serve as a possible research tool to aid the study of the visual characteristics of cervical neoplasia.

A system for searching uterine cervix images by visual attributes

“…CervigramFinder incorporates fundamental CBIR functions, such as feature extraction, feature normalization, feature combination, feature dimension reduction, and similarity measures [9]. We did an extensive study on evaluating and identifying key techniques among those found in the technical literature by comparing their performance for the retrieval of cervigrams.…”

Cervicographic image retrieval by spatial similarity of lesions

“…CervigramFinder is a Web-accessible contentbased image retrieval (CBIR) prototype system for searching cervigrams by their visual attributes [3]. It operates on an expert-annotated subset of the NCI cervigram data for which the lesion regions-of-interest (ROI) have been manually pre-marked and labeled by NCI medical experts [4].…”

“…• Normalized center of the region (3) where, N is the total number of pixels in the region and , are the angle and range, respectively, associated with point i.…”

Analysis of Similarity Measurements in CBIR Using Clustered Tamura Features for Biomedical Images

“…For this purpose a content based image retrieval (CBIR) system could be used [11,14,19], both to benefit the management of increasingly large image collections, and to support clinical care, biomedical research, and education. However, although the number of experimental algorithms comprehending specific problems and databases is growing, few systems exist with relative success [3,7,29]. So, biomedical applications are one of the priority areas where CBIR can meet more success outside the experimental sphere, due to population aging in developed countries.…”