A Learning-Based Similarity Fusion and Filtering Approach for Biomedical Image Retrieval Using SVM Classification and Relevance Feedback

Abstract

This paper presents a classification-driven biomedical image retrieval framework based on image filtering and similarity fusion by employing supervised learning techniques. In this framework, the probabilistic outputs of a multiclass support vector machine (SVM) classifier as category prediction of query and database images are exploited at first to filter out irrelevant images, thereby reducing the search space for similarity matching. Images are classified at a global level according to their modalities based on different low-level, concept, and keypoint-based features. It is difficult to find a unique feature to compare images effectively for all types of queries. Hence, a query-specific adaptive linear combination of similarity matching approach is proposed by relying on the image classification and feedback information from users. Based on the prediction of a query image category, individual precomputed weights of different features are adjusted online. The prediction of the classifier may be inaccurate in some cases and a user might have a different semantic interpretation about retrieved images. Hence, the weights are finally determined by considering both precision and rank order information of each individual feature representation by considering top retrieved relevant images as judged by the users. As a result, the system can adapt itself to individual searches to produce query-specific results. Experiment is performed in a diverse collection of 5 000 biomedical images of different modalities, body parts, and orientations. It demonstrates the efficiency (about half computation time compared to search on entire collection) and effectiveness (about 10%-15% improvement in precision at each recall level) of the retrieval approach.

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

“…The best performance of the presented iterative classification-based image retrieval framework in [21] based on dataset of ImageCLEF 2005 consisting of 10,000 medical X-ray images of 57 classes (9,000 images as training dataset and 1,000 images as test dataset) was 0.915, 0.88, 0.71, and 0.67 for Pr (20), Pr(Re= Pr), Pr(Re=0.5), and Pr−Re area, respectively. The best performance of the proposed CBIR system in [22] based on a database consisting of 5,000 images of 30 categories was 0.52, 0.53, and 0.50 for Pr(Re=Pr), Pr(Re=0.5), and Pr−Re area, respectively (these values have been approximately calculated from precision-recall curve in [22]). The best performance of the proposed CBIR system in [23] based on a database consisting of 2,785 images of 15 categories was 0.77, 0.78, and 0.76 for Pr(Re=Pr), Pr(Re=0.5), and Pr−Re area, respectively (these values have been approximately calculated from precision-recall curve in [23]).…”

“…Due to involve the users' intention in the retrieval procedure, many algorithms have been presented to integrate relevance feedback (RF) to retrieval algorithm in the literature [15][16][17][18][19][20][21][22][23]. The query refinement strategies are one of the most commonly used methods which modifies the query image in the RF phase.…”

“…Also, this system proposed an RF mechanism using a synthetic distance measure based on the weighted Euclidean distance measure and GMM-based dependency probability similarity measure. In the study of Rahman et al [22], a learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM has been introduced. This system applied a RFbased similarity fusion technique which feature weights are updated at each iteration by considering both the precision and rank order information of relevant images.…”

Automatic Detection of Biomedical Compound Figure using Bag of Words

“…Among other classifiers, SVMs have shown a better generalization performance in medical domain compared with other classification techniques. [9][10][11][19][20] …”

An Expert Local Mesh Correlation Histograms for Biomedical Image Indexing and Retrieval

“…Cai et al [2] have proposed a prototype design which supports an efficient content-based retrieval based on physiological kinetic features and reduces image storage requirements. Rahman et al [20] have proposed a classification-driven biomedical image retrieval framework based on image filtering and similarity fusion by employing supervised learning techniques. Yang et al [32] have proposed a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities.…”