A Framework for Medical Image Retrieval Using Machine Learning and Statistical Similarity Matching Techniques With Relevance Feedback

Abstract

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.

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

“…The best reported retrieval result in [10] on a dataset of 1,501 radiological images of 17 classes was 0.62, 0.67, and 0.66 for Pr(Re=Pr), Pr(Re=0.5), and Pr− Re area, respectively (these values have been approximately calculated from precision-recall curve in [10]). The best performance of the presented CBIR system in [20] based on a database consisting of 5,000 images of 20 classes was 0.68, 0.82, and 0.72 for Pr(Re=Pr), Pr(Re=0.5), and Pr−Re area, respectively (these values have been approximately calculated from precision-recall curve in [20]). 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 performance of this framework was evaluated based on four criteria, Pr (20), Pr(Re=Pr), Pr(Re=0.5), and Pr−Re area where the last three criteria were extracted from precision-recall curve. Table 3 presents the retrieval results of our proposed frameworks based on these four criteria for different values of the score threshold.…”

“…Table 3 presents the retrieval results of our proposed frameworks based on these four criteria for different values of the score threshold. In Table 3, the best retrieval results in our framework are 0.95, 0.97, 0.97, and 0.96 for Pr (20), Pr(Re= Pr), Pr(Re=0.5), and Pr−Re area, respectively. To evaluate results of our work, we obtain a comparison between our proposed framework and other presented retrieval techniques in the literature.…”

“…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.…”

“…In medical image application, Rahman et al [20] introduced a CBMIR system using a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean clustering for categorization and updating the parameter of the query class based on the positive feedback images. Pourghasssem and Daneshvar [21] proposed a framework for the image retrieval of a large-scale database of medical X-ray images.…”