Marcelo Ponciano-Silva

Including the perceptual parameter to tune the retrieval ability of pulmonary CBIR systems

The research on Content-Based Image Retrieval (CBIR) is growing in relevance at a fast pace. Algorithms and tools for CBIR can help decision-making processes, for example allowing the specialist to retrieve cases similar to the one under evaluation. However, the main reservation about using CBIR is the semantic gap, which is the divergence among automatic results and what the user is expecting. We propose the “perceptual parameter”, which allows changing the relationship between the feature extraction algorithms and the distance functions, aimed at finding the best integration of both from the specialists point of view. This work integrates the three main elements of similarity queries: the extracted features from the images, the distance function employed to quantify the similarity and the similarity perception from the user. These three elements allowed to build the Ȝsimilarity operatorsȝ. The experiments performed show that the new perceptual parameter can narrow the semantic gap between what the system retrieves and what the specialist expects.

Content-based retrieval of medical images: From context to perception

A challenge in Content-Based retrieval of image exams is to provide a timely answer that complies to the specialists expectation. In many situations, when a specialist gets a new image to analyze, having information and knowledge from similar cases can be very helpful. However, the semantic gap between low-level image features and their high level semantics may impair the system acceptability. In this paper we propose a new method where we gather from the physicians the visual patterns they use to recognize anomalies in images and apply this knowledge not only in the preprocessing of the images, but also on building feature extractors based on these visual patterns. Moreover, our approach generates feature vectors with lower dimensionality diminishing the “dimensionality curse” problem. Experiments using computed tomography lung images show that the proposed method improves the precision of the query results up to 75%, and generates feature vectors up to 94% smaller than traditional feature extraction techniques while keeping the same representative power. This work shows that perception-based feature extraction combined with the image context can be successfully employed to perform similarity queries in medical image databases.

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users’ sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.

PRoSPer: Perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.

Integrating user preference to similarity queries over medical images datasets

Large amounts of images from medical exams are being stored in databases, so developing retrieval techniques is an important research problem. Retrieval based on the image visual content is usually better than using textual descriptions, as they seldom gives every nuances that the user may be interested in. Content-based image retrieval employs the similarity among images for retrieval. However, similarity is evaluated using numeric methods, and they often orders the images by similarity in a way rather distinct from the users intention. In this paper, we propose a technique to allow expressing the users preference over attributes associated to the images, so similarity queries can be refined by preference rules. Experiments performed over a dataset with computed tomography lung images shows that correctly expressing the users preferences, the similarity query precision can increase from an average of 60% up to close to 100%, when enough interesting images exists in the database.

Being Similar is Not Enough: How to Bridge Usability Gap through Diversity in Medical Images

In this paper we present a technique developed to bridge the usability gap in Content-Based Medical Image Retrieval (CBMIR) systems exploring both similarity and diversity. Usability gaps are related to how easy to use a software tool from the radiologists perspective is. Although much have been done to better express similarity queries, the use of CBMIR over massive databases may have drawbacks that impact its usability. We claim that much of the problems derives from the fact that many images returned are closer to each other than to the query element (near-duplicates). To target this nuisance, we propose to boost similarity queries with diversity, using a technique to hierarchically cluster near-duplicates. We tailored a domain-independent and parameter-free method by controlling the maximum area reached in the search space. This novel approach to improve CBMIR systems take advantage of diversity expectations. The proposed approach BridGE (Better result with influence diversification to Group Elements) aims at adding new relevant information to the analysts, reducing the need of further query refinement or relevance feedback cycles. The results are displayed to the specialist as a traditional CBMIR result whereas the radiologists are able to expand the clusters and navigate through them. The results support our claim that a CBMIR system empowered with diversity is able to bridge the usability gap, grouping near-duplicates and being at least 2 orders of magnitude faster than its mainly competitors.