A preliminary study of content-based mammographic masses retrieval

Tao, Yimo; Lo, Shih-Chung Benedict; Freedman, Matthew T.; Xuan, Jianhua

doi:10.1117/12.711528

Cited by 31 publications

(23 citation statements)

References 16 publications

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“…However, the relatively low performance of CAD schemes in mass detection [12], make them less accepted as mass diagnosis tools. As an alternative, the interactive CAD systems, based on content-based information retrieval schemes [13,14,15], identify visually similar mass lesions that eventually are clinically relevant to the actual lesion [16]. Actually, CBIR-based CAD schemes have a potential to provide radiologist with visual aid and increase their confidence in accepting CAD-cued results in the decision making process.…”

Section: Introductionmentioning

confidence: 97%

Automatic BI-RADS Description of Mammographic Masses

Narváez

Díaz

Romero

2010

Digital Mammography

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Abstract. This paper presents a CBIR (Content Based Information Retrieval) framework for automatic description of mammographic masses according to the well known BI-RADS lexicon. Unlike other approaches, we do not attempt to segment masses but instead, we describe the regions an expert selects, after the series of rules defined in the BI-RADS lexicon. The content based retrieval strategy searches similar regions by automatically computing the Mahalanobis distance of feature vectors that describe main shape and texture characteristics of the selected regions. A description of a test region is based on the BI-RADS description associated to the retrieved regions. The strategy was assessed in a set of 444 masses with different shapes and margins. Suggested descriptions were compared with a ground truth already provided by the data base, showing a precision rate of 82.6% for the retrieval task and a sensitivity rate of 80% for the annotation task.

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Section: Introductionmentioning

confidence: 97%

Automatic BI-RADS Description of Mammographic Masses

Narváez

Díaz

Romero

2010

Digital Mammography

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“…These libraries have been used to train and validate computeraided diagnosis (CAD) systems in a variety of medical domains, including breast cancer. However, the value of CAD in clinical practice is controversial, due to their "blackbox" nature and lack of reasoning ability [7], [8], [9], [10], [11], despite significant recent progress [12], [13], [14], [15], [16], [17], [18], [19], [20] both in automated detection and characterization of breast masses. An alternative approach, espoused by efforts such as ISADS [2], eschews automated diagnosis in favor of providing medical professionals with additional context about the current case that could enable them to make a more informed decision.…”

Section: Related Workmentioning

confidence: 99%

“…We evaluate the proposed algorithm in the context of ISADS using two metrics. The first metric, classification accuracy, indicates the extent to which malignant images can be detected based on the images that are retrieved by the system [18], [19]. We compute classification accuracy by the K Nearest Neighbor (KNN) classifier: Given a test example x, we first identify the K training examples that have the shortest distance to x, where distance is computed using the metric learned from training examples; we then compute the probability that x is malignant based on the percentage of its K nearest neighbors that belong to the malignant class.…”

Section: Experiments Setupmentioning

confidence: 99%

A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval

Yang

Jin

Mummert

et al. 2010

IEEE Trans. Pattern Anal. Mach. Intell.

154

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Abstract-Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming distance using the learned binary representation. A boosting algorithm is presented to efficiently learn the distance function. We evaluate the proposed algorithm on a mammographic image reference library with an Interactive Search-Assisted Decision Support (ISADS) system and on the medical image data set from ImageCLEF. Our results show that the boosting framework compares favorably to state-of-the-art approaches for distance metric learning in retrieval accuracy, with much lower computational cost. Additional evaluation with the COREL collection shows that our algorithm works well for regular image data sets.

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“…Let be the feature vector of the candidate image and be the feature vector of the incomplete query image. Euclidean distance between the candidate image feature vector and the query image feature vector is given by [17]. The result of the distance calculation is used for retrieving images similar to incomplete query image.…”

Section: Distance Calculationmentioning

confidence: 99%