Using relevance feedback to reduce the semantic gap in content-based image retrieval of mammographic masses

Rosa, Natália Abdala; Felipe, Joaquim Cezar; Traina, Agma J. M.; Traina, Caetano; Rangayyan, Rangaraj M.; Azevedo-Marques, Paulo Mazzoncini de

doi:10.1109/iembs.2008.4649176

Cited by 15 publications

(10 citation statements)

References 11 publications

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“…In our experiments, the gain in the performance of CBIR with RFb was associated with the BI-RADS category of the query mammographic image, with large improvement in cases of lesions belonging to categories 4 and 5. The proposed method could find applications in computer-aided diagnosis (CAD) of breast cancer [12].…”

Section: Analysis Of Work Donementioning

confidence: 99%

Using Relevance Feedback in Bridging Semantic Gaps in Content-Based Image Retrieval

Deb

2010

2010 Second International Conference on Advances in Future Internet

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Content-based image retrieval (CBIR) is a difficult area of research in multimedia systems. The research has proved extremely difficult because of the inherent problems in proper automated analysis and feature extraction of the image to facilitate proper classification of various objects. An image may contain more than one objects and to segment the image in line with object features to extract meaningful objects and then classify it in high-level like table, chair, car and so on has become a challenge to the researchers in the field. The latter part of the problem, the gap between low-level features like color, shape, texture, spatial relationships and highlevel definitions of the images is called the semantic gap. Until we solve these problems in an effective way, the efficient processing and retrieval of information from images will be difficult to achieve. In this paper we explore the possibilities of how relevance feedback can help us solve this problem of semantic gap although lot of works have already been done using the concepts of relevance feedback in this area. This would enable efficient image retrieval for internet of the future.

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Section: Analysis Of Work Donementioning

confidence: 99%

Using Relevance Feedback in Bridging Semantic Gaps in Content-Based Image Retrieval

Deb

2010

2010 Second International Conference on Advances in Future Internet

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“…Poor performance is caused by high false-positive rates [8] and the use of only one view [9]. The benefit of using CAD systems are still being tested [10,11], and new CAD schemes are being developed [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Asymmetry, which consists of a comparison of the left and right breast images [27], is a technique that could be used to significantly improve the results.…”

Section: Open Accessmentioning

confidence: 99%

Image Similarity to Improve the Classification of Breast Cancer Images

Tahmoush

2009

Algorithms

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Techniques in image similarity can be used to improve the classification of breast cancer images. Breast cancer images in the mammogram modality have an abundance of non-cancerous structures that are similar to cancer, which make classification of images as containing cancer especially difficult to work with. Only the cancerous part of the image is relevant, so the techniques must learn to recognize cancer in noisy mammograms and extract features from that cancer to appropriately classify images. There are also many types or classes of cancer with different characteristics over which the system must work. Mammograms come in sets of four, two images of each breast, which enables comparison of the left and right breast images to help determine relevant features and remove irrelevant features. In this work, image feature clustering is done to reduce the noise and the feature space, and the results are used in a distance function that uses a learned threshold in order to produce a classification. The threshold parameter of the distance function is learned simultaneously with the underlying clustering and then integrated to produce an agglomeration that is relevant to the images. This technique can diagnose breast cancer more accurately than commercial systems and other published results

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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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Using relevance feedback to reduce the semantic gap in content-based image retrieval of mammographic masses

Cited by 15 publications

References 11 publications

Using Relevance Feedback in Bridging Semantic Gaps in Content-Based Image Retrieval

Using Relevance Feedback in Bridging Semantic Gaps in Content-Based Image Retrieval

Image Similarity to Improve the Classification of Breast Cancer Images

Automatic BI-RADS Description of Mammographic Masses

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