Content-based image retrieval for digital mammography

El-Naqa, I.; Yang, Yongyi; Galatsanos, N.P.; Wernick, M.N.

doi:10.1109/icip.2002.1038924

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…Muramatsu et al [9] proposed a psychophysical measure based on neural networks for the evaluation of similar images with mammographic masses. For other related previous works, see Tao and Sklansky [10], Ornes et al [11], El-Naqa et al [12], and Nakagawa et al [13,14].…”

Section: Introductionmentioning

confidence: 94%

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

et al. 2008

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Object We investigate the use of relevance feedback (RFb) and the inclusion of expert knowledge to reduce the semantic gap in content-based image retrieval (CBIR) of mammograms. Materials and methods Tests were conducted with radiologists, in which their judgment of the relevance of the retrieved images was used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon textural characteristics and the distribution of density of fibroglandular tissue in the breast. The features used include statistics of the gray-level histogram, texture features based upon the gray-level co-occurrence matrix, moment-based features, measures computed in the Radon domain, and granulometric measures. Queries for CBIR with RFb were executed by three radiologists. The performance of CBIR was measured in terms of precision of retrieval and a measure of relevance-weighted precision (RWP) of retrieval. ResultsThe results indicate improvement due to RFb of up to 62% in precision and 39% in RWP. ConclusionThe gain in performance of CBIR with RFb depended upon the BI-RADS breast density index of the query mammographic image, with greater improvement in cases of mammograms with higher density.

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Section: Introductionmentioning

confidence: 94%

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

et al. 2008

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“…The features extracted from images could be divided into static (snapshot of enhancement at one point in time) and dynamic (time variant) features according to the acquisition protocol at the time of scanning, and into pre-or intratreatment features according to the scanning time point (33). The static features are based on intensity, object morphology, and texture as presented in our previous work on pattern recognition analysis in PET images (15) or our similarity learning in content-based retrieval from mammogram databases (34,35). The dynamic features are extracted from time-varying acquisitions such as dynamic PET, SPECT or MR.…”

Section: Image Features Extractionmentioning

confidence: 99%

Radiomics in precision medicine for lung cancer

Constanzo

Wei

Tseng

et al. 2017

Transl. Lung Cancer Res.

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With the improvement of external radiotherapy delivery accuracy, such as intensity-modulated and stereotactic body radiation therapy, radiation oncology has recently entered in the era of precision medicine. Despite these precise irradiation modalities, lung cancers remain one of the most aggressive human cancers worldwide, possibly because of diverse genotypic alterations that drive and maintain lung tumorigenesis. It has been long recognized that imaging could aid in the diagnosis, tumor delineation, and monitoring of lung cancer. Moreover, accumulating evidence suggests that imaging information could be further used to tailor treatment type and intensity, as well as predict treatment outcomes in radiotherapy. However, these imaging tasks have been carried out either qualitatively or using simplistic metrics that doesn't take advantage of the full scale of imaging knowledge. Radiomics, which is a recent field of research that aims to provide a more quantitative representation of imaging information relating tumor phenotypes to clinical and genotypic endpoints by embedding extracted image features into predictive mathematical models. These predictive models can be a key component in the clinician decision making and treatment personalization. This review provides an overview of the radiomics application and its methodology for radiation oncology studies in lung cancer.

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“…This improvement in speed while maintaining effectiveness was also demonstrated in our earlier work by learning the regression of a toy sin c function example from both sequentially and randomly selected data points. 26 All the results above were obtained by assuming that all the feedback samples were selected and used all at once for incremental learning. We also considered an alternative strategy for successive learning as follows.…”

Section: Vb1 Relevance Feedback With Local Perturbationmentioning

confidence: 99%

“…Recently, several studies were performed to develop CBIR systems for mammography. [20][21][22][23][24][25][26][27][28][29][30][31] This was initiated by the pioneering work of Swett et al, 23 who developed a computer-based expert system called MAMMO/ICON for automated mammographic image retrieval based on speech recognition technology using findings in the textual report or in the dictation. Mazurowski et al 28 proposed an optimization framework for improving a case-based computer-aided decision ͑CAD͒ system that was developed for the classification of regions of interests ͑ROIs͒ in mammograms.…”

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confidence: 99%

Adaptive learning for relevance feedback: Application to digital mammography

Yang

Naqa

2010

Medical Physics

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Purpose: With the rapid growing volume of images in medical databases, development of efficient image retrieval systems to retrieve relevant or similar images to a query image has become an active research area. Despite many efforts to improve the performance of techniques for accurate image retrieval, its success in biomedicine thus far has been quite limited. This article presents an adaptive content-based image retrieval ͑CBIR͒ system for improving the performance of image retrieval in mammographic databases. Methods: In this work, the authors propose a new relevance feedback approach based on incremental learning with support vector machine ͑SVM͒ regression. Also, the authors present a new local perturbation method to further improve the performance of the proposed relevance feedback system. The approaches enable efficient online learning by adapting the current trained model to changes prompted by the user's relevance feedback, avoiding the burden of retraining the CBIR system. To demonstrate the proposed image retrieval system, the authors used two mammogram data sets: A set of 76 mammograms scored based on geometrical similarity and a larger set of 200 mammograms scored by expert radiologists based on pathological findings. Results:The experimental results show that the proposed relevance feedback strategy improves the retrieval precision for both data sets while achieving high efficiency compared to offline SVM. For the data set of 200 mammograms, the authors obtained an average precision of 0.48 and an area under the precision-recall curve of 0.79. In addition, using the same database, the authors achieved a high pathology matching rate greater than 80% between the query and the top retrieved images after relevance feedback. Conclusions: Using mammographic databases, the results demonstrate that the proposed approach is more accurate than the model without using relevance feedback not only in image retrieval but also in pathology matching while maintaining its effectiveness for online relevance feedback applications.

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Content-based image retrieval for digital mammography

Cited by 10 publications

References 5 publications

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

Radiomics in precision medicine for lung cancer

Adaptive learning for relevance feedback: Application to digital mammography

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