BI-RADS guided mammographic mass retrieval

Tao, Yimo; Lo, S.-C.B.; Hadjiski, Lubomir; Chan, Heang Ping; Freedman, Matthew T.

doi:10.1117/12.878274

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…A mammographic mass retrieval platform developed by the Georgetown University Medical Center and University of Michigan Medical Center was presented in SPIE CAD workshops in 2009 and 2010 [ 36 ].…”

Section: Resultsmentioning

confidence: 99%

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Prototypes for Content-Based Image Retrieval in Clinical Practice

Depeursinge¹,

Fischer²,

Müller³

et al. 2011

TOMINFOJ

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Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice.We define applicability to clinical practice by having recently demonstrated the CBIR system on one of the CAD demonstration workshops held at international conferences, such as SPIE Medical Imaging, CARS, SIIM, RSNA, and IEEE ISBI. From 2009 to 2011, the programs of CADdemo@CARS and the CAD Demonstration Workshop at SPIE Medical Imaging were sought for the key word “retrieval” in the title. The systems identified were analyzed and compared according to the hierarchy of gaps for CBIR systems.In total, 70 software demonstrations were analyzed. 5 systems were identified meeting the criterions. The fields of application are (i) bone age assessment, (ii) bone fractures, (iii) interstitial lung diseases, and (iv) mammography. Bridging the particular gaps of semantics, feature extraction, feature structure, and evaluation have been addressed most frequently.In specific application domains, CBIR technology is available for clinical practice. While system development has mainly focused on bridging content and feature gaps, performance and usability have become increasingly important. The evaluation must be based on a larger set of reference data, and workflow integration must be achieved before CBIR-CAD is really established in clinical practice.

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Section: Resultsmentioning

confidence: 99%

“…The collaboration between the two research groups resulted in a publication at SPIE MI 2011 [ 36 ], which was used for the description of the CBIR system in Section 3.1.4. These four systems are described in details in Section 3.1, compared in Section 3.2. and analyzed in terms of gap identification in Section 3.3.…”

Section: Methodsmentioning

confidence: 99%

Prototypes for Content-Based Image Retrieval in Clinical Practice

Depeursinge¹,

Fischer²,

Müller³

et al. 2011

TOMINFOJ

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“…The last two approaches in Table 1 aim at combining CAD and CBMIR strategies as a single tool. The study of Tao [34] proposes an architecture where masses are previously segmented and represented using shape features. To lobulated and irregular shape features, curvature scale descriptors and radial length were used, while texture features were extracted from mass margin.…”

Section: Combining Cad and Cbmir For Analyses Of Mammogramsmentioning

confidence: 99%

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

et al. 2015

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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.

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“…Wei et al [3] projected a relevance feedback learning approach and perform classification with the application of SVM radial kernel using a data set of enormous photographs. Tao et al [4] related the function of 2 classification models, termed as curvature scale space as well as local linear embedded metric with the application of a database of and accuracy of 2 classifiers. Abirami et al [5] employed a wavelet features for 2-class classification of digital mammograms which has gained maximum accuracy for Mammographic Images Analysis Society (MIAS) data set.…”

Section: Introductionmentioning

confidence: 99%

Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

Priya¹

2021

TURCOMAT

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Right now, breast cancer is considered as a most important health problem among women over the world. The detection of breast cancer in the beginning stage can reduce the mortality rate to a considerable extent. Mammogram is an effective and regularly used technique for the detection and screening of breast cancer. The advanced deep learning (DL) techniques are utilized by radiologists for accurate finding and classification of medical images. This paper develops a new deep segmentation with residual network (DS-RN) based breast cancer diagnosis model using mammogram images. The presented DS-RN model involves preprocessing, Faster Region based Convolution Neural Network (R-CNN) (Faster R-CNN) with Inception v2 model based segmentation, feature extraction and classification. To classify the mammogram images, decision tree (DT) classifier model is used. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the DS-RN model has reached to a maximum classification performance with the maximum sensitivity, specificity, accuracy and F-Measure of 98.15%, 100%, 98.86% and 99.07% respectively.

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BI-RADS guided mammographic mass retrieval

Cited by 8 publications

References 5 publications

Prototypes for Content-Based Image Retrieval in Clinical Practice

Prototypes for Content-Based Image Retrieval in Clinical Practice

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

Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

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