Automatic breast parenchymal density classification integrated into a CADe system

Bueno, Gloria; Vállez, Noelia; Déniz, Óscar; Esteve, Pablo Navarro; Rienda, Miguel A.; Arias, Mauricio; Pastor, Carlos

doi:10.1007/s11548-010-0510-z

Cited by 13 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The main lesions in breast images are masses and microcalcifications [33]. Figure 1 shows the hierarchy data results that must be saved in case of working with a regular mammographic study which contains four images (a craniocaudal projection (CC) and a mediolateral oblique (MLO) projection by each breast).…”

Section: Methodsmentioning

confidence: 99%

“…In this step, it is important to control that there are no inconsistencies between data stored in SR files and data stored in the EHR database. For creating SR files the dcm4che library is used [33]. To store the results in the EHR database, the CAD system calls to a JavaScript function of the EHR.…”

Section: Methodsmentioning

confidence: 99%

“…In [33] several algorithms for helping in detecting masses, microcalcifications, and distortions were developed. These algorithms were integrated into the CAD tool.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

CADe System Integrated within the Electronic Health Record

Vállez

Bueno

Déniz

et al. 2013

BioMed Research International

View full text Add to dashboard Cite

The latest technological advances and information support systems for clinics and hospitals produce a wide range of possibilities in the storage and retrieval of an ever-growing amount of clinical information as well as in detection and diagnosis. In this work, an Electronic Health Record (EHR) combined with a Computer Aided Detection (CADe) system for breast cancer diagnosis has been implemented. Our objective is to provide to radiologists a comprehensive working environment that facilitates the integration, the image visualization, and the use of aided tools within the EHR. For this reason, a development methodology based on hardware and software system features in addition to system requirements must be present during the whole development process. This will lead to a complete environment for displaying, editing, and reporting results not only for the patient information but also for their medical images in standardised formats such as DICOM and DICOM-SR. As a result, we obtain a CADe system which helps in detecting breast cancer using mammograms and is completely integrated into an EHR.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

CADe System Integrated within the Electronic Health Record

Vállez

Bueno

Déniz

et al. 2013

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Some works have used this classification method in the breast cancer area, such as the work developed by Bueno et al (38) and the one made by Castella et al (50). By one hand, the first author just uses the method without any conclusive value of efficacy reported; on the other hand, Castela (50) proved that NB performance is excellent for low-dimensional features spaces, where the independent assumption can still be considered as valid.…”

Section: Naïve Bayesmentioning

confidence: 99%

Using Data Mining Techniques to Support Breast Cancer Diagnosis

Diz

Marreiros

2015

New Contributions in Information Systems and Technologies

View full text Add to dashboard Cite

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method.In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis.In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings.Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix.For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests).The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS ® scale.The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications.The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques. VIII IX ResumoCada vez mais assistimos a um aumento global do número de métodos de apoio a decisão e diagnóstico assistido por computador, aplicados a diversas áreas da medicina.Na área de investigação do cancro da mama muitos são os trabalhos que têm sido desenvolvidos como segunda leitura de modo a reduzir o número de falsos positivos no diagnóstico.Neste estudo é apresentado um conjunto de técnicas de data mining que poderão ser aplicadas a um sistema de apoio à decisão na área do diagnóstico de cancro da mama.Esta abordagem tem por objetivo ajudar os clínicos na identificação de achados mamográficos como microcalcificações, massas e mesmo tecidos normais, de forma a evitar diagnósticos errados.Para isso, neste trabalho é usada uma base de dados fidedigna, de 410 imagens correspondentes a 115 pacientes, contendo análises prévias, realizadas por radiologistas, de microcalcificações, massas e tecidos considerados normais.Ao longo deste trabalho são utilizadas duas técnicas de extração de características, a matriz de coocorrência de níveis de cinza e a matriz de comprimento da linha de níveis de cinza.

show abstract

“…Since mammographic tissue density is one of the strongest known breast cancer risk indicator to date [13, 14], several research groups have attempted to improve CAD performance in mass detection by taking mammographic density information into account in CAD decision making [15, 16]. Due to considerable inter- and intra-observer variability, subjectively rating mammographic density using Breast Imaging Reporting and Data System (BIRADS) is often unreliable [17].…”

Section: Introductionmentioning

confidence: 99%

Improving Performance of Computer-aided Detection of Masses by Incorporating Bilateral Mammographic Density Asymmetry: An Assessment

Wang

et al. 2012

Academic Radiology

View full text Add to dashboard Cite

Rationale and Objectives Bilateral mammographic density asymmetry is a promising indicator in assessing risk of having or developing breast cancer. This study aims to assess the performance improvement of a computer-aided detection (CAD) scheme in detecting masses by incorporating bilateral mammographic density asymmetrical information. Materials and Methods A testing dataset containing 2400 full-field digital mammograms (FFDM) acquired from 600 examination cases was established. Among them, 300 are positive cases with verified cancer associated with malignant masses and 300 are negative cases. Two computerized schemes were applied to process images of each case. The first single-image based CAD scheme detected suspicious mass regions and the second scheme computed average and difference of mammographic tissue density depicted between the left and right breast. A fusion method based on rotation of the CAD scoring projection reference axis was then applied to combine CAD-generated mass detection scores and either the computed average or difference (asymmetry) of bilateral mammographic density scores. The CAD performance levels with and without incorporating mammographic density information were evaluated and compared using a free-response receiver operating characteristic (FROC) type data analysis method. Results CAD achieved a case-based mass detection sensitivity of 0.74 and a region-based sensitivity of 0.56 at a false-positive rate of 0.25 per image. By fusing the CAD and bilateral mammographic density asymmetry scores, the case-based and region-based sensitivity levels of the CAD scheme were increased to 0.84 and 0.69, respectively, at the same false-positive rate. Fusion with average mammographic density only slightly increased CAD sensitivity to 0.75 (case-based) and 0.57 (region-based). Conclusions This study indicated that (1) bilateral mammographic density asymmetry was a stronger indicator of the case depicting suspicious masses than the average density computed from two breasts and (2) fusion between the conventional CAD scores and bilateral mammographic density asymmetry information could substantially increase CAD performance in mass detection.

show abstract

Automatic breast parenchymal density classification integrated into a CADe system

Abstract: The ability to detect suspicious lesions on dense and heterogeneous tissue has been tested. The tools enhance the detectability of lesions and they are able to distinguish their local attenuation without local tissue density constraints.

Cited by 13 publications

References 17 publications

CADe System Integrated within the Electronic Health Record

CADe System Integrated within the Electronic Health Record

Using Data Mining Techniques to Support Breast Cancer Diagnosis

Improving Performance of Computer-aided Detection of Masses by Incorporating Bilateral Mammographic Density Asymmetry: An Assessment

Contact Info

Product

Resources

About