-
[show abstract]
[hide abstract]
ABSTRACT: The number of women with breast implants is increasing. Radiologists must be familiar with the normal and abnormal findings of common implants. Implant rupture is a well-known complication after surgery and is the main cause of implant removal. Although mammography and ultrasonography are the standard first steps in the diagnostic workup, magnetic resonance imaging (MRI) is the most useful imaging modality for the characterisation of breast implants because of its high spatial resolution and contrast between implants and soft tissues and absence of ionising radiation. MRI has the highest sensitivity and specificity for implant rupture, thanks to its sequences that can suppress or emphasise the signal from silicone. Regardless of the technique used, the overall aim of imaging breast implants is to provide essential information about tissue and prosthesis integrity, detect implant abnormalities and detect breast diseases unrelated to implants, such as breast cancer.
Insights into imaging. 12/2011; 2(6):653-670.
-
[show abstract]
[hide abstract]
ABSTRACT: The goal of this article is to analyze and compare the performance of a developed mass computer-aided detection (CAD) system that takes breast density information into account when using manual or automatic breast density annotations in the training step. The advantages of considering this breast density information will be highlighted.
The image database used in this article is 92 mediolateral oblique (MLO) and 92 craniocaudal (CC) mammograms obtained by a full-field digital mammographic unit. All mammograms contain at least one mass. The evaluation of the experiments is performed using free receiver operating characteristic analysis for evaluating the detection performance and pixel-based receiver operating characteristic analysis for evaluating the segmentation accuracy. In addition, the performance of the automatic breast density classifier is shown using confusion matrices.
When the breast density information is not considered and at a specificity of two false positives per image, the sensitivity obtained by the CAD system is 0.747 for the CC views and 0.853 for the MLO views. Considering the breast density information, the sensitivity for CC and MLO mammograms increases to 0.800 and 0.893, respectively, using manual classification, and 0.827 and 0.907, respectively, using automatic estimation. The same trend is observed when evaluating the CAD segmentation accuracy for detected masses in terms of area under the curve values: without considering breast density, these are 0.920 +/- 0.057 and 0.917 +/- 0.072; using manual classification, 0.934 +/- 0.039 and 0.932 +/- 0.046; and using automatic estimation, 0.947 +/- 0.038 and 0.946 +/- 0.045 for CC and MLO views, respectively.
The experiments showed improved results when breast density information was taken into account. Moreover, the results obtained when using automatic breast density estimation outperformed those based on the manual annotations provided by expert radiologists. In this sense, the experiments showed that breast density information can be beneficial for CAD systems, and this information can be estimated robustly by an automatic procedure, which reduces the inter- and intra-class variability of the radiologists.
Academic radiology 07/2010; 17(7):877-83. · 2.09 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.
Medical image analysis 04/2010; 14(2):87-110. · 3.09 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Studies reported in the literature indicate that the increase in the breast density is one of the strongest indicators of developing breast cancer. In this paper, we present an approach to automatically evaluate the density of a breast by segmenting its internal parenchyma in either fatty or dense class. Our approach is based on a statistical analysis of each pixel neighbourhood for modelling both tissue types. Therefore, we provide connected density clusters taking the spatial information of the breast into account. With the aim of showing the robustness of our approach, the experiments are performed using two different databases: the well-known Mammographic Image Analysis Society digitised database and a new full-field digital database of mammograms from which we have annotations provided by radiologists. Quantitative and qualitative results show that our approach is able to correctly detect dense breasts, segmenting the tissue type accordingly.
Journal of Digital Imaging 07/2009; 23(5):527-37. · 1.25 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The purpose of this article is to present a novel algorithm for the detection of masses in mammographic computer-aided diagnosis systems. Four key points provide the novelty of our approach: (1) the use of eigenanalysis for describing variation in mass shape and size; (2) a Bayesian detection methodology providing a mathematical sound framework, flexible enough to include additional information; (3) the use of a two-dimensional principal components analysis approach to facilitate false positive reduction; and (4) the incorporation of breast density information, a parameter correlated with the performance of most mass detection algorithms and which is not considered in existing approaches. To study the performance of the system two experiments were carried out. The first is related to the ability of the system to detect masses, and thus, free-response receiver operating characteristic analysis was used, showing that the method is able to give high accuracy at a high specificity (80% detection at 1.40 false positives per image). Second, the ability of the system to highlight the pixels belonging to a mass is studied using receiver operating characteristic analysis, resulting in A(z) = 0.89 +/- 0.04. In addition, the robustness of the approach is demonstrated in an experiment where we used the Digital Database for Screening Mammography database for training and the Mammographic Image Analysis Society database for testing the algorithm.
Medical Physics 06/2008; 35(5):1840-53. · 2.83 Impact Factor
-
Albert Torrent,
Anton Bardera,
Arnau Oliver,
Jordi Freixenet,
Imma Boada,
Miguel Feixes,
Robert Marti,
Xavier Lladó, Josep Pont,
Elsa Pérez,
Salvador Pedraza,
Joan Martí
Digital Mammography, 9th International Workshop, IWDM 2008, Tucson, AZ, USA, July 20-23, 2008, Proceedings; 01/2008
-
Digital Mammography, 9th International Workshop, IWDM 2008, Tucson, AZ, USA, July 20-23, 2008, Proceedings; 01/2008
-
Pattern Recognition and Image Analysis, Third Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings, Part II; 01/2007
-
Digital Mammography, 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006, Proceedings; 01/2006
-
[show abstract]
[hide abstract]
ABSTRACT: The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.
Medical Image Analysis.
-
[show abstract]
[hide abstract]
ABSTRACT: It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment
