The Experts below are selected from a list of 18822 Experts worldwide ranked by ideXlab platform
Keith Humphreys - One of the best experts on this subject based on the ideXlab platform.
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association of Microcalcification clusters with short term invasive breast cancer risk and breast cancer risk factors
Scientific Reports, 2019Co-Authors: Kamila Czene, Per Hall, Keith HumphreysAbstract:Using for-presentation and for-processing digital mammograms, the presence of Microcalcifications has been shown to be associated with short-term risk of breast cancer. In a previous article we developed an algorithm for Microcalcification cluster detection from for-presentation digital mammograms. Here, we focus on digitised mammograms and use a three-step algorithm. In total, 253 incident invasive breast cancer cases (with a negative mammogram between three months and two years before diagnosis, from which we measured Microcalcifications) and 728 controls (also with prior mammograms) were included in a short-term risk study. After adjusting for potential confounding variables, we found evidence of an association between the number of Microcalcification clusters and short-term (within 3–24 months) invasive breast cancer risk (per cluster OR = 1.30, 95% CI = (1.11, 1.53)). Using the 728 postmenopausal healthy controls, we also examined association of Microcalcification clusters with reproductive factors and other established breast cancer risk factors. Age was positively associated with the presence of Microcalcification clusters (p = 4 × 10−04). Of ten other risk factors that we studied, life time breastfeeding duration had the strongest evidence of association with the presence of Microcalcifications (positively associated, unadjusted p = 0.001). Developing algorithms, such as ours, which can be applied on both digitised and digital mammograms (in particular for presentation images), is important because large epidemiological studies, for deriving markers of (clinical) risk prediction of breast cancer and prognosis, can be based on images from these different formats.
Reyer Zwiggelaar - One of the best experts on this subject based on the ideXlab platform.
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topological modeling and classification of mammographic Microcalcification clusters
IEEE Transactions on Biomedical Engineering, 2015Co-Authors: Zhili Chen, Harry Strange, Arnau Oliver, Erika R E Denton, Caroline Boggis, Reyer ZwiggelaarAbstract:Goal: The presence of Microcalcification clusters is a primary sign of breast cancer; however, it is difficult and time consuming for radiologists to classify Microcalcifications as malignant or benign. In this paper, a novel method for the classification of Microcalcification clusters in mammograms is proposed. Methods: The topology/connectivity of individual Microcalcifications is analyzed within a cluster using multiscale morphology. This is distinct from existing approaches that tend to concentrate on the morphology of individual Microcalcifications and/or global (statistical) cluster features. A set of Microcalcification graphs are generated to represent the topological structure of Microcalcification clusters at different scales. Subsequently, graph theoretical features are extracted, which constitute the topological feature space for modeling and classifying Microcalcification clusters. $k$ -nearest-neighbors-based classifiers are employed for classifying Microcalcification clusters. Results: The validity of the proposed method is evaluated using two well-known digitized datasets (MIAS and DDSM) and a full-field digital dataset. High classification accuracies (up to 96%) and good ROC results (area under the ROC curve up to 0.96) are achieved. A full comparison with related publications is provided, which includes a direct comparison. Conclusion: The results indicate that the proposed approach is able to outperform the current state-of-the-art methods. Significance: This study shows that topology modeling is an important tool for Microcalcification analysis not only because of the improved classification accuracy but also because the topological measures can be linked to clinical understanding.
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modelling mammographic Microcalcification clusters using persistent mereotopology
Pattern Recognition Letters, 2014Co-Authors: Harry Strange, Zhili Chen, Erika R E Denton, Reyer ZwiggelaarAbstract:In mammographic imaging, the presence of Microcalcifications, small deposits of calcium in the breast, is a primary indicator of breast cancer. However, not all Microcalcifications are malignant and their distribution within the breast can be used to indicate whether clusters of Microcalcifications are benign or malignant. Computer-aided diagnosis (CAD) systems can be employed to help classify such Microcalcification clusters. In this paper a novel method for classifying Microcalcification clusters is presented by representing discrete mereotopological relations between the individual Microcalcifications over a range of scales in the form of a mereotopological barcode. This barcode based representation is able to model complex relations between multiple regions and the results on mammographic Microcalcification data shows the effectiveness of this approach. Classification accuracies of 95% and 80% are achieved on the MIAS and DDSM datasets, respectively. These results are comparable to existing state-of-the art methods. This work also demonstrates that mereotopological barcodes could be used to help trained clinicians in their diagnosis by providing a clinical interpretation of barcodes that represent both benign and malignant cases.
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automatic Microcalcification and cluster detection for digital and digitised mammograms
Knowledge Based Systems, 2012Co-Authors: Arnau Oliver, Albert Torrent, Xavier Llado, Meritxell Tortajada, Lidia Tortajada, Melcior Sentis, Jordi Freixenet, Reyer ZwiggelaarAbstract:In this paper we present a knowledge-based approach for the automatic detection of Microcalcifications and clusters in mammographic images. Our proposal is based on using local features extracted from a bank of filters to obtain a local description of the Microcalcifications morphology. The developed approach performs an initial training step in order to automatically learn and select the most salient features, which are subsequently used in a boosted classifier to perform the detection of individual Microcalcifications. Subsequently, the Microcalcification detection method is extended in order to detect clusters. The validity of our approach is extensively demonstrated using two digitised databases and one full-field digital database. The experimental evaluation is performed in terms of ROC analysis for the Microcalcification detection and FROC analysis for the cluster detection, resulting in better than 80% sensitivity at 1 false positive cluster per image.
Martin R Bennett - One of the best experts on this subject based on the ideXlab platform.
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identifying active vascular Microcalcification by 18 f sodium fluoride positron emission tomography
Nature Communications, 2015Co-Authors: Agnese Irkle, Alex T Vesey, David Y Lewis, Jeremy N Skepper, Joseph L E Bird, Marc R Dweck, Francis R Joshi, Ferdia A Gallagher, Elizabeth A Warburton, Martin R BennettAbstract:Vascular calcification is a complex biological process that is a hallmark of atherosclerosis. While macrocalcification confers plaque stability, Microcalcification is a key feature of high-risk atheroma and is associated with increased morbidity and mortality. Positron emission tomography and X-ray computed tomography (PET/CT) imaging of atherosclerosis using 18F-sodium fluoride (18F-NaF) has the potential to identify pathologically high-risk nascent Microcalcification. However, the precise molecular mechanism of 18F-NaF vascular uptake is still unknown. Here we use electron microscopy, autoradiography, histology and preclinical and clinical PET/CT to analyse 18F-NaF binding. We show that 18F-NaF adsorbs to calcified deposits within plaque with high affinity and is selective and specific. 18F-NaF PET/CT imaging can distinguish between areas of macro- and Microcalcification. This is the only currently available clinical imaging platform that can non-invasively detect Microcalcification in active unstable atherosclerosis. The use of 18F-NaF may foster new approaches to developing treatments for vascular calcification.
Pim A. Jong - One of the best experts on this subject based on the ideXlab platform.
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Vascular uptake on ^18F-sodium fluoride positron emission tomography: precursor of vascular calcification?
Journal of Nuclear Cardiology, 2020Co-Authors: Annemarie M. Harder, Jelmer M. Wolterink, Jonas W. Bartstra, Wilko Spiering, Sabine R. Zwakenberg, Joline W. Beulens, Riemer H. J. A. Slart, Gert Luurtsema, Willem P. Mali, Pim A. JongAbstract:Background Microcalcifications cannot be identified with the present resolution of CT; however, ^18F-sodium fluoride (^18F-NaF) positron emission tomography (PET) imaging has been proposed for non-invasive identification of Microcalcification. The primary objective of this study was to assess whether ^18F-NaF activity can assess the presence and predict the progression of CT detectable vascular calcification. Methods and Results The data of two longitudinal studies in which patients received a ^18F-NaF PET-CT at baseline and after 6 months or 1-year follow-up were used. The target to background ratio (TBR) was measured on PET at baseline and CT calcification was quantified in the femoral arteries at baseline and follow-up. 128 patients were included. A higher TBR at baseline was associated with higher calcification mass at baseline and calcification progression ( β = 1.006 [1.005-1.007] and β = 1.002 [1.002-1.003] in the studies with 6 months and 1-year follow-up, respectively). In areas without calcification at baseline and where calcification developed at follow-up, the TBR was .11–.13 ( P
Michael Brady - One of the best experts on this subject based on the ideXlab platform.
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Classification of clusters of Microcalcifications in digital breast tomosynthesis
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2010Co-Authors: Candy P S Ho, Chris Tromans, Julia A Schnabel, Michael BradyAbstract:The detection of Microcalcifications, reconstruction of clusters of Microcalcifications and their subsequent classification into malignant and benign are important tasks in the early detection of breast cancer. Digital breast tomosynthesis (DBT) provides new opportunities in such tasks. By utilizing the multiple projections in DBT and using the geometry of DBT, we have developed an approach to them based on epipolar curves. It improves the sensitivity and specificity in detection; provides information for estimation of 3D positions of Microcalcifications; and facilitates classification. We have generated 15 simulated datasets, each with a Microcalcification cluster based on an ellipsoidal shape. We estimate the 3D positions of the Microcalcifications in each of the clusters and reconstruct the clusters as ellipsoids. We classify each cluster as malignant or benign based on the parameters of the ellipsoids. The classification result is compared with the ground truth. Our results show that the deviations between the actual and estimated 3D positions of the Microcalcification, and the actual and estimated parameters of the ellipsoids are sufficiently small that the classification results are 100% correct. This demonstrates the feasibility in cluster classification in 3D.
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a biologically inspired algorithm for Microcalcification cluster detection
Medical Image Analysis, 2006Co-Authors: Marius George Linguraru, Ruth English, Kostas Marias, Michael BradyAbstract:The early detection of breast cancer greatly improves prognosis. One of the earliest signs of cancer is the formation of clusters of Microcalcifications. We introduce a novel method for Microcalcification detection based on a biologically inspired adaptive model of contrast detection. This model is used in conjunction with image filtering based on anisotropic diffusion and curvilinear structure removal using local energy and phase congruency. An important practical issue in automatic detection methods is the selection of parameters: we show that the parameter values for our algorithm can be estimated automatically from the image. This way, the method is made robust and essentially free of parameter tuning. We report results on mammograms from two databases and show that the detection performance can be improved by first including a normalisation scheme.
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three dimensional reconstruction of Microcalcification clusters from two mammographic views
IEEE Transactions on Medical Imaging, 2001Co-Authors: Michael Brady, Ralph Highnam, Christian Peter Behrenbruch, Ruth English, Yasuyo KitaAbstract:Classification of benign/malignant Microcalcification clusters is a major diagnostic challenge for radiologists. Clinical studies have revealed that the shape of the cluster, and the spatial distribution of individual Microcalcifications within it, are important indicators of its malignancy. However, mammographic images of clustered Microcalcifications confound their three-dimensional (3-D) distribution with image projection and breast compression. This paper presents a novel model-based method for reconstructing Microcalcification clusters in 3-D from two mammographic views (cranio-caudal and medio-lateral oblique-"shoulder to the opposite hip" or lateral-medio). The authors develop a 3-D breast representation and a parameterised breast compression model which constraints geometrically the possible 3-D positions of a calcification in a two-dimensional image. Corresponding calcifications in the two views are matched using an estimate of the calcification volume. Both the geometric constraint and the matching criterion are utilized in the final reconstruction step to build the 3-D reconstructed clusters. Validation experiments are described using 30 clusters to verify the individual steps of the model, and results consistent with known ground truth are obtained. Some of the approximations in the model and future work are discussed in the concluding section.