Texture-Based Breast Cancer Prediction in Full-Field Digital Mammograms Using the Dual-Tree Complex Wavelet Transform and Random Forest Classification

Moschidis, Emmanouil; Chen, Xin; Taylor, Chris; Astley, Susan M.

doi:10.1007/978-3-319-07887-8_30

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…First, we included a set of breast density and texture features estimated using specific algorithms, while additional features have been used in previous studies. [16][17][18][19][20][21]25,[28][29][30] Moreover, our dataset consisted of images which were collected with digital mammography units from two different vendors, although there are generally more vendors with FDA-approved units for use in mammography facilities. 47 Furthermore, additional system-specific parameters (e.g., anode filter) might F.…”

Section: Discussionmentioning

confidence: 99%

“…[23][24][25] This raises the question of potential differences in imagederived measurements from raw and processed mammograms, and subsequent implications in related interpretation. Recent studies investigating associations of digital mammography quantitative descriptors with breast cancer risk [28][29][30] as well as intra-and inter-reader agreement 12,28,31 suggest that DM representation may have an effect. However, this topic remains largely unexplored, with breast density measurements in processed versus raw DM compared in few studies, 12,28,31 while the literature lacks reports for similar comparisons of parenchymal texture descriptors.…”

Section: Introductionmentioning

confidence: 99%

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Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

et al. 2016

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Purpose: With raw digital mammograms (DMs), which retain the relationship with x-ray attenuation of the breast tissue, not being routinely available, processed DMs are often the only viable means to acquire imaging measures. The authors investigate differences in quantitative measures of breast density and parenchymal texture, shown to have value in breast cancer risk assessment, between the two DM representations. Methods: The authors report data from 8458 pairs of bilateral raw ("FOR PROCESSING") and processed ("FOR PRESENTATION") DMs acquired from 4278 women undergoing routine screening evaluation, collected with DM units from two different vendors. Breast dense tissue area and percent density (PD), as well as a range of quantitative descriptors of breast parenchymal texture (statistical, co-occurrence, run-length, and structural descriptors), were measured using previously validated, fully automated software. Feature measurements were compared using matched-pairs Wilcoxon signed-ranks test, correlation (r), and linear-mixed-effects (LME) models, where potential interactions with woman-and system-specific factors were also assessed. The authors also compared texture feature correlations with the established risk factors of the Gail lifetime risk score (r G ) and breast PD (r PD ), and evaluated the within woman intraclass feature correlation (ICC), a measure of bilateral breast-tissue symmetry, in raw versus processed images. Results: All density measures and most of the texture features were strongly (r ≥ 0.6) or moderately (0.4 ≤ r < 0.6) correlated between raw and processed images. However, measurements were significantly different between the two imaging formats (Wilcoxon signed-ranks test, p w < 0.05). The association between measurements varied across features and vendors, and was substantially modified by woman-and system-specific image acquisition factors, such as age, BMI, and mAs/kVp, respectively. The strongest correlation, combined with minimal LME-model interactions, was observed for structural texture features. Overall, texture measures from either image representation were weakly associated with Gail lifetime risk (−0.2 ≤ r G ≤ 0.2), weakly to moderately associated with breast PD (−0.6 ≤ r PD ≤ 0.6), and had overall strong bilateral symmetry (ICC ≥ 0.6). Conclusions: Differences in measures from processed versus raw DM depend highly on the feature, the DM vendor, and image acquisition settings, where structural features appear to be more robust across the different DM settings. The reported findings may serve as a reference in the design of future large-scale studies on mammographic features and breast cancer risk assessment involving multiple DM representations. C

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

et al. 2016

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“…Though multiparametric TA appears highly predictive of risk, comparative studies on one large dataset are lacking and more research is needed into optimised methodology, including location and size of ROIs chosen. It is also important to know whether the image format (raw or processed) and vendor unit affects the predictive abilities of TA, as this would have a significant impact upon the design of prospective multicentre and multivendor studies (181,182). Nonetheless the results are encouraging, particularly the fact that TA appears to confer information on risk separate from that provided by MD (175,180).…”

Section: Ultrasound (Us) and Us Tomographymentioning

confidence: 99%

Breast density: why all the fuss?

Vinnicombe

2018

Clinical Radiology

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The term "breast density" or mammographic density (MD) denotes those components of breast parenchyma visualised at mammography that are denser than adipose tissue. MD is composed of a mixture of epithelial and stromal components, notably collagen, in variable proportions. MD is most commonly assessed in clinical practice with the time-honoured method of visual estimation of area-based percent density (PMD) on a mammogram, with categorisation into quartiles. The computerised semi-automated thresholding method, Cumulus, also yielding area-based percent density, is widely used for research purposes; however, the advent of fully automated volumetric methods developed as a consequence of the widespread use of digital mammography (DM) and yielding both absolute and percent dense volumes, has resulted in an explosion of interest in MD recently. Broadly, the importance of MD is twofold: firstly, the presence of marked MD significantly reduces mammographic sensitivity for breast cancer, even with state-of-the-art DM. Recognition of this led to the formation of a powerful lobby group ('Are You Dense') in the US, as a consequence of which 32 states have legislated for mandatory disclosure of MD to women undergoing mammography. Secondly, it is now widely accepted that MD is in itself a risk factor for breast cancer, with a four-to sixfold increased relative risk in women with PMD in the highest quintile compared to those with PMD in the lowest quintile. Consequently, major research efforts are underway to assess whether use of MD could provide a major step forward towards risk-adapted, personalised breast cancer prevention, imaging, and treatment.

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“…In this paper we present and detail such a method, which extracts features from images of breast cancer tissues, and also supports the development of CAD systems. Another innovative characteristic of our method is that it defines the best association after applying well known algorithms of artificial intelligence, such as decision tree (DT), random forest (RaF), SVM and polynomial (PL) ( Moschidis, Chen, Taylor, & Astley, 2014;Nascimento et al, 2013; de Nazar Silva, de Carvalho Filho, Corra Silva, Cardoso de Paiva, & Gattass, 2015; Ramos et al, 2012 ). The results were evaluated by considering both the rates accuracy (AC) and area under the ROC curve (AUC), which were computed for images with different physical properties.…”

Section: Introductionmentioning

confidence: 99%

LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues

Bruno

Nascimento

Ramos

et al. 2016

Expert Systems with Applications

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In computer-aided diagnosis one of the crucial steps to classify suspicious lesions is the extraction of features. Texture analysis methods have been used in the analysis and interpretation of medical images. In this work we present a method based on the association among curvelet transform, local binary patterns, feature selection by statistical analysis and distinct classification methods, in order to support the development of computer aided diagnosis system. The similar features were removed by the statistical analysis of variance (ANOVA). The understanding of the features was evaluated by applying the decision tree, random forest, support vector machine and polynomial (PL) classifiers, considering the metrics accuracy (AC) and area under the ROC curve (AUC): the rates were calculated on images of breast tissues with different physical properties (commonly observed in clinical practice). The datasets were the Digital Database for Screening Mammography, Breast Cancer Digital Repository and UCSB biosegmentation benchmark. The investigated groups were normal-abnormal and benign-malignant. The association of curvelet transform, local binary pattern and ANOVA with the PL classifier achieved higher AUC and AC values for all cases: the obtained rates were among 91% and 100%. These results are relevant, specially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed association is useful as an automated protocol for the diagnosis of breast tissues and may contribute to the diagnosis of breast tissues (mammographic and histopathological images).

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Texture-Based Breast Cancer Prediction in Full-Field Digital Mammograms Using the Dual-Tree Complex Wavelet Transform and Random Forest Classification

Cited by 6 publications

References 10 publications

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

Breast density: why all the fuss?

LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues

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