Generalized breast density metrics

Abstract

Mammograms represent data that can inform future risk of breast cancer. Data from two case-control study populations were analyzed. Population 1 included women (N = 180 age matched case-control pairs) with mammograms acquired with one indirect x-ray conversion mammography unit. Population 2 included women (N = 319 age matched case-control pairs) with mammograms acquired from 6 direct x-ray conversion units. The Fourier domain was decomposed into n concentric rings (radial spatial frequency bands). The power in each ring was summarized giving a set of measures. We investigated images in raw, for presentation (processed) and calibrated representations and made comparison with the percentage of breast density (BD) determined with the operator assisted Cumulus method. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs), per standard deviation increase derived from the respective breast density distributions and 95% confidence intervals (CIs) were estimated. A measure from a lower radial frequency ring, corresponding 0.083–0.166 cycles mm−1 and BD had significant associations with risk in both populations. In Population 1, the Fourier measure produced significant associations in each representation: OR = 1.76 (1.33, 2.32) for raw; OR = 1.43 (1.09, 1.87) for processed; and OR = 1.68 (1.26, 2.25) for calibrated. BD also provided significant associations in Population 1: OR = 1.72 (1.27, 2.33). In Population 2, the Fourier measure produced significant associations for each representation as well: OR = 1.47 (1.19, 1.80) for raw; OR = 1.38 (1.15, 1.67) for processed; and OR = 1.42 (1.15, 1.75) for calibrated. BD provided significant associations in Population 2: OR = 1.43 (1.17, 1.76). Other coincident spectral regions were also predictive of case-control status. In sum, generalized breast density measures were significantly associated with breast cancer in both FFDM technologies.

Spatial Correlation and Breast Cancer Risk

“…Patient characteristics for Study 1 and Study 2 [16] are provided in Table 1a and 1b, respectively. Both studies were comprised of primarily Caucasian women (83.8% for Study 1 and 89.2% for Study 2).…”

Image Texture and Fourier Analysis in a Polar Spatial Frequency System: methods illustration

“…These were processed with filters constructed in the FD that were also 1001×1001 pixels (double precision). In the numerical manipulations, where units aided the illustrations, we used d = 70μm (or 0.07mm) giving fc ≈ 7.14 cycles/mm, following from our work in mammography [27] unless otherwise noted. The peppers image is in 24-bit red-green-blue (RGB) format with 512×512 pixels.…”

“…Pm is shown in right side of Figure 6, which is easily visualized as a onedimensional radial spatial frequency plot. Similarly, the band analysis can also be used to compare spectra by considering variations in the respective Pm (or pm) as illustrated previously [22,27]. Radial spatial frequencies are illustrated in the spatial domain.…”

“…This is provided within the context of describing a non-parametric method for characterizing the spectra of twodimensional images. This approach divides the power spectrum of a given image into concentric bands of constant radial bandwidth, neglecting angular dependencies, described previously [17,22,27]. Summation over a given band produces a measure indexed to its specific location in the radial frequency plane and bandwidth.…”

Estimación Semi-automática de Fracción Glandular Mamaria en Imágenes de Mastografía

“…La densidad mamográfica ha sido evaluada por métodos totalmente automatizados [5], especificando la variabilidad entre observadores y la impracticidad para el cribado de la población, lo que sugiere el desarrollo de una medida semiautomatizada para aplicaciones dentro de la clínica. Se han realizado también esfuerzos para incorporar escalas visuales y umbrales basados en el histograma, matrices de coocurrencia, longitud de corrida, composición arquitectónica, complejidad o transformaciones multiresolución y multiespectrales [6] [7], pero estos enfoques requieren etapas de procesamiento más complejas o inexactas, las cuales son aplicadas a la imagen completa sin discriminar primero ciertas estructuras, que no deberían ser consideradas en la estimación de la FG.…”