Mammographic image denoising and enhancement using the Anscombe transformation, adaptive wiener filtering, and the modulation transfer function

RICE: A method for quantitative mammographic image enhancement

We introduce Region of Interest Contrast Enhancement (RICE) to identify focal densities in mammograms. It aims to help radiologists: 1) enhancing the contrast of mammographic images; and 2) detecting regions of interest (such as focal densities) that are candidate masses potentially masked behind dense parenchyma. Cancer masking is an unsolved issue, particularly in breast density categories BI-RADS C and D. RICE suppresses normal breast parenchyma in order to highlight focal densities. Unlike methods that enhance mammograms by modifying the dynamic range of an image; RICE relies on the actual tissue composition of the breast. It segments Volumetric Breast Density (VBD) maps into smaller regions and then applies a recursive mechanism to estimate the 'neighbourhood' for each segment. The method then subtracts and updates the neighbourhood, or the encompassing tissue, from each piecewise constant component of the breast image. This not only enhances the appearance of a candidate mass but also helps in estimating the mass density. In extensive experiments, RICE enhances focal densities in all breast density types including the most challenging category BI-RADS D. Suitably adapted, RICE can be used as a precursor to any computer-aided diagnostics and detection system.

Relative Contributions of Anatomical and Quantum Noise in Signal Detection and Perception of Tomographic Digital Breast Images

Anatomical and quantum noise inhibits detection of malignancies in clinical images such as in digital mammography (DM), digital breast tomosynthesis (DBT) and breast CT (bCT). In this work, we examine the relative influence and interactions of these two types of noise on the task of low contrast mass detectability in DBT. We show how the changing levels of quantum noise contributes to the estimated power-law slope β by changing DBT acquisition parameters as well as with spatial filtering like an adaptive Weiner filtering. Finally, we examine via human observer LROC studies whether power spectral parameters obtained from DBT images correlate with mass detectability in those images. Our results show that lower values of power-law slope β can result from heightened quantum noise or image artifacts and do not necessarily imply reduced anatomical noise or improved signal detectability for the given imaging system. These results strengthen the argument that when power-law magnitude K is varying, β is less relevant to lesion detectability. Our preliminary results also point to K values having strong correlation to human observer performance, at least for the task shown in this paper. As a byproduct of these main results, we also show that while changes in acquisition geometry can improve mass detectability, the use of efficient filters like an adaptive Weiner filtering can significantly improve the detection of low contrast masses in DBT.

Development of a high resolution x-ray inspection system using a carbon nanotube based miniature x-ray tube

A new concept for a non-destructive testing device using a novel carbon nanotube (CNT) based miniature x-ray tube is proposed. The device can be used for small-scale internal inspection of objects. To investigate the effectiveness of the proposed concept, the device was fabricated and its performance was systematically analyzed. The non-destructive testing device consists of a CNT based miniature x-ray tube, a scintillator, an optical lens, and a detector. The size of the focal spot needed to identify objects as small as 5 µm was calculated through simulation. An electron optics simulation software, E-GUN, was used to optimize the geometries of both the focusing cup and the x-ray target to achieve the desired focal spot size of the x-ray tube. The CNT based miniature x-ray tube was fabricated using the brazing process, and an NdFeB focusing lens was used to further reduce the focal spot size. XR images were obtained using the fabricated device and the spatial resolutions of the images were evaluated using the modulation transfer function (MTF). The fields of view (FOVs) per probe are 7.1 mm² and 1.8 mm² when using a 5× optical lens and a 10× optical lens, respectively. The FOV can be increased by increasing the number of probes incorporated into the device. MTF10 values were determined to be 105 lp/mm and 230 lp/mm when using the 5× optical lens and 10× optical lens, respectively. By using an optical lens to enlarge the XR images, the effect of focal spot was minimized and clear XR images were obtained.

Improving the Automated Detection of Calcifications Using Adaptive Variance Stabilization

In this paper, we analyze how stabilizing the variance of intensity-dependent quantum noise in digital mammograms can significantly improve the computerized detection of microcalcifications (MCs). These lesions appear on mammograms as tiny deposits of calcium smaller than 20 pixels in diameter. At this scale, high frequency image noise is dominated by quantum noise, which in raw mammograms can be described with a square-root noise model. Under this assumption, we derive an adaptive variance stabilizing transform (VST) that stabilizes the noise to unitary standard deviation in all the images. This is achieved by estimating the noise characteristics from the image at hand. We tested the adaptive VST as a preprocessing stage for four existing computerized MC detection methods on three data sets acquired with mammographic units from different manufacturers. In all the test cases considered, MC detection performance on transformed mammograms was statistically significantly higher than on unprocessed mammograms. Results were also superior in comparison with a "fixed" (nonparametric) VST previously proposed for digital mammograms.

Comparison of screening performance metrics and patient dose of two mammographic image acquisition modes in the Danish National Breast Cancer Screening Programme

INTRODUCTION

In this study, screening performance metrics and radiation dose were compared for two image acquisition modes for breast cancer screening with MAMMOMAT Inspiration (Siemens Healthcare GmbH, Forchheim, Germany). This mammography system can operate without an anti-scatter grid in place but using software scatter correction instead. This grid-less acquisition mode (PRIME) requires less patient dose due to the increase in primary radiation reaching the detector. This study retrospectively analyses data from the Region of Southern Denmark where the grid-less mode has been installed in November 2013 and replaced grid-based screening.

METHODS AND MATERIALS

A total of 72,188 screening cases from the same geographical region in Denmark were included in the study. They were subdivided into two study populations: cases acquired before and after installation of the grid-less acquisition mode. Sensitivity and specificity of breast cancer screening were calculated for the two populations; thus representing the performance of grid-less and grid-based screening. To measure the entrance surface air kerma (ESAK) additional phantom tests were carried out. Polymethylmethacrylate (PMMA) attenuation plates with different thicknesses (20-70 mm in steps of 10 mm) simulated the compressed breast (21 mm-90 mm) and a solid-state dosimeter was used.

RESULTS

Statistical testing of the results showed that screening with grid-less acquisition provides equivalent performance with respect to sensitivity and specificity compared to grid-based screening. The specificity was 98.11% (95% confidence interval (CI) from 97.93% to 98.29%) and 97.96% (95% CI from 97.84% to 98.09%) for screening with grid-less acquisition and grid-based acquisition, respectively. The cancer detection rate as a measure for sensitivity was equal (0.55%) for grid-less screening and grid-based screening. An average glandular dose saving between 13.5% and 36.4% depending on breast thickness in grid-less acquisition was obtained compared to grid-based acquisition.

CONCLUSION

Statistically significant equivalence was shown with an equivalence margin of 0.12% points for cancer detection rate and with an equivalence margin of 0.40% points for specificity. A marked patient dose savings in grid-less acquisition of up to 36% compared to grid-based acquisition was achieved. It can be concluded that grid-less acquisition with software scatter correction is an alternative to grid-based acquisition in mammography.

Method for simulating dose reduction in digital mammography using the Anscombe transformation

PURPOSE

This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose.

METHODS

The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose.

RESULTS

The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%.

CONCLUSIONS

A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.

A new approach for clustered MCs classification with sparse features learning and TWSVM

In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the l_P-regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods.

A modified undecimated discrete wavelet transform based approach to mammographic image denoising

In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.