Quantitative analysis for breast density estimation in low dose chest CT scans

Automated Breast Density Measurements From Chest Computed Tomography Scans

To develop an automated method for quantifying percent breast density from chest computed tomography (CT) scans. A naïve Bayesian classifier based on gray-level intensities and spatial relationships was developed on CT scans from 10 patients diagnosed with Hodgkin lymphoma (HL) and imaged as part of routine clinical care. The algorithm was validated on CT scans from 75 additional HL patients. The classifier was developed and validated using a reference dataset with consensus manual segmentation of fibroglandular tissue. Accuracy was evaluated at the pixel-level to examine how well the algorithm identified pixels with fibroglandular tissue using true and false positive fractions (TPF and FPF, respectively). Quantitative estimates of the patient-level CT percent density were contrasted to each other using the concordance correlation coefficient, ρ_c, and to subjective ACR BI-RADS density assessments using Kendall's τ_b. The pixel-level TPF for identifying pixels with fibroglandular tissue was 82.7% (interquartile range of patient-specific TPFs 65.5%-89.6%). The pixel-level FPF was 9.2% (interquartile range of patient-specific FPFs 2.5%-45.3%). Patient-level agreement of the algorithm's automated density estimate with that obtained from the reference dataset was high, ρ_c = 0.93 (95% CI 0.90-0.96) as was agreement with a radiologist's subjective ACR-BI-RADS assessments, τ_b = 0.77. It is possible to obtain automated measurements of percent density from clinical CT scans.

Quantitative breast density analysis using tomosynthesis and comparison with MRI and digital mammography

BACKGROUND AND OBJECTIVE

Breast density at mammography has been used as markers of breast cancer risk. However, newly introduced tomosynthesis and computer-aided quantitative method could provide more reliable breast density evaluation.

METHODS

In the experiment, 98 tomosynthesis image volumes were obtained from 98 women. For each case, an automatic skin removal was used and followed by a fuzzy c-mean (FCM) classifier which separated the fibroglandular tissues from other tissues in breast area. Finally, percent of breast density and breast volume were calculated and the results were compared with MRI. In addition, the percent of breast density and breast area of digital mammography calculated using the software Cumulus (University of Toronto, Toronto, ON, Canada.) were also compared with 3-D modalities.

RESULTS

Percent of breast density and breast volume, which were computed from tomosynthesis, MRI and digital mammography were 17.37% ± 4.39% and 607.12 cm³ ± 323.01 cm³, 20.3% ± 8.6% and 537.59 cm³ ± 287.74 cm³, and 12.03% ± 4.08%, respectively. There were significant correlations on breast density as well as volume between tomosynthesis and MRI (R = 0.482 and R = 0.805), tomosynthesis and breast density with breast area of digital mammography (R = 0.789 and R = 0.877), and MRI and breast density with breast area of digital mammography (R = 0.482 and R = 0.857) (all P values < .001).

CONCLUSIONS

Breast density and breast volume evaluated from tomosynthesis, MRI and breast density and breast area of digital mammographic images have significant correlations and indicate that tomosynthesis could provide useful 3-D information on breast density through proposed method.

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

RATIONALE AND OBJECTIVES

Low-dose chest computed tomography (LDCT), increasingly being used for screening of lung cancer, may also be used to measure breast density, which is proven as a risk factor for breast cancer. In this study, we developed a segmentation method to measure quantitative breast density on CT images and correlated with magnetic resonance density.

MATERIALS AND METHODS

Forty healthy women receiving both LDCT and breast magnetic resonance imaging (MRI) were studied. A semiautomatic method was applied to quantify the breast density on LDCT images. The intra- and interoperator reproducibility was evaluated. The volumetric density on MRI was obtained by using a well-established automatic template-based segmentation method. The breast volume (BV), fibroglandular tissue volume (FV), and percent breast density (PD) measured on LDCT and MRI were compared.

RESULTS

The measurements of BV, FV, and PD on LDCT images yield highly consistent results, with the intraclass correlation coefficient of 0.999 for BV, 0.977 for FV, and 0.966 for PD for intraoperator reproducibility, and intraclass correlation coefficient of 0.953 for BV, 0.974 for FV, and 0.973 for PD for interoperator reproducibility. The BV, FV, and PD measured on LDCT and MRI were well correlated (all r ≥ 0.90). Bland-Altman plots showed that a larger BV and FV were measured on LDCT than on MRI.

CONCLUSIONS

The preliminary results showed that quantitative breast density can be measured from LDCT, and that our segmentation method could yield a high reproducibility on the measured volume and PD. The results measured on LDCT and MRI were highly correlated. Our results showed that LDCT may provide valuable information about breast density for evaluating breast cancer risk.

An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier

In this paper, a novel framework of computer-aided diagnosis (CAD) system has been presented for the classification of benign/malignant breast tissues. The properties of the generalized pseudo-Zernike moments (GPZM) and pseudo-Zernike moments (PZM) are utilized as suitable texture descriptors of the suspicious region in the mammogram. An improved classifier- adaptive differential evolution wavelet neural network (Ada-DEWNN) is proposed to improve the classification accuracy of the CAD system. The efficiency of the proposed system is tested on mammograms from the Mammographic Image Analysis Society (mini-MIAS) database using the leave-one-out cross validation as well as on mammograms from the Digital Database for Screening Mammography (DDSM) database using 10-fold cross validation. The proposed method on MIAS-database attains a fair accuracy of 0.8938 and AUC of 0.935 (95 % CI = 0.8213-0.9831). The proposed method is also tested for in-plane rotation and found to be highly rotation invariant. In addition, the proposed classifier is tested and compared with some well-known existing methods using receiver operating characteristic (ROC) analysis using DDSM- database. It is concluded the proposed classifier has better area under the curve (AUC) (0.9289) and highly précised with 95 % CI, 0.8216 to 0.9834 and 0.0384 standard error.

Imaging Breast Density: Established and Emerging Modalities

Mammographic density has been proven as an independent risk factor for breast cancer. Women with dense breast tissue visible on a mammogram have a much higher cancer risk than women with little density. A great research effort has been devoted to incorporate breast density into risk prediction models to better estimate each individual’s cancer risk. In recent years, the passage of breast density notification legislation in many states in USA requires that every mammography report should provide information regarding the patient’s breast density. Accurate definition and measurement of breast density are thus important, which may allow all the potential clinical applications of breast density to be implemented. Because the two-dimensional mammography-based measurement is subject to tissue overlapping and thus not able to provide volumetric information, there is an urgent need to develop reliable quantitative measurements of breast density. Various new imaging technologies are being developed. Among these new modalities, volumetric mammographic density methods and three-dimensional magnetic resonance imaging are the most well studied. Besides, emerging modalities, including different x-ray–based, optical imaging, and ultrasound-based methods, have also been investigated. All these modalities may either overcome some fundamental problems related to mammographic density or provide additional density and/or compositional information. The present review article aimed to summarize the current established and emerging imaging techniques for the measurement of breast density and the evidence of the clinical use of these density methods from the literature.

Current and Future Methods for Measuring Breast Density: A Brief Comparative Review

Breast density is one of the strongest predictors of breast cancer risk. Women with the densest breasts are 4 to 6 times more likely to develop cancer compared with those with the lowest densities. Breast density is generally assessed using mammographic imaging; however, this approach has limitations. Magnetic resonance imaging and ultrasound tomography are some alternative imaging modalities that can aid mammography in patient screening and the measurement of breast density. As breast density becomes more commonly discussed, knowledge of the advantages and limitations of breast density as a marker of risk will become more critical. This review article discusses the relationship between breast density and breast cancer risk, lists the benefits and drawbacks of using multiple different imaging modalities to measure density and briefly discusses how breast density will be applied to aid in breast cancer prevention and treatment.

Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter

Background

Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method.

Methods

The novel method is based on oriented Gaussian derivatives filter with seven orientations, combined with Fuzzy C-Means (FCM) clustering and thresholding to refine the lung region. In addition, a new algorithm to automatically generate a threshold value for each Gaussian response is also proposed. The algorithms are applied to both PA and AP chest radiographs from both public JSRT dataset and our private datasets from collaborative hospital. Two pre-processing blocks are introduced to standardize the images from different machines. Comparisons with the previous works found in the literature on JSRT dataset shows that our method gives a reasonably good result. We also compare our algorithm with other unsupervised methods to provide fairly comparative measures on the performances for all datasets.

Results

Performance measures (accuracy, F-score, precision, sensitivity and specificity) for the segmentation of lung in public JSRT dataset are above 0.90 except for the overlap measure is 0.87. The standard deviations for all measures are very low, from 0.01 to 0.06. The overlap measure for the private image database is 0.81 (images from standard machine) and 0.69 (images from two mobile machines). The algorithm is fully automated and fast, with the average execution time of 12.5 s for 512 by 512 pixels resolution.

Conclusions

Our proposed method is fully automated, unsupervised, with no training or learning stage is necessary to segment the lungs taken using both a standard machine and two different mobile machines. The proposed pre-processing blocks are significantly useful to standardize the radiographs from mobile machines. The algorithm gives good performance measures, robust, and fast for the application of the CBMIRS.

How does semi-automated computer-derived CT measure of breast density compare with subjective assessments to assess mean glandular breast density, in patients with breast cancer?

OBJECTIVES

(a) To compare radiologists' breast mammographic density readings with CT subjective measures. (b) To correlate computer-derived measurement of CT density with subjective assessments. (c) To evaluate density distributions in this cohort of patients with breast cancer.

METHODS

A retrospective review of mammograms and CT scans in 77 patients with breast cancer obtained within 1 year of each other was performed. Two radiologists independently reviewed both CT and mammograms and classified each case into four categories as defined by the breast imaging-reporting and data system of the American College of Radiology. Inter-reader agreements were obtained for both mammographic and CT density subjective evaluations by using the Cohen-weighted kappa statistic and Spearman correlation. The semi-automated computer-derived measurement of breast density was correlated with visual measurements.

RESULTS

Inter-reader agreements were lower for subjective CT density grades than those for mammographic readings 0.428 [confidence interval (CI), 0.24-0.89] vs 0.571 (CI, 0.35-0.76). There was moderately good correlation between subjective CT density grades and the mammographic density grades for both readers (0.760 for Reader 1 and 0.913 for Reader 2). The semi-automated CT density measurement correlated well with the subjective assessments, with complete agreement of the density grades in 84.9% of patients and only one level difference in the rest.

CONCLUSIONS

Semi-automated CT density measurements in the evaluation of breast density correlated well with subjective mammographic density measurement.

ADVANCES IN KNOWLEDGE

There is good correlation between CT and mammographic density, but further studies are needed on how to incorporate semi-automated CT breast density measurement in the risk stratification of patients.

A hybrid method based on fuzzy clustering and local region-based level set for segmentation of inhomogeneous medical images

medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.