FFDM image quality assessment using computerized image texture analysis

Quantitative measures of image quality (IQ) are routinely obtained during the evaluation of imaging systems. These measures, however, do not necessarily correlate with the IQ of the actual clinical images, which can also be affected by factors such as patient positioning. No quantitative method currently exists to evaluate clinical IQ. Therefore, we investigated the potential of using computerized image texture analysis to quantitatively assess IQ. Our hypothesis is that image texture features can be used to assess IQ as a measure of the image signal-to-noise ratio (SNR). To test feasibility, the "Rachel" anthropomorphic breast phantom (Model 169, Gammex RMI) was imaged with a Senographe 2000D FFDM system (GE Healthcare) using 220 unique exposure settings (target/filter, kVs, and mAs combinations). The mAs were varied from 10%-300% of that required for an average glandular dose (AGD) of 1.8 mGy. A 2.5cm2 retroareolar region of interest (ROI) was segmented from each image. The SNR was computed from the ROIs segmented from images linear with dose (i.e., raw images) after flat-field and off-set correction. Image texture features of skewness, coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the Premium ViewTM postprocessed image ROIs. Multiple linear regression demonstrated a strong association between the computed image texture features and SNR (R2=0.92, p≤0.001). When including kV, target and filter as additional predictor variables, a stronger association with SNR was observed (R2=0.95, p≤0.001). The strong associations indicate that computerized image texture analysis can be used to measure image SNR and potentially aid in automating IQ assessment as a component of the clinical workflow. Further work is underway to validate our findings in larger clinical datasets.

[1]  Li Lan,et al.  Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. , 2007, Academic radiology.

[2]  C B Caldwell,et al.  Development of an anthropomorphic breast phantom. , 1990, Medical physics.

[3]  Andrew D. A. Maidment,et al.  Quality control for digital mammography: part II. Recommendations from the ACRIN DMIST trial. , 2006, Medical physics.

[4]  Berkman Sahiner,et al.  Computerized image analysis: estimation of breast density on mammograms , 2000, Medical Imaging: Image Processing.

[5]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[6]  Zhimin Huo,et al.  Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. , 2002, Radiology.

[7]  M. Giger,et al.  Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. , 2004, Medical physics.

[8]  M. Giger,et al.  Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. , 2000, Medical physics.

[9]  Andrew D. A. Maidment,et al.  Texture in digital breast tomosynthesis: a comparison between mammographic and tomographic characterization of parenchymal properties , 2008, SPIE Medical Imaging.

[10]  Andrew D. A. Maidment,et al.  Quality control for digital mammography in the ACRIN DMIST trial: part I. , 2006, Medical physics.

[11]  Maryellen L. Giger,et al.  Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms , 2004, CARS.

[12]  Maryellen L. Giger,et al.  Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment , 2008, Journal of Digital Imaging.

[13]  K Doi,et al.  A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads. , 1984, Physics in medicine and biology.

[14]  Ann-Katherine Carton,et al.  Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study. , 2009, Academic radiology.

[15]  N Karssemeijer,et al.  Automated classification of parenchymal patterns in mammograms. , 1998, Physics in medicine and biology.

[16]  Andrew D. A. Maidment,et al.  Image quality in digital mammography: image acquisition. , 2006, Journal of the American College of Radiology : JACR.

[17]  Robert Marti,et al.  A Novel Breast Tissue Density Classification Methodology , 2008, IEEE Transactions on Information Technology in Biomedicine.

[18]  M J Yaffe,et al.  Screen-film and digital mammography. Image quality and radiation dose considerations. , 2000, Radiologic clinics of North America.

[19]  Hilde Bosmans,et al.  Clinical image quality criteria for full field digital mammography: a first practical application. , 2008, Radiation protection dosimetry.