FFDM image quality assessment using computerized image texture analysis

Rachelle Berger; Ann-Katherine Carton; Andrew D. A. Maidment; Despina Kontos

doi:10.1117/12.845297

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22 March 2010 FFDM image quality assessment using computerized image texture analysis

Rachelle Berger, Ann-Katherine Carton, Andrew D. A. Maidment, Despina Kontos

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Proceedings Volume 7622, Medical Imaging 2010: Physics of Medical Imaging; 762213 (2010) https://doi.org/10.1117/12.845297
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

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 View^TM 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.

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Rachelle Berger, Ann-Katherine Carton, Andrew D. A. Maidment, and Despina Kontos "FFDM image quality assessment using computerized image texture analysis", Proc. SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 762213 (22 March 2010); https://doi.org/10.1117/12.845297

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