Analysis of feature relevance using an image quality index applied to digital mammography

Abstract

In previous work, we investigated the application of the normalized anisotropic quality index (NAQI) as an image quality metric for digital mammography. The initial assessment showed that NAQI depends not only on radiation dose, but also varies based on image features such as breast anatomy. In this work, these dependencies are analyzed by assessing the contribution of a range of features on NAQI values. The generalized matrix learning vector quantization (GMLVQ) was used to evaluate feature relevance and to rank the imaging parameters and breast features that affect NAQI. The GMLVQ uses prototype vectors to segregate and to analyze the NAQI in three classes: (1) low, (2) medium, and (3) high NAQI values. We used Spearman’s correlation coefficient (ρ) to compare the results obtained by the GMLVQ method. The GMLVQ was trained using 6,076 clinical mammograms. The statistical analysis showed that NAQI is dependent on several imaging parameters and breast features; in particular, breast area (ρ = -0.65), breast density (p = 0.62) and tube current-exposure time product (mAs) (p = 0.56). The GMLVQ results show that the most relevant parameters that affect the NAQI values were breast area (approx. 31%), mAs (approx. 24%) and breast density (approx. 15%). The GMLVQ method allowed us to better understand the NAQI results and provide support for the use of this metric for image quality assessment in digital mammography.