Is it blur or is it texture? An analysis of computational blur and texture measures commonly used in medical image...

In this work, we endeavor to investigate how texture information may contribute to the response of a blur measure (BM) with motivation rooted in mammography. This is vital as the interpretation of the BM is typically not evaluated with respect to texture present in an image. We are particularly concerned with lower scales of blur ($$) as this blur is least likely to be detected but can still have a detrimental effect on detectability of microcalcifications.

Three sets of linear models, where BM response was modeled as a linear combination of texture information determined by texture measures (TMs), were constructed from three different datasets of equal-blur-level images; one of computer-generated mammogram-like clustered lumpy background (CLB) images and two image sets derived from the Brodatz texture images. The linear models were refined by removing those TMs that are not significantly non-zero across all three datasets for each BM. We use five levels of Gaussian blur to blur the CLB images and assess the ability of the BMs and TMs to separate the images based on blur level.

We found that many TMs used frequently in the reduced linear models, mimicked the structure of the BMs that they modeled. Surprisingly, while none of the BMs could separate the CLB images across all levels of blur, a group of TMs could. These TMs occurred infrequently in the reduced linear models meaning that they rely on different information compared with that used by the BMs.

These results confirm our hypothesis that BMs can be influenced by texture information in an image. That a subset of TMs performed better than all BMs on the blur classification problem with the CLB images further shows that conventional BMs may not be the optimal tool for blur classification in mammogram images.