Local image registration a comparison for bilateral registration mammography

TL;DR

This study compares deformable registration algorithms for aligning bilateral mammograms to enhance computer-aided diagnosis, finding SPLINE outperforms DEMONS in accuracy on a set of artificially altered images.

Contribution

It evaluates and compares the effectiveness of DEMONs and SPLINE algorithms for bilateral mammogram registration, highlighting the superior performance of SPLINE.

Findings

SPLINE outperforms DEMONS in registration accuracy

Registration accuracy assessed using mean square errors, mutual information, and correlation

Evaluation conducted on 132 artificially altered mammogram images

Abstract

Early tumor detection is key in reducing the number of breast cancer death and screening mammography is one of the most widely available and reliable method for early detection. However, it is difficult for the radiologist to process with the same attention each case, due the large amount of images to be read. Computer aided detection (CADe) systems improve tumor detection rate; but the current efficiency of these systems is not yet adequate and the correct interpretation of CADe outputs requires expert human intervention. Computer aided diagnosis systems (CADx) are being designed to improve cancer diagnosis accuracy, but they have not been efficiently applied in breast cancer. CADx efficiency can be enhanced by considering the natural mirror symmetry between the right and left breast. The objective of this work is to evaluate co-registration algorithms for the accurate alignment of the left to right breast for CADx enhancement. A set of mammograms were artificially altered to create a ground truth set to evaluate the registration efficiency of DEMONs, and SPLINE deformable registration algorithms. The registration accuracy was evaluated using mean square errors, mutual information and correlation. The results on the 132 images proved that the SPLINE deformable registration over-perform the DEMONS on mammography images.