Matching Corresponding Regions of Interest on Cranio-Caudal and Medio-Lateral Oblique View Mammograms

Abstract

Automatically matching corresponding regions of interest (ROIs) on two-view images is valuable in breast cancer diagnosis, benefiting of saving time and cutting the workload. We propose a method for matching the corresponding ROIs by integrating the geometric model and image similarity searching. The geometric model is implemented by restoring a free breast in the 3D space from two-view preprocessed breast contours. Then, the possible position of the ROI center on cranio-caudal (CC)/medio-lateral oblique (MLO) view image is represented by three feature points in the 3D space. As the view changes, these points can be mapped onto the MLO/CC view image. A matching strip is created later according to the confidence interval, within which the specific position of the ROI can be located by image similarity searching. The experiments were conducted on 273 pairs of mammograms with 400 calcifications and 284 pairs with 300 masses to verify the accuracy of the geometric model and similarity searching. The mean absolute error between the curves and the ROI centers was 3.36 ± 2.90 mm. For 95% detection sensitivity, the confidence interval was ±8.77 mm. For calcifications, the mean distance between the centers of the matched ROIs and the reference was 3.92 ± 4.61 mm. About 93.46% cases had overlap greater than 50%, and 92.46% cases had overlap greater than 75%. For masses, the mean distance was 6.15 ± 7.08 mm. About 88.46% cases had overlap greater than 50%, and 85.58% cases had overlap greater than 75%.

Keywords

• Mammogram
• geometric model
• matching ROIs
• similarity measure