An automatic geometrical and statistical method to detect acoustic shadows in intraoperative ultrasound brain images

In ultrasound images, acoustic shadows appear as regions of low signal intensity linked to boundaries with very high acoustic impedance differences. Acoustic shadows can be viewed either as informative features to detect lesions or calcifications, or as damageable artifacts for image processing tasks such as segmentation, registration or 3D reconstruction. In both cases, the detection of these acoustic shadows is useful. This paper proposes a new method to detect these shadows that combines a geometrical approach to estimate the B-scans shape, followed by a statistical test based on a dedicated modeling of ultrasound image statistics. Results demonstrate that the combined geometrical-statistical technique is more robust and yields better results than the previous statistical technique. Integration of regularization over time further improves robustness. Application of the procedure results in (1) improved 3D reconstructions with fewer artifacts, and (2) reduced mean registration error of tracked intraoperative brain ultrasound images.