Transformation optimization and image blending for 3D liver ultrasound series stitching

Yuanyuan Sun; Taygun Kekec; Adriaan Moelker; Wiro J. Niessen; Theo van Walsum

doi:10.1117/12.2551439

16 March 2020 Transformation optimization and image blending for 3D liver ultrasound series stitching

Yuanyuan Sun, Taygun Kekec, Adriaan Moelker, Wiro J. Niessen, Theo van Walsum

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 1131512 (2020) https://doi.org/10.1117/12.2551439
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

We propose a consistent ultrasound volume stitching framework, with the intention to produce a volume with higher image quality and extended field-of-view in this work. Directly using pair-wise registrations for stitching may lead to geometric errors. Therefore, we propose an approach to improve the image alignment by optimizing a consistency metric over multiple pairwise registrations. In the optimization, we utilize transformed points to effectively compute a distance between rigid transformations. The method has been evaluated on synthetic, phantom and clinical data. The results indicate that our transformation optimization method is effective and our stitching framework has a good geometric precision. Also, the compound images have been demonstrated to have improved CNR values.

Conference Presentation

Citation Download Citation

Yuanyuan Sun, Taygun Kekec, Adriaan Moelker, Wiro J. Niessen, and Theo van Walsum "Transformation optimization and image blending for 3D liver ultrasound series stitching", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131512 (16 March 2020); https://doi.org/10.1117/12.2551439

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