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1. Bias minimizing filter design for gradient-based image registration

Author

Robinson, Dirk and Milanfar, Peyman

Subjects
*METHODOLOGY, *NOISE, *PAPER, *ETHNOMETHODOLOGY
Abstract

Abstract: Gradient-based image registration techniques represent a very popular class of approaches to registering pairs or sets of images. As the name suggests, these methods rely on image gradients to perform the task of registration. Very often, little attention is paid to the filters used to estimate image gradients. In this paper, we explore the relationship between such gradient filters and their effect on overall estimation performance in registering translated images. We propose a methodology for designing filters based on image content that minimize the estimator bias inherent to gradient-based image registration. We show that minimizing such bias improves the overall estimator performance in terms of mean square error (MSE) for high signal-to-noise ratio (SNR) scenarios. Finally, we propose a technique for designing such optimal gradient filters in the context of iterative multiscale image registration and verify their further improved performance. [Copyright &y& Elsevier]

Published
2005
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2. The 2014 liver ultrasound tracking benchmark

Author

Tobias Benz, S Allaire, Julia Schwaab, D Lübke, M. A. Lediju Bell, Jan Rühaak, T Kipshagen, D Y F Chung, Matthias Günther, Nassir Navab, V. De Luca, Cristian Grozea, Sven Rothlubbers, O Somphone, Markus Kowarschik, Lars König, Satoshi Kondo, Christine Tanner, Amalia Cifor, Jürgen Jenne, and Publica

Subjects
Paper, medicine.medical_specialty, respiratory motion, Databases, Factual, Tracking (particle physics), Liver ultrasound, motion estimation, Imaging, Three-Dimensional, Healthy volunteers, medicine, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, image guidance, Ultrasonography, Radiological and Ultrasound Technology, business.industry, ultrasound, Respiration, Liver Neoplasms, Patient data, tracking, Congresses as Topic, Magnetic Resonance Imaging, image registration, Benchmarking, Liver, Case-Control Studies, Benchmark (computing), challenge, Radiology, business, Free breathing, Algorithms
Abstract

The Challenge on Liver Ultrasound Tracking (CLUST) was held in conjunction with the MICCAI 2014 conference to enable direct comparison of tracking methods for this application. This paper reports the outcome of this challenge, including setup, methods, results and experiences. The database included 54 2D and 3D sequences of the liver of healthy volunteers and tumor patients under free breathing. Participants had to provide the tracking results of 90% of the data (test set) for pre-defined point-landmarks (healthy volunteers) or for tumor segmentations (patient data). In this paper we compare the best six methods which participated in the challenge. Quantitative evaluation was performed by the organizers with respect to manual annotations. Results of all methods showed a mean tracking error ranging between 1.4 mm and 2.1 mm for 2D points, and between 2.6 mm and 4.6 mm for 3D points. Fusing all automatic results by considering the median tracking results, improved the mean error to 1.2 mm (2D) and 2.5 mm (3D). For all methods, the performance is still not comparable to human inter-rater variability, with a mean tracking error of 0.5-0.6 mm (2D) and 1.2-1.8 mm (3D). The segmentation task was fulfilled only by one participant, resulting in a Dice coefficient ranging from 76.7% to 92.3%. The CLUST database continues to be available and the online leader-board will be updated as an ongoing challenge.

Published
2015