11 March 2011 Robust linear registration of CT images using random regression forests
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79621X (2011) https://doi.org/10.1117/12.878085
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
Global linear registration is a necessary first step for many different tasks in medical image analysis. Comparing longitudinal studies1, cross-modality fusion2, and many other applications depend heavily on the success of the automatic registration. The robustness and efficiency of this step is crucial as it affects all subsequent operations. Most common techniques cast the linear registration problem as the minimization of a global energy function based on the image intensities. Although these algorithms have proved useful, their robustness in fully automated scenarios is still an open question. In fact, the optimization step often gets caught in local minima yielding unsatisfactory results. Recent algorithms constrain the space of registration parameters by exploiting implicit or explicit organ segmentations, thus increasing robustness4,5. In this work we propose a novel robust algorithm for automatic global linear image registration. Our method uses random regression forests to estimate posterior probability distributions for the locations of anatomical structures - represented as axis aligned bounding boxes6. These posterior distributions are later integrated in a global linear registration algorithm. The biggest advantage of our algorithm is that it does not require pre-defined segmentations or regions. Yet it yields robust registration results. We compare the robustness of our algorithm with that of the state of the art Elastix toolbox7. Validation is performed via 1464 pair-wise registrations in a database of very diverse 3D CT images. We show that our method decreases the "failure" rate of the global linear registration from 12.5% (Elastix) to only 1.9%.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ender Konukoglu, Ender Konukoglu, Antonio Criminisi, Antonio Criminisi, Sayan Pathak, Sayan Pathak, Duncan Robertson, Duncan Robertson, Steve White, Steve White, David Haynor, David Haynor, Khan Siddiqui, Khan Siddiqui, } "Robust linear registration of CT images using random regression forests", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621X (11 March 2011); doi: 10.1117/12.878085; https://doi.org/10.1117/12.878085
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