Robust linear registration of CT images using random regression forests

Ender Konukoglu; Antonio Criminisi; Sayan Pathak; Duncan Robertson; Steve White; David Haynor; Khan Siddiqui M.D.

doi:10.1117/12.878085

11 March 2011 Robust linear registration of CT images using random regression forests

Ender Konukoglu, Antonio Criminisi, Sayan Pathak, Duncan Robertson, Steve White, David Haynor, Khan Siddiqui M.D.

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%.

Citation Download Citation

Ender Konukoglu, Antonio Criminisi, Sayan Pathak, Duncan Robertson, Steve White, David Haynor, and Khan Siddiqui M.D. "Robust linear registration of CT images using random regression forests", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621X (11 March 2011); https://doi.org/10.1117/12.878085

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