Mixed Metric Random Forest for Dense Correspondence of Cone-Beam Computed Tomography Images
Efficient dense correspondence and registration of CBCT images is an essential yet challenging task for inter-treatment evaluations of structural variations. In this paper, we propose an unsupervised mixed metric random forest (MMRF) for dense correspondence of CBCT images. The weak labeling resulted from a clustering forest is utilized to discriminate the badly-clustered supervoxels and related classes, which are favored in the following fine-tuning of the MMRF by penalized weighting in both classification and clustering entropy estimation. An iterative scheme is introduced for the forest reinforcement to minimize the inconsistent supervoxel labeling across CBCT images. In order to screen out the inconsistent matching pairs and to regularize the dense correspondence defined by the forest-based metric, we evaluate consistencies of candidate matching pairs by virtue of isometric constraints. The proposed correspondence method has been tested on 150 clinically captured CBCT images, and outperforms state-of-the-arts in terms of matching accuracy while being computationally efficient.
This work was supported by National Natural Science Foundation of China under Grant 61272342, and the Seeding Grant for Medicine and Information Sciences of Peking University under Grant 2014MI24.
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