Robust point matching (RPM) jointly estimates correspondences and non-rigid warps between unstructured
point-clouds. RPM does not, however, utilize information of the topological structure or group memberships of
the data it is matching. In numerous medical imaging applications, each extracted point can be assigned group
membership attributes or labels based on segmentation, partitioning, or clustering operations. For example,
points on the cortical surface of the brain can be grouped according to the four lobes. Estimated warps should
enforce the topological structure of such point-sets, e.g. points belonging to the temporal lobe in the two
point-sets should be mapped onto each other.
We extend the RPM objective function to incorporate group membership labels by including a Label Entropy
(LE) term. LE discourages mappings that transform points within a single group in one point-set onto points
from multiple distinct groups in the other point-set. The resulting Labeled Point Matching (LPM) algorithm
requires a very simple modification to the standard RPM update rules.
We demonstrate the performance of LPM on coronary trees extracted from cardiac CT images. We partitioned
the point sets into coronary sections without a priori anatomical context, yielding potentially disparate labelings
(e.g. [1,2,3] → [a,b,c,d]). LPM simultaneously estimated label correspondences, point correspondences, and a
non-linear warp. Non-matching branches were treated wholly through the standard RPM outlier process akin to
non-matching points. Results show LPM produces warps that are more physically meaningful than RPM alone.
In particular, LPM mitigates unrealistic branch crossings and results in more robust non-rigid warp estimates.
Proc. SPIE. 7259, Medical Imaging 2009: Image Processing
KEYWORDS: Image processing algorithms and systems, Detection and tracking algorithms, Image segmentation, Image processing, Image registration, Medical imaging, Computed tomography, Current controlled current source
Automated labeling of the bronchial tree is essential for localization of airway related diseases (e.g. chronic bronchitis) and is also a useful precursor to lung-lobe labeling. We describe an automated method for registration-based labeling of a bronchial tree. The bronchial tree is segmented from a CT image using a region-growing based algorithm. The medial line of the extracted tree is then computed using a potential field based approach. The expert-labeled target (atlas) and the source bronchial trees in the form of extracted centerline point sets are brought into alignment by calculating a non-rigid thin-plate spline (TPS) mapping from the source to the target. The registration takes into account global as well as local variations in anatomy between the two images through the use of separable linear and non-linear components of the transformation; as a result it is well suited to matching structures that deviate at finer levels: namely higher order branches. The method is validated by registering together pairs of datasets for which the ground truth labels are known in advance: the labels are transferred after matching target to source and then compared with the true values. The method was tested on datasets each containing 18 branch centerpoints and 12 bifurcation locations (30 landmarks in total) annotated manually by a radiologist, where the performance was measured as the number of landmarks having the correct transfer of labels. An overall accuracy of labeling of 91.5 % was obtained in matching 23 pairs of datasets obtained from different patients.