Location Registration and Recognition (LRR)

Location Registration and Recognition (LRR) for Longitudinal Evaluation of Corresponding Regions in CT Volumes

This page gives a high level overview of our research on Location Registration and Recognition (LRR). For more details, please refer to our article published in MICCAI 2008 proceedings.



The algorithm described in this paper takes (a) two temporally separated CT scans, I1 and I2, and (b) a series of locations in I1, and it produces, for each location, an affine transformation mapping the locations and their immediate neighborhood from I1 to I2. It does this without deformable registration by using a combination of feature extraction, indexing, refinement and decision processes. Together these essentially "recognize" the neighborhoods. We show on lung CT scans that this works at near interactive speeds, and is at least as accurate as the Diffeomorphic Demons algorithm [1]. The algorithm may be used both for diagnosis and treatment monitoring.

Motivation and Intuition

  • Given:  image volumes I1 and I2
    set of locations L = {x_1, ..., x_N} from I1.
  • Goal: find, for each x_k, the affine transformation Tk, which best aligns neighborhood N(x_k) with a region of I2.

Algorithm Outline

Repeat for each pre-selected location x_k:

  1. Gather keypoints within N(x_k).
  2. Indexing: for keypoint in N(x_k), find match in I2.
  3. Order matches by increasing descriptor distance. Consider the best M=20.
  4. Repeat
    1. Pick next match and generate initial transform.
    2. Estimate local affine transform parameters.
    3. Apply the verification classifier.
  5. Until all matches from the list have been processed.
  6. No transformation has been found for x_k.

Figure 1: Diagram of the Location Registration and Recognition system. The initial transform Tk maps the region surrounding the location x_k from image I1 into image I2. The transform Tk is refined into accurate alignment in the estimation stage using correspondences between image features. If the verification step decides that the alignment is correct, the algorithm finishes. Otherwise, new initialization is generated.



Figure 2: Examples of LRR (1st and 3rd column) vs. deformable registration (2nd and 4th column). Agreement of both results (a), and examples where LRR alignment is better (b). Features detected in fixed (blue) and moving (red) images drive the registration and robust estimation handles outliers (e.g. the case with changes on the bottom right which is partially aligned in the lower half).

Lung CT Neighborhood 1Lung CT Neighborhood 5
Lung CT Neighborhood 2Lung CT Neighborhood 6
Lung CT Neighborhood 3Lung CT Neighborhood 7
Lung CT Neighborhood 4Lung CT Neighborhood 8

Figure 3: Examples of nodule alignments shown as a checkerboard image alternating fixed and mapped moving axial slices. Images in the upper row have superimposed fixed features (blue) and mapped moving features (red). LRR correctly aligns nodules of various shapes and sizes in neighborhoods with different structural complexity.

Nodule Neighborhood 1 Nodule Neighborhood 5 Nodule Neighborhood 2 Nodule Neighborhood 6
Nodule Neighborhood 3 Nodule Neighborhood 7 Nodule Neighborhood 4 Nodule Neighborhood 8


  • Algorithm for Location Registration and Recognition (LRR) without solving deformable registration first or simultaneously
  • Technique to obtain initial transform using shape contexts
  • Novel verification algorithm
  • Handle changes within the local regions
  • At least as accurate as the deformable registration
  • Fast algorithm runs at near interactive speeds

     Future work:

  • Combining results from multiple locations, exploring other applications

Publications and Further Reading

  1. Sofka, M., V. Stewart, C., 2010. Location Registration and Recognition (LRR) for Serial Analysis of Nodules in Lung CT Scans. Medical Image Analysis 14, 407–428.

  1. Sofka, M., V. Stewart, C., 2008. Location Registration and Recognition (LRR) for Longitudinal Evaluation of Corresponding Regions in CT Volumes. In: Proceedings of the 11th International Conference On Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008). pp. 989–997.


[1] Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Non-parametric diffeomorphic image registration with the demons algorithm. In: Proceedings of the 10th International Conference of Medical Image Computing and Computer-Assisted Intervention (MICCAI 2007), Brisbane, Australia (2007) 319-326.