Decoupling Registration from Measurement: Longitudinal 3D Wound Assessment Anchored to Stable Peri-Wound Skin
Main Article Content
Abstract
Longitudinal three-dimensional wound assessment requires stable spatial correspondences across scans, yet the wound surface–the very region to be measured–changes continuously through contraction, granulation, and remodelling. Including it in registration risks absorbing genuine healing change into the spatial transformation, biasing longitudinal measurements. We decouple registration from measurement by anchoring alignment exclusively to the peri-wound healthy skin while excluding the wound from correspondence construction and transform fitting. A coarse-to-fine rigid-registration pipeline, enhanced by wound-edge distance-decaying weighting, selects the best transformation automatically from multiple candidates. On seven longitudinal tasks across four chronic-wound cases referenced to the first scan, the method achieved mean fitness of 0.57 ± 0.27, root-mean-square error (RMSE) of 2.44 ± 0.94 mm, and overlap ratio of 57.98 ± 22.68%. Where both conventional baselines failed completely, the method still produced usable alignments (fitness > 0.10, overlap > 15%). Spatial weighting further reduced mean RMSE to 2.15 mm, enabling unified quantification of wound area, volume, and depth. These findings indicate that a stable anatomical reference may matter more than algorithmic complexity in longitudinal lesion tracking–a decoupling principle applicable beyond wound care.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
C. K. Sen, “Human wound and its burden: Updated 2022 compendium of estimates,” Adv. Wound Care, vol. 12, pp. 657–667, 2023, doi: 10.1089/wound.2023.0150.
F. Diban, S. Di Lodovico, P. Di Fermo, et al., “Biofilms in chronic wound infections: Innovative antimicrobial approaches using the in vitro Lubbock chronic wound biofilm model,” Int. J. Mol. Sci., vol. 24, Art. no. 1004, 2023, doi: 10.3390/ijms24021004.
M. C. Redmond, G. Gethin, and D. P. Finn, “A review of chronic wounds and their impact on negative affect, cognition, and quality of life,” Int. Wound J., vol. 22, Art. no. e70748, 2025, doi: 10.1111/iwj.70748.
Y. J. Wu, L. P. Wu, and M. F. Yu, “The clinical value of intelligent wound measurement devices in patients with chronic wounds: A scoping review,” Int. Wound J., vol. 21, Art. no. e14843, 2024, doi: 10.1111/iwj.14843.
M. C. Alonso, H. T. Mohammed, R. D. Fraser, et al., “Comparison of wound surface area measurements obtained using clinically validated artificial intelligence-based technology versus manual methods and the effect of measurement method on debridement code reimbursement cost,” Wounds, vol. 35, pp. 387–394, 2023, doi: 10.25270/wnds/23031.
C. L. Zhao, Y. C. Guo, L. L. Li, et al., “Non-invasive techniques for wound assessment: A comprehensive review,” Int. Wound J., vol. 21, Art. no. e70109, 2024, doi: 10.1111/iwj.70109.
K. Moj, A. I., Z. K., et al., “3D measurement of chronic wounds in routine care: A review with practical guidance for smartphone photogrammetry,” Ann. Biomed. Eng., to be published, doi: 10.1007/s10439-026-04172-z.
A. G. Cutti, M. G. Santi, A. H. Hansen, et al., “Accuracy, repeatability, and reproducibility of a hand-held structured-light 3D scanner across multi-site settings in lower limb prosthetics,” Sensors, vol. 24, Art. no. 2350, 2024, doi: 10.3390/s24072350.
D. Filko and E. K. Nyarko, “Autonomous robot-driven chronic wound 3D reconstruction and analysis system,” Robotics, vol. 14, Art. no. 30, 2025, doi: 10.3390/robotics14030030.
R. Bartl, T. Mocnik, G. Tinkhauser, et al., “A lightweight approach to 3D measurement of chronic wounds,” J. WSCG, vol. 27, pp. 53–60, 2019, doi: 10.24132/jwscg.2019.27.1.8.
P. Gholami, M. A. Ahmadi-Pajouh, N. Abolftahi, et al., “Segmentation and measurement of chronic wounds for bioprinting,” IEEE J. Biomed. Health Inform., vol. 22, no. 4, pp. 1269–1277, 2018, doi: 10.1109/JBHI.2017.2743526.
H. Chi, M. Liu, J. Wang, et al., “Cross-hierarchical decoding with SAM for semi-supervised medical image segmentation,” IEEE Trans. Circuits Syst. Video Technol., vol. 36, pp. 3742–3753, 2026, doi: 10.1109/TCSVT.2025.3625276.
H. Wang, S. Guo, J. Ye, et al., “SAM-Med3D: A vision foundation model for general-purpose segmentation on volumetric medical images,” IEEE Trans. Neural Netw. Learn. Syst., vol. 36, pp. 17599–17612, 2025, doi: 10.1109/TNNLS.2025.3586694.
N. Curti, Y. Merli, C. Zengarini, et al., “Effectiveness of semi-supervised active learning in automated wound image segmentation,” Int. J. Mol. Sci., vol. 24, Art. no. 706, 2023, doi: 10.3390/ijms24010706.
P. Zhang, Y. C. Zhang, and Q. Li, “RGB-D camera-based automatic wound-measurement system,” IEEE Trans. Instrum. Meas., vol. 72, pp. 1–11, 2023, doi: 10.1109/TIM.2023.3265758.
D. Filko, R. Cupec, and E. K. Nyarko, “Wound measurement by RGB-D camera,” Mach. Vis. Appl., vol. 29, pp. 633–654, 2018, doi: 10.1007/s00138-018-0920-4.
P. Sheehan, P. Jones, A. Caselli, et al., “Percent change in wound area of diabetic foot ulcers over a 4-week period is a robust predictor of complete healing in a 12-week prospective trial,” Diabetes Care, vol. 26, no. 6, pp. 1879–1882, 2003, doi: 10.2337/diacare.26.6.1879.
O. A. Pena and P. Martin, “Cellular and molecular mechanisms of skin wound healing,” Nat. Rev. Mol. Cell Biol., vol. 25, pp. 599–616, 2024, doi: 10.1038/s41580-024-00715-1.
P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 2, pp. 239–256, 1992, doi: 10.1109/34.121791.
Y. Chen and G. Medioni, “Object modelling by registration of multiple range images,” Image Vis. Comput., vol. 10, no. 3, pp. 145–155, 1992, doi: 10.1016/0262-8856(92)90066-C.
M. A. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM, vol. 24, no. 6, pp. 381–395, 1981, doi: 10.1145/358669.358692.
R. B. Rusu, N. Blodow, and M. Beetz, “Fast Point Feature His-tograms (FPFH) for 3D registration,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Kobe, Japan, May 2009, pp. 3212–3217, doi: 10.1109/ROBOT.2009.5152473.
A. Myronenko and X. Song, “Point set registration: Coherent point drift,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 12, pp. 2262–2275, 2010, doi: 10.1109/TPAMI.2010.46.
B. Jian and B. C. Vemuri, “Robust point set registration using Gaussian mixture models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1633–1645, 2011, doi: 10.1109/TPAMI.2010.223.
Z. Yang, J. S. Heiselman, C. Han, et al., “Resolving the ambiguity of complete-to-partial point cloud registration for image-guided liver surgery with patches-to-partial matching,” IEEE J. Biomed. Health Inform., vol. 30, pp. 459–472, 2026, doi: 10.1109/JBHI.2025.3583875.
J. P. Thirion, “Image matching as a diffusion process: An analogy with Maxwell’s demons,” Med. Image Anal., vol. 2, no. 3, pp. 243–260, 1998, doi: 10.1016/S1361-8415(98)80022-4.
B. B. Avants, C. L. Epstein, M. Grossman, et al., “Symmetric diffeo-morphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain,” Med. Image Anal., vol. 12, no. 1, pp. 26–41, 2008, doi: 10.1016/j.media.2007.06.004.
S. Klein, M. Staring, K. Murphy, et al., “elastix: A toolbox for intensity-based medical image registration,” IEEE Trans. Med. Imaging, vol. 29, no. 1, pp. 196–205, 2010, doi: 10.1109/TMI.2009.2035616.
B. B. Avants, N. J. Tustison, G. Song, et al., “A reproducible evaluation of ANTs similarity metric performance in brain image registration,” NeuroImage, vol. 54, no. 3, pp. 2033–2044, 2011, doi: 10.1016/j.neuroimage.2010.09.025.
G. Balakrishnan, A. Zhao, M. R. Sabuncu, et al., “VoxelMorph: A learning framework for deformable medical image registration,” IEEE Trans. Med. Imaging, vol. 38, no. 8, pp. 1788–1800, 2019, doi: 10.1109/TMI.2019.2897538.
M. Reuter, N. J. Schmansky, H. D. Rosas, et al., “Within-subject template estimation for unbiased longitudinal image analysis,” NeuroImage, vol. 61, no. 4, pp. 1402–1418, 2012, doi: 10.1016/j.neuroimage.2012.02.084.
I. Csapo, B. Davis, Y. Shi, et al., “Longitudinal image registration with non-uniform appearance change,” in Proc. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), Nice, France, 2012, pp. 280–288, doi: 10.1007/978-3-642-33454-2_35.
I. Csapo, B. Davis, Y. Shi, et al., “Longitudinal image registration with non-uniform appearance change,” Med. Image Comput. Comput.-Assist. Interv., pp. 280–288, 2012, doi: 10.1007/978-3-642-33454-2_35.
X. Li, L. R. Arlinghaus, A. B. Chakravarthy, et al., “Early DCE-MRI changes after longitudinal registration may predict breast cancer response to neoadjuvant chemotherapy,” in Proc. Biomed. Image Registration, Nashville, TN, USA, 2012, pp. 229–235, doi: 10.1007/978-3-642-31340-0_24.
C. Mattusch, U. Bick, F. Michallek, et al., “Development and validation of a four-dimensional registration technique for DCE breast MRI,” Insights Imaging, vol. 14, Art. no. 17, 2023, doi: 10.1186/s13244-022-01362-w.