Persistent coordinates system for image processing
A computer-implemented method and system are disclosed for persistent anatomical localization of surface features on a human body across multiple imaging sessions, poses, and time points. The method generates a template mesh representing a human body surface and determines subject pose from image data using pose estimation cues including joint markers, silhouettes, or other lightweight anatomical indicators. A location of interest is encoded within a local coordinate frame associated with an anatomical segment and projected onto the template mesh to generate a persistent spatial coordinate. The persistent spatial coordinate enables reversible mapping between image space and template mesh space, allowing consistent localization of the anatomical feature across different views and sessions. The persistent spatial coordinate is generated without requiring deformable mesh reconstruction at the time of initial localization.
1 . An anatomical localization method for generating persistent coordinates of a location on a human body across multiple digitally generated images acquired at different time points, the method comprising:
generating a template mesh representing a surface of a human body, the surface including predefined anatomical regions and reference structures;
determining a pose of a human subject from image data comprising at least one of RGB images, RGB-D images, video images, infrared images, or volumetric image data;
segmenting the human body into a plurality of rigid or semi-rigid anatomical segments based on at least one of the determined pose or image-derived geometry;
encoding a location on the human body by computing coordinates of the location within a local coordinate frame defined for one of the anatomical segments using reference geometry derived from the image data;
projecting the encoded coordinates onto the template mesh to generate a persistent spatial coordinate defined in a coordinate space of the template mesh, the persistent spatial coordinate being invariant to changes in subject pose across the different time points;
re-projecting the persistent spatial coordinate from the template mesh into subsequently acquired images or imaging modalities to enable at least one of feature tracking, localization, or annotation propagation over time; and
selectively refining the persistent spatial coordinate to improve spatial consistency across the different time points,
wherein the encoding and projecting steps are performed without constructing a subject-specific deformable mesh of the human subject at the time the persistent spatial coordinate is generated.
2 . The method of claim 1 , wherein each individual subject is represented using the same generic template mesh.
3 . The method of claim 1 , wherein determining the pose of the human subject comprises of keypoint detection, wherein silhouette estimation for contour estimation and two-dimensional body region segmentation for determining rigid body boundaries may be configured but are not required.
4 . The method of claim 1 , wherein encoding the location comprises computing spatial coordinates relative to pose estimation landmarks using at least one of: pairwise triangulation, barycentric coordinates, affine coordinates, polar coordinates, or surface geodesic coordinates on the template mesh.
5 . The method of claim 1 , wherein projecting the encoded coordinates comprises aligning non-rigid mesh body parts to the determined pose using at least one of: skeletal kinematic transformations, Perspective-n-Point optimization, or Iterative Closest Point refinement as an affine transformation prior to encoding.
6 . The method of claim 1 , wherein alignment accuracy is further improved by using image-derived silhouette boundaries, texture-based deformations, or anatomical prior constraints to provide further contextual information for coordinate encoding by constraining encoded points in conjunction with the pose estimation landmark references.
7 . The method of claim 1 , wherein re-projecting the persistent spatial coordinate is performed in real time for video sequences or live image streams.
8 . The method of claim 1 , wherein the persistent spatial coordinate enables multimodal reference mapping between different imaging devices, acquisition times, or imaging modalities.
9 . The method of claim 1 , wherein the location corresponds to a skin lesion, abrasion, surgical marker, or other surface characteristic.
10 . The method of claim 1 , wherein selectively refining the persistent spatial coordinate comprises:
mapping a plurality of persistent coordinates associated with two different time points onto the template mesh;
generating correspondence signals for the persistent coordinates at each time point based on at least one of diffused locational signals, texture, color, lesion confidence, lesion severity, or disease classification; and
computing a flow field on the template mesh from the correspondence signals to refine alignment of the persistent coordinates between the two time points.