Abstract: A method and apparatus for ultra-high resolution total body photography is provided. The method uses one or more depth cameras to do an initial scan of the subject. The scan captures depth information unique to the subject that will be used by scanning software to compute the flight path and angular position of all cameras during the scan. Given a 3D mesh of the subject obtained by the depth cameras, an Expectation-Minimization (EM) algorithm is used to assign points on the mesh uniquely to cameras and then solve for a focus distance for each camera given the associated point set.

Abstract: The invention relates to a method and apparatus for high resolution total body photography (TBP). The inventive method uses a multi-stage keypoints focused pipeline that begins with blob detection to rapidly and coarsely localize potential lesions within high-resolution TBP images. Once these regions of interest are identified, a deep learning classifier evaluates them for malignancy risk. Acknowledging that wide-field imaging can compromise classification precision due to variability in resolution and appearance, the process is further refined by integrating ugly duckling analysis and t-SNE clustering. The ugly duckling detection process groups suspicious regions across all images, effectively highlighting clusters of high-risk candidates for further clinical review utilizing the method of the invention.