Automated Detection of New or Evolving Melanocytic Lesions Using a 3D Body Model

Detection of new or rapidly evolving melanocytic lesions is crucial for early diagnosis and treatment of melanoma. We propose a fully automated pre-screening system for detecting new lesions or changes in existing ones, on the order of 2 - 3mm, over almost the entire body surface. Our solution is based on a multi-camera 3D stereo system. The system captures 3D textured scans of a subject at different times and then brings these scans into correspondence by aligning them with a learned, parametric, non-rigid 3D body model. This means that captured skin textures are in accurate alignment across scans, facilitating the detection of new or changing lesions. The integration of lesion segmentation with a deformable 3D body model is a key contribution that makes our approach robust to changes in illumination and subject pose.

[1]  Heng Huang,et al.  A New Hybrid Technique for Dermatological Image Registration , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[2]  Rafael García,et al.  Computerized analysis of pigmented skin lesions: A review , 2012, Artif. Intell. Medicine.

[3]  H. Voigt,et al.  Topodermatographic image analysis for melanoma screening and the quantitative assessment of tumor dimension parameters of the skin , 1995, Cancer.

[4]  Michael J. Black,et al.  Home 3D body scans from noisy image and range data , 2011, 2011 International Conference on Computer Vision.

[5]  K. McMasters,et al.  Current management of melanoma. , 2013, Current problems in surgery.

[6]  Thomas Vetter,et al.  Skin Detail Analysis for Face Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Michael J. Black,et al.  FAUST: Dataset and Evaluation for 3D Mesh Registration , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Ghassan Hamarneh,et al.  A graph-based approach to skin mole matching incorporating template-normalized coordinates , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  William T. Freeman,et al.  A reliable skin mole localization scheme , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[10]  Ghassan Hamarneh,et al.  Uncertainty-Based Feature Learning for Skin Lesion Matching Using a High Order MRF Optimization Framework , 2012, MICCAI.

[11]  D A Perednia,et al.  Automated feature detection in digital images of skin. , 1991, Computer methods and programs in biomedicine.

[12]  D A Perednia,et al.  Automatic registration of multiple skin lesions by use of point pattern matching. , 1992, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.