Fusion of Multi-view Tissue Classification Based on Wound 3D Model

Region classification from a single image is no more reliable when the labeling must be applied on a 3D surface. Depending on camera viewpoint and surface curvature, lighting variations and perspective effects alter colorimetric analysis and area measurements. This problem can be overcome if a 3D model of the object of interest is available. This general approach has been evaluated for the design of a complete wound assessment tool using a simple free handled digital camera. Clinical tests demonstrate that multi view classification results in enhanced tissue labeling and more precise measurements, a significant step toward accurate monitoring of the healing process.

[1]  S. Treuillet,et al.  Supervised Tissue Classification from Color Images for a Complete Wound Assessment Tool , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  A. Hoppe,et al.  Analysis of Skin Wound Images Using Digital Color Image Processing: A Preliminary Communication , 2004, The international journal of lower extremity wounds.

[3]  Hazem Wannous,et al.  Efficient SVM classifier based on color and texture region features for wound tissue images , 2008, SPIE Medical Imaging.

[4]  C. Ozturk,et al.  A new structured light method for 3-D wound measurement , 1996, Proceedings of the IEEE 22nd Annual Northeast Bioengineering Conference.

[5]  Marco Romanelli,et al.  Technological advances in wound bed measurements , 2002 .

[6]  J. Winder,et al.  New protocol for leg ulcer tissue classification from colour images , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Wangdo Kim,et al.  Wound measurement by curvature maps: a feasibility study , 2006, Physiological measurement.

[8]  S. Treuillet,et al.  Finding Two Optimal Positions of a Hand-Held Camera for the Best Reconstruction , 2007, 2007 3DTV Conference.

[9]  Dorin Comaniciu,et al.  Robust analysis of feature spaces: color image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  B. S. Manjunath,et al.  Unsupervised Segmentation of Color-Texture Regions in Images and Video , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Huiru Zheng,et al.  Case-based tissue classification for monitoring leg ulcer healing , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[12]  Benjamin Albouy,et al.  Robust Semi-Dense Matching across Uncalibrated and Widely Separated Views , 2007, MVA.

[13]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[14]  B. S. Manjunath,et al.  An efficient color representation for image retrieval , 2001, IEEE Trans. Image Process..

[15]  D. Keast,et al.  MEASURE: A proposed assessment framework for developing best practice recommendations for wound assessment. , 2004, Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society.

[16]  Mark G. Duckworth,et al.  A Clinically Affordable Non-Contact Wound Measurement Device , 2007 .

[17]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Robert Baker,et al.  A noncontact wound measurement system. , 2002, Journal of rehabilitation research and development.

[19]  P Plassmann,et al.  MAVIS: a non-invasive instrument to measure area and volume of wounds. Measurement of Area and Volume Instrument System. , 1998, Medical engineering & physics.

[20]  Benjamin Albouy,et al.  Accurate 3D Structure Measurements from Two Uncalibrated Views , 2006, ACIVS.

[22]  Adilson Gonzaga,et al.  Segmentation and analysis of leg ulcers color images , 2001, Proceedings International Workshop on Medical Imaging and Augmented Reality.

[23]  Paolo Cignoni,et al.  Derma: Monitoring the Evolution of Skin Lesions with a 3D System , 2003, VMV.

[24]  F. A. Heuvel,et al.  Development of a Robust Photogrammetric Metrology System for Monitoring the Healing of Bedsores , 2005 .

[25]  Marina Kolesnik,et al.  Segmentation of wounds in the combined color-texture feature space , 2004, SPIE Medical Imaging.

[26]  Adam F. Cohen,et al.  PHOTOGRAMMETRIC WOUND MEASUREMENT WITH A THREE-CAMERA VISION SYSTEM , 2000 .