Case Based Color Calibration of Wound Images for Monitoring Pressure Sore Healing

In this paper a case based calibration technique is proposed for monitoring and assessment of wound images. For development of the proposed approach digital color images were obtained from pressure sores, induced in guinea pigs. Wound images were calibrated with respect to the hue component of the healthy tissue of each case. Hue polar histograms were used for calibration. Images were reconstructed after calibration based on their new hue values. The calibrated images can be segmented by thresholding the hue values.

[1]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[2]  Peter Plassmann,et al.  A Structured Light System For Measuring Wounds , 1995 .

[3]  L. Dubertret,et al.  Quantitative and kinetic evolution of wound healing through image analysis , 2000, IEEE Transactions on Medical Imaging.

[4]  A. Venot,et al.  Assessment of healing kinetics through true color image processing , 1993, IEEE Trans. Medical Imaging.

[5]  P. Plassmann,et al.  An instrument to measure the dimensions of skin wounds , 1995 .

[6]  Stephen J. Sangwine,et al.  A colour histogram clustering technique for tissue analysis of healing skin wounds , 1997 .

[7]  B Belem.  Non-invasive wound assessment by image analysis. , 2004 .

[8]  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.

[9]  Jong-Soo Choi,et al.  Design and implementation of color correction system for images captured by digital camera , 2008, IEEE Transactions on Consumer Electronics.

[10]  Marina Kolesnik,et al.  Multi-dimensional Color Histograms for Segmentation of Wounds in Images , 2005, ICIAR.

[11]  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.

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

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

[14]  Raimondo Schettini,et al.  A New Method for RGB to XYZ Transformation Based on Pattern Search Optimization , 2007, IEEE Transactions on Consumer Electronics.

[15]  M. Nischik,et al.  Analysis of skin erythema using true-color images , 1997, IEEE Transactions on Medical Imaging.

[16]  Sahar Moghimi,et al.  Assessment of Artificially Induced Pressure Sores Using a Modified Fractal Analysis , 2009 .

[17]  Paul Anthony Iaizzo,et al.  Wound status evaluation using color image processing , 1997, IEEE Transactions on Medical Imaging.

[18]  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.