Automatic colorimetric calibration of human wounds
- Sven Van Poucke†1Email author,
- Yves Vander Haeghen†2,
- Kris Vissers3,
- Theo Meert4 and
- Philippe Jorens5
© Van Poucke et al; licensee BioMed Central Ltd. 2010
Received: 12 March 2009
Accepted: 18 March 2010
Published: 18 March 2010
Recently, digital photography in medicine is considered an acceptable tool in many clinical domains, e.g. wound care. Although ever higher resolutions are available, reproducibility is still poor and visual comparison of images remains difficult. This is even more the case for measurements performed on such images (colour, area, etc.). This problem is often neglected and images are freely compared and exchanged without further thought.
The first experiment checked whether camera settings or lighting conditions could negatively affect the quality of colorimetric calibration. Digital images plus a calibration chart were exposed to a variety of conditions. Precision and accuracy of colours after calibration were quantitatively assessed with a probability distribution for perceptual colour differences (dE_ab). The second experiment was designed to assess the impact of the automatic calibration procedure (i.e. chart detection) on real-world measurements. 40 Different images of real wounds were acquired and a region of interest was selected in each image. 3 Rotated versions of each image were automatically calibrated and colour differences were calculated.
1st Experiment: Colour differences between the measurements and real spectrophotometric measurements reveal median dE_ab values respectively 6.40 for the proper patches of calibrated normal images and 17.75 for uncalibrated images demonstrating an important improvement in accuracy after calibration. The reproducibility, visualized by the probability distribution of the dE_ab errors between 2 measurements of the patches of the images has a median of 3.43 dE* for all calibrated images, 23.26 dE_ab for all uncalibrated images. If we restrict ourselves to the proper patches of normal calibrated images the median is only 2.58 dE_ab! Wilcoxon sum-rank testing (p < 0.05) between uncalibrated normal images and calibrated normal images with proper squares were equal to 0 demonstrating a highly significant improvement of reproducibility. In the second experiment, the reproducibility of the chart detection during automatic calibration is presented using a probability distribution of dE_ab errors between 2 measurements of the same ROI.
The investigators proposed an automatic colour calibration algorithm that ensures reproducible colour content of digital images. Evidence was provided that images taken with commercially available digital cameras can be calibrated independently of any camera settings and illumination features.
Chronic wounds are a major health problem, not only because of their incidence, but also because of their time- and resource-consuming management. This study was undertaken to investigate the possible use of colorimetric imaging during the assessment of human wound repair. The outline design of the current study is based on the system requirements for colorimetric diagnostic tools, published previously [1, 2].
Almost all colours can be reconstructed using a combination of three base colours; red, green and blue (RGB) . Together, these three base colours define a 3-dimensional colour space that can be used to describe colours.
The accurate handling of colour characteristics of digital images is a non-trivial task because RGB signals generated by digital cameras are 'device-dependent', i.e. different cameras produce different RGB signals for the same scene. In addition, these signals will change over time as they are dependent on the camera settings and some of these may be scene dependent, such as the shutter speed and aperture diameter. In other words, each camera defines a custom device-dependent RGB colour space for each picture taken. As a consequence, the term RGB (as in RGB-image) is clearly ill-defined and meaningless for anything other than trivial purposes. As measurements of colours and colour differences in this paper are based on a standard colorimetric observer as defined by the CIE (Commission Internationale de l'Eclairage), the international standardizing body in the field of colour science, it is not possible to make such measurements on RGB images if the relationship between the varying camera RGB colour spaces and the colorimetric colour spaces (colour spaces based on said human observer) is not determined. However, there is a standard RGB colour space (sRGB) that is fixed (device-independent) and has a known relationship with the CIE colorimetric colour spaces. Furthermore, sRGB should more or less display realistically on most modern display devices without extra manipulation or calibration (look for a 'sRGB' or '6500K' setting) . One disadvantage of sRGB is that it cannot represent all the colours detected by the human eye. We believe that finding the relationship between the varying and unknown camera RGB and the sRGB colour space will eliminate most of the variability introduced by the camera and lighting conditions.
The research has been carried out in accordance with the Helsinki Declaration; the methods used were subject to ethical committee approval (B32220083450 Commissie voor Medische Ethiek Faculteit Geneeskunde Leuven Belgium). Patients received detailed written and verbal explanation and patient authorization was required before inclusion and analysis of the images.
The purpose of the first experiment was to investigate whether camera settings or lighting conditions negatively affect the quality of the colorimetric calibration . Chronic wounds are assessed in different locations and environments. Therefore, we assessed the calibration algorithm under extreme lighting conditions and with inappropriate camera settings.
Canon Eos D10
Scene lighting (cabinet)
Camera white balance
During the calibration procedure uniform illumination is assumed, as is a reference chart as part of the image of interest. The calibration provides a means of transforming the acquired images (defined in an unknown colour space, which is normally RGB), to a standard, well-defined colour space i.e. sRGB . sRGB has a known relationship to the CIE L*a*b* colorimetric space, allowing computation of perceptual colour differences. The CIE L*a*b* colorimetric space, or CIELAB space with coordinates L*, a* and b*, refers to the colour-opponent space; L* refers to Luminance, a* and b* refer to the colour-opponent dimensions [34–36]. The 'detection of the MBCCC' in the digital image can be done manually or automatically. The algorithm behind MBCCC detection is based on the initial detection of all the bright areas in an image (areas with pixel values close to 255), followed by a shape analysis. Shapes that are not rectangular, and either too small or too large compared with the image dimensions, are discarded (in pixel, we do not know the real dimension yet). The remaining areas are candidates for the MBCCC white patch. For each of the white patch candidates, the corresponding MBCCC black patch is searched for, taking into account the typical layout of the colour chart and the dimensions of the white patch candidate. If this succeeds, the patches are checked for saturation (average pixel value > 255-δ or < \delta with \delta a small number, e.g. 3) in each of the colour channels individually. If the number of saturated patches is acceptable (typically fewer than 6 out of 24 patches), calibration proceeds and its quality is assessed. Quality assessment consists of examining various conditions relating to the colour differences between the known spectrophotometric and the computed sRGB values, in accepted and rejected patches. If any of these tests fail, the algorithm rejects the calibration and continues the search.
In this experiment precision is defined as a measure of the proximity of consecutive colour measurements on an image of the same subject. This is also known as reproducibility. The precision of the MBCCC chart detection, together with the calibration process, were evaluated by computing the perceptual colour differences between all the possible pairs of measurements of each colour square of the MBCCC chart. These perceptual colour differences are expressed in CIE units, and are computed using the Euclidean metric in the CIE L*a*b* colour space. Theoretically, one unit is the 'just noticeable colour difference' and anything above five units is 'clearly noticeable'.
The accuracy of a procedure is a measure of how close its results are to the 'real' values, i.e. those obtained using the 'standard' procedure or measurement device. For colour measurements this would be a spectrophotometer. Consequently, the accuracy of the chart detection and colour calibration can be assessed by computing the perceptual colour differences between the measurements of the colour squares of the MBCCC chart and the spectrophotometric values of these squares. For this assessment the calibration was performed using half the colour patches of the MBCCC chart, while the other half were utilised in evaluation of accuracy. Accuracy is likely to be higher when the whole chart is used for calibration purposes. Precision and accuracy result in a probability distribution for the dE_ab errors. Tukey's five-number summary of the dE_ab colour differences of each patch was also calculated and visualized using a box plot (the minimum, the lower quartile, the median, the upper quartile and the maximum). Wilcoxon rank-sum statistics were used to test the calibration, which compares the locations of two populations to determine if one population has been shifted with respect to another. A sum of ranks comparison, which works by ranking the combined data sets and summing the ranks for each dE_ab, was utilised to compare the sum of the ranks with significance values based on the decision alpha (p < 0.05).
Digital images (n = 40) of the chronic wounds were taken using a Sony Cybershot DSC-F828 digital camera (8.0 million effective pixels) and Carl Zeiss 28 - 200 mm equivalent lens, with fully automatic settings at different indoor locations, as is usually the case in daily clinical practice.
Calibration Procedure and Analysis
The calibration procedure was carried out in accordance with that recorded for experiment 1. The dE_ab colour differences between the average colour of the ROI of the four rotated versions of each image were computed and visualized using a probability distribution graph.
Colour differences between the measurements and real spectrophotometric measurements revealed median dE_ab values of 6.40 for proper patches of calibrated normal images and 17.75 for uncalibrated images, respectively, demonstrating an important improvement in accuracy after calibration (Figure 10). The result for the patches used in the calibration was also included, and they had a median of 1.59 dE_ab.
Figure 12 presents the accuracy box plot for the proper patches of the normal images. As mentioned above, we could only use patches that had not been used in computing the calibration in order to check accuracy, therefore only 12 patches are shown in this figure.
As figure 11 demonstrates, the reproducibility, visualized by the probability distribution of the dE_ab errors between two measurements of the patches of the images, had a median of 3.43 dE* for all calibrated images, 23.26 dE_ab for all uncalibrated images, a median of 2.83 dE_ab for all 'normal' calibrated images, and 14.25 dE_ab for all 'normal' uncalibrated images. Restricting the calculation to the proper patches of normal calibrated images, the median was 2.58 dE_ab. Wilcoxon sum-rank testing (p < 0.05) between uncalibrated normal images and calibrated normal images with proper squares was equal to zero, demonstrating a highly significant improvement in reproducibility.
The research presented here provides evidence that images taken with commercially available digital cameras can be calibrated independently of camera settings and illumination features, provided that illumination in the field of view is uniform and a calibration chart is used. This may be particularly useful during chronic wound assessment, as this is often performed in different locations and under variable lighting conditions. The proposed calibration transforms the acquired images in an unknown colour space (usually RGB) to a standard, well defined colour space (sRGB) that allows images to be displayed properly and has a known relationship to the CIE colorimetric colour spaces. First, we challenged the calibration procedure with a large collection of images containing both 'normal' images with proper camera settings and images that were purposely over- or underexposed and/or had white balance mismatches. The reproducibility and accuracy of the calibration procedure is presented and demonstrates marked improvements. The calibration procedure works very well on the images with improper camera settings, as evidenced by the minimal differences between the error distributions of the complete set of images and the set with only the 'normal' images. An innovative feature demonstrated during our research is the automatic 'detection and calibration of the MacBeth Colour Checker Chart Mini [MBCCC]' in the digital image. Secondly, we tested the effect of this MBCCC chart detection on subsequent real-world colour measurements. Figure 14 demonstrates the probability distribution of errors between two colour measurements of the same region of interest that can be attributed to variations in the chart detection process. The majority of these errors were below 1 dE_ab, demonstrating that the chart detection is robust.
This experiment is part of the research presented by the Woundontology Consortium, which is a semi-open, international, virtual community of practice devoted to advancing the field of research in non-invasive wound assessment by image analysis, ontology and semantic interpretation and knowledge extraction http://www.woundontology.com. The interests of this consortium are related to the establishment of a community driven, semantic content analysis platform for digital wound imaging with special focus on wound bed surface area and color measurements in clinical settings. Current research by the Woundontology Consortium is related to our concerns of the interpretation of clinical wound images without any calibration or reference procedure. Therefore we are investigating techniques to promote standardization. The platform used by this Consortium is based on Wiki technology, a collaborative environment to develop a "woundontology" using the Collaborative Ontology Development Service (CODS) and an image server. Research on wound bed texture analysis is performed by a computer program: "MaZda". This application has been under development since 1998, to satisfy the needs of the participants of the COST B11 European project "Quantitative Analysis of Magnetic Resonance Image Texture" (1998-2002). Additionally, wound bed texture parameter data-mining is analyzed using "RapidMiner" which is one of the world-wide leading open-source data mining solution.
Recently, results on: THE RED-YELLOW-BLACK (R-Y-B) SYSTEM: A COLORIMETRIC ANALYSIS OF CONVEX HULLS IN THE CIELAB COLOR SPACE were presented at the EWMA 2009 conference in Helsinki, Finland.
To our knowledge, the proposed technology is the first demonstration of a fundamental, and in our opinion, essential tool for enabling intra-individual (in different phases of wound healing) and inter-individual (for features and properties) comparisons of digital images in human wound healing. By implementing this step in the assessment, we believe that scientific standards for research in this domain will be improved .
Part of this work was performed at the Liebaert Company (manufacturer for technical textile in Deinze, Belgium). We thank the members of the Colour Assessment Cabinet specially Albert Van Poucke, for the fruitful discussions.
We also would like to thank the reviewers who helped to improve this paper with their suggestions.
- Haeghen Vander Y, Naeyaert JM: Consistent cutaneous imaging with commercial digital cameras. Arch Dermatol. 2006, 142 (1): 42-46. 10.1001/archderm.142.1.42.Google Scholar
- Van Geel N, Haeghen Vander Y, Ongenae K, Naeyaert JM: A new digital image analysis system useful for surface assessment of vitiligo lesions in transplantation studies. Eur J Dermatol. 2004, 14 (3): 150-155.PubMedGoogle Scholar
- Kanthraj GR: Classification and design of teledermatology practice: What dermatoses? Which technology to apply?. 2009, Journal of the European Academy of Dermatology and Venereology, 23 (8): 865-875.View ArticlePubMedGoogle Scholar
- Aspres N, Egerton IB, Lim AC, Shumack SP: Imaging the skin. Australas J Dermatol. 2003, 44 (1): 19-27. 10.1046/j.1440-0960.2003.00632.x.View ArticlePubMedGoogle Scholar
- Bhatia AC: The clinical image: archiving clinical processes and an entire specialty. Arch Dermatol. 2006, 142 (1): 96-98. 10.1001/archderm.142.1.96.View ArticlePubMedGoogle Scholar
- Hess CT: The art of skin and wound care documentation. Advances in Skin & Wound Care. 2005, 18: 43-53.View ArticleGoogle Scholar
- Bon FX, Briand E, Guichard S, Couturaud B, Revol M, J Servant JM, Dubertret L: Quantitative and kinetic evolution of wound healing through image analysis. IEEE Trans Med Imaging. 2000, 19 (7): 767-772. 10.1109/42.875206.View ArticlePubMedGoogle Scholar
- Jury CS, Lucke TW: The clinical photography of herbert brown: a perspective on early 20th century dermatology. Clin Exp Dermatol. 2001, 26 (5): 449-454. 10.1046/j.1365-2230.2001.00856.x.View ArticlePubMedGoogle Scholar
- Phillips K: Incorporating digital photography into your wound-care practice. Wound Care Canada. 2006, 16-18.Google Scholar
- Oduncu H, Hoppe A, Clark M, Williams RJ, Harding KG: Analysis of skin wound images using digital colour image processing: a preliminary communication. Int J Low Extrem Wounds. 2004, 3 (3): 151-156. 10.1177/1534734604268842.View ArticlePubMedGoogle Scholar
- Levy JL, Trelles MA, Levy A, Besson R: Photography in dermatology: comparison between slides and digital imaging. J Cosmet Dermatol. 2003, 2: 131-134. 10.1111/j.1473-2130.2004.00081.x.View ArticlePubMedGoogle Scholar
- Tucker WFG, Lewis FM: Digital imaging: a diagnostic screening tool?. Int J Dermatol. 2005, 44 (6): 479-481. 10.1111/j.1365-4632.2005.01990.x.View ArticlePubMedGoogle Scholar
- Wagner JH, Miskelly GM: Background correction in forensic photography. ii. Photography of blood under conditions of non-uniform illumination or variable substrate color_practical aspects and limitations. J Forensic Sci. 2003, 48 (3): 604-613.PubMedGoogle Scholar
- Wagner JH, Miskelly GM: Background correction in forensic photography. i. photography of blood under conditions of non-uniform illumination or variable substrate color_theoretical aspects and proof of concept. J Forensic Sci. 2003, 48 (3): 593-603.PubMedGoogle Scholar
- Riley RS, Ben-Ezra JM, Massey D, Slyter RL, Romagnoli G: Digital photography: A primer for pathologists. J Clin Lab Anal. 2004, 18: 91-128. 10.1002/jcla.20009.View ArticlePubMedGoogle Scholar
- Palioto DB, Sato S, Ritman G, Mota LF, Caffesse RG: Computer assisted image analysis methods for evaluation of periodontal wound healing. Braz Dent J. 2001, 12: 167-172.PubMedGoogle Scholar
- Heydecke G, Schnitzer S, Türp JC: The colour of human gingiva and mucosa: visual measurement and description of distribution. Clin Oral Invest. 2005, 9: 257-265. 10.1007/s00784-005-0006-3.View ArticleGoogle Scholar
- Scheinfeld N: Photographic images, digital imaging, dermatology, and the law. Arch Dermatol. 2004, 140 (4): 473-476. 10.1001/archderm.140.4.473.View ArticlePubMedGoogle Scholar
- Gopalakrishnan D: Colour analysis of the human airway wall. 2003, Master's thesis, University of IOWAGoogle Scholar
- Lotto RB, Purves D: The empirical basis of colour perception. Consciousness and Cognition. 2002, 11: 609-629. 10.1016/S1053-8100(02)00014-4.View ArticlePubMedGoogle Scholar
- Prasad S, Roy B: Digital photography in medicine. J Postgrad Med. 2003, 49 (4): 332-336.PubMedGoogle Scholar
- Maglogiannis I, Kosmopoulos DI: A system for the acquisition of reproducible digital skin lesions images. Technol Health Care. 2003, 11 (6): 425-441.PubMedGoogle Scholar
- Haeghen Vander Y: Development of a dermatological workstation with calibrated acquisition and management of colour images for the follow-up of patients with an increased risk of skin cancer. 2001, Ph.D. thesis, University GhentGoogle Scholar
- Gilmore S: Modelling skin disease: lessons from the worlds of mathematics, physics and computer science. Australas J Dermatol. 2005, 46 (2): 61-69. 10.1111/j.1440-0960.2005.00143.x.View ArticlePubMedGoogle Scholar
- Goldberg DJ: Digital photography, confidentiality, and teledermatology. Arch Dermatol. 2004, 140 (4): 477-478. 10.1001/archderm.140.4.477.View ArticlePubMedGoogle Scholar
- Macaire L, Postaire JG: Colour image segmentation by analysis of subset connectedness and colour homogeneity properties. Computer Vision and Image Understanding. 2006, 102: 105-116. 10.1016/j.cviu.2005.12.001.View ArticleGoogle Scholar
- Streinera DL: Precision and accuracy: Two terms that are neither. Journal of Clinical Epidemiology. 2006, 59: 327-330. 10.1016/j.jclinepi.2005.09.005.View ArticleGoogle Scholar
- Feit J, Ulman V, Kempf W, Jedlickov H: Acquiring images with very high resolution using a composing method. Cesk Patol. 2004, 40 (2): 78-82.PubMedGoogle Scholar
- Byrne A, Hilbert DR: Colour realism and colour science. Behavioral and Brain Sciences. 2006, 26: 3-64.Google Scholar
- Harkness N: The colour wheels of art, perception, science and physiology. Optics and Laser Technology. 2006, 38: 219-229. 10.1016/j.optlastec.2005.06.010.View ArticleGoogle Scholar
- Multimedia systems and equipment - Colour measurement and management -Part 2-1: Colour management - Default RGB colour space - sRGB. 1999, IEC 61966-2-1 Ed. 1.0 Bilingual
- Haeghen Vander Y, Naeyaert JM, Lemahieu I, Philips W: An imaging system with calibrated colour image acquisition for use in dermatology. IEEE Trans Med Imaging. 2000, 19 (7): 722-730. 10.1109/42.875195.View ArticleGoogle Scholar
- Ikeda I, Urushihara K, Ono T: A pitfall in clinical photography: the appearance of skin lesions depends upon the illumination device. Arch Dermatol Res. 2003, 294: 438-443.PubMedGoogle Scholar
- Leon K, Mery D, Pedrischi F, Leon J: Colour measurement in l*a*b* units from rgb digital images. Food Research International. 2006, 39 (2006): 1084-1091. 10.1016/j.foodres.2006.03.006.View ArticleGoogle Scholar
- Danilova MV, Mollon JD: The comparison of spatially separated colours. Vision Res. 2006, 46 (6-7): 823-836. 10.1016/j.visres.2005.09.026.View ArticlePubMedGoogle Scholar
- Johnson GM: A top down description of s-cielab and ciede2000. Col Res Appl. 2003, 28: 425-435. 10.1002/col.10195.View ArticleGoogle Scholar
- Bellomo R, Bagshaw SM: Evidence-based medicine: classifying the evidence from clinical trials_the need to consider other dimensions. Crit Care. 2006, 10 (5): 232-10.1186/cc5045.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2342/10/7/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.