Lemahieu, Ignace

Naeyaert, Jean

Vander Haeghen, Yves

Undetermined

Ghent University Academic Bibliography

1Towards Consistent Color Image Acquisition inDermatologyYves Vander Haeghen*,***, Prof. Dr. J.M. Naeyeart**, Prof. Dr. I. Lemahieu**Dept. Engineering, ELIS-MEDISIP, University Gent, St.-Pietersnieuwstraat 41, 9000 Gent,Belgium**Dept. Dermatology, University Hospital, De Pintelaan 185, 9000 Gent, Belgium***E-mail: Yves.VanderHaeghen@rug.ac.beAbstract— We propose a simple, quick and practicalcolor calibration procedure that results in a precise (re-producible), albeit not very accurate (close to values ob-tained with a reference device), color description in astandard device-dependent color space (sRGB). The sys-tem consists of a 3-chip CCD camera, a continuous ring-light, frame grabber and PC. The calibration procedureinvolves building a profile of the acquisition system basedon the 24 color targets. This profile is easily checked be-fore a set of image acquisitions and remains valid overa long period. The acquired images are transformedfrom the device-dependent RGB camera space to thegamma corrected sRGB (or ITU-R BT.709) space andare readily displayable on a CRT-based monitor. More-over, sRGB tristimulus values are readily transformedthe CIE L*a*b* space, allowing percetual color differ-ences ∆E∗ab to be computed. The field of view (FOV) ofthe system is 1.5 by 2.0 cm, and the resolution is around38 pixels/mm for a 570 by 760 pixel image. Althoughthe accuracy of the proposed procedure is not very high(∆E∗ab > 10 for some of the MBCCC targets), the preci-sion or reproducibility for those targets is quite good withan average error of 〈∆E∗ab〉 = 0.34, and a maximal errorof ∆E∗ab < 1.2. For in vivo measurements typical maxi-mal error is more like ∆E∗ab < 2.2 based on preliminaryfindings, mainly because of the difficulty in determiningconsistent regions of intrest (ROI’s) in different images.Keywords—Color calibration, Dermatology, Color im-age acquisition, camera calibration, CCD cameraI. IntroductionIN dermatology color and color difference often conveyimportant diagnostic information, especially when in-vestigating pigmented lesions and more particularly skincancer. In order to make quantitative color measure-ments on irregular and variable sized skin lesions, stan-dard chromameters or spectrophotometers are generallyuseless because of their fixed aperture. Traditional pho-tography has the benefit of having a visual record ofthe lesion, but doesn’t have very consistent color re-production due to differences in film, illumination anddevelopment. Although in digital photography no filmis involved, it not straightforward to obtain a constantresponse from a digital image acquisition system [1],[2]. Moreover, most digital cameras are not colorimet-ric, i.e. their color sensors don’t have spectral responsefunctions that are proportional to the CIE x¯ y¯ z¯ colormatching functions (CIE stands for ’Commission Inter-nationale de l’Eclairage’, a standardizing body in thefield of color science). This means it is not easy tocompute perceptual color differences because the rela-tion between the device-dependent camera RGB spaceand the device-independent CIE XYZ space is unknownand has to be determined. This subject was alreadyextensively covered in the literature [3], [4], [2], [5], [6],[7], [8], [9], but here the emphasis is clearly on a sim-ple and practical scheme, making no pretense at being acolor appearance model or at color constancy. Instead itavoids some of the problems concerning changes in illu-minant between source (camera RGB) and output space(sRGB). The use of digital photography in dermatologyhas already been investigated several times, mostly us-ing device-dependent color spaces, e.g. RGB and HSV[10], [11], [12], [13]. Sometimes device-independent colorspaces were used [14], although it is unclear just how thenecessary transform was obtained. At present no sys-tem for use in dermatology has been proposed that usesa standard color space with known primaries and whitepoint. Such a color space has the additional benefit ofallowing the interchange of images for more than justviewing purposes, and opens perspectives in the area oftelemedicine.II. The acquisition systemThe acquisition system consists of a JVC KY-55B 3-chip CCD camera with a Pentax manual zoom lens, aSchott1 KL1500 150 Watt halogen light source and anIntegral Technologies2, FlashPoint 128 frame grabber.The field of view of the CCD camera is 2.0 cm by 1.5cm. With an image containing 760 by 570 pixels the res-olution is 38 pixels/mm. The light source is linked by a2 m long optical fiber to a continuous ring-light that fitsaround the zoom lens. A blue filter changes the colortemperature of the light source from 2800 K to around5100 K. The frame grabber is fitted in a standard 1501Shott Glaswerke, Hagenauer strasze 38, D-65203 Wiesbaden2Integral Technologies, Inc, 9855 Crosspoint blvd, Suite 126,Indianapolis, Indiana, 46256 USA2MHz Pentium PC running Windows 95, and acquisi-tion is done using the PAL analog RGB format whichis digitized with 8-bit precision per color channel. Thesettings of the CCD camera can be adjusted through theserial port of the PC. The settings of the frame grabberare controlled using the FlashPoint Software Developer’sToolkit 3.0 from Integral Technologies. The color tar-gets are taken from the MacBeth Color Checker Chart3(MBCCC).III. Calibration of acquisition systemThe calibration of the whole acquisition system con-sists of several consecutive steps: determination of thecamera offset, the frame grabber offset, the frame grab-ber gain, the camera aperture, the color gains of thecamera, the linearizing look-up table, and finally thetransform from the device-dependent RGB space to thedevice-independent CIE XYZ space. All these settingsare stored in a so-called profile of the acquisition system.An important issue in these steps, besides consistencyin time, is to maximize the dynamic range of the cam-era and frame grabber. This means that for a per-fectly diffuse reflector and given a certain illuminant thecamera RGB output voltages should be at their max-imum, and the corresponding digitized values shouldbe (255, 255, 255). We also have to make sure that aperfectly dark scene should result in zero output volt-ages at the camera and digitized values of (0, 0, 0). Anynon-linearity of acquisition system (mainly the camerasensors) with regard to luminance is removed using alook-up table (LUT). For more details and the individ-ual calibration steps, see [15].The RGB to XYZ transform is determined by a least-squares method based on measurements of the MBCCCtargets with known XYZ tristimuli. It is clear that min-imizing the mapping errors in a more perceptual colorspace, e.g. R*G*B* or CIE L*a*b*, would be far bet-ter, but this means tackling non-linear optimization ina multidimensional space. Properly mapping neutralcolor is very important for proper color reproduction,see [16], and thus the white-point preserving transfor-mation proposed in [6] performs very well, especiallyunder maximum ignorance assumptions. However, asthe proportion of neutral color targets used in deter-mining T is quite large there will be a ’natural’ bias inthe least-squares procedure towards properly mappingneutral colors. Moreover, the maximum ignorance as-sumption may not be the best premise in the case ofskin imaging.3Macbeth, 405 Little Britain rd, New Windsor, NY 12553-6148,USAFig. 1. The different color spaces of the calibration scheme andtheir relations.IV. Checking and adjusting an existingacquisition system calibration profileOnce a profile is determined and stored it can be usedas long as the acquisition system doesn’t change toomuch (aging of the light source bulb, changes in cam-era sensor spectral response, ...). This is easily checkedby comparing the CIE L*a*b* value of a test target(MBCCC ’white’) with its value during the calibrationprocedure. We set a limit of 1∆E∗ab unit as the maxi-mum deviation for the acceptance of the profile, as this isgenerally accepted to be lowest visable perceptual colordifference. Images acquired with a validated and ad-justed profile are transformed to the gamma correctedsRGB space (sR*G*B*) before they are stored, see fig. 1and fig. 5 for an example. This is called output render-ing and should provide a fairly realistic image on anymodern CRT-based monitor which has its white-pointset at 6500 K [17].V. Experimental results and discussionWith accuracy we mean the consistency with whichmeasurements of colors are close their real measuredvalues, as measured by a reference instrument, e.g. aspectrophotometer. Good accuracy will be especiallyimportant when exchanging images. Experiments con-sisted of 19 measurements of the MBCCC targets underdifferent profiles and after different warmup-times of theacquisition system. For each target t the average error〈∆E∗ab(t)〉 was computed with regard to the real CIEL*a*b* value of the target, see fig. 2. Two conclusions3present themselves: firstly, the RGB to CIE XYZ trans-form does indeed perform well for neutral colors (targets19 to 24), and secondly, the greatly varying results be-tween the targets indicates that, as expected, the min-imization of mapping errors in the CIE XYZ space isnot such a good idea. Averaged over all the MBCCCtargets we get 〈∆E∗ab〉 = 4.1 with ∆E∗ab < 12. It hasto be noted that proper accuracy measurements shoulduse other targets than the ones used in the determina-tion of the acquisition system profile. Real accuracymay therefore be lower than presented here. With pre-Fig. 2. The average accuracy of the MBCCC color targets. Theerror bars represent one sample standard deviationcision we describe the reproducibility of measurements,or the way repeated measurements are spreaded aroundthe average of those measurements. Precision will beimportant for any quantitative measurement of imagecolor characteristics. We can distinguish several typesof precision: short-term precision when making severalconsecutive measurements of the same target, medium-term or profile precision when comparing measurementsmade under one profile, and long-term or inter-profileprecision when talking about the agreement betweenmeasurements made under different profiles.Short term precision based on 20 consecutive measure-ments of the MBCCC ’white’ target was very good:〈∆E∗ab(white)〉 = 0.04, with ∆E∗ab(white) < 0.1. Theresults for the medium-term and long-term precisioncan be seen in fig. 3 and 4. Here the average error〈∆E∗ab(t)〉 for each target t was computed with regardto the average sample CIE L*a*b* value of the target(10 and 9 measurements respectively). To simulate pos-sible long-term changes in the acquisition system, thecolor temperature of the light source was modified forhalf of the profiles of fig. 4. There was no noticeabledifference in precision between profiles for the normaland modified acquisition system. The average, samplestandard deviation and maximal perceptual color differ-ences over all the MBCCC targets for medium-term andlong-term precision are 〈∆E∗ab〉 = 0.34, sE∗ab = 0.094with ∆E∗ab < 1.2, and 〈∆E∗ab〉 = 0.30, sE∗ab = 0.10 with∆E∗ab < 1.2 respectively. Finally, 4 measurements ofFig. 3. The average medium-term precision of the MBCCC colortargets. The error bars represent one sample standard devia-tionFig. 4. The average long-term precision of the MBCCC color tar-gets. The error bars represent one sample standard deviationthe same area of human skin were made: the first twowith the same acquisition system profile and during thesame warm-up cycle, the third on a different warm-upcycle, and the fourth using a newly created profile. Be-cause it is very difficult to obtain a pixel-precise align-ment of the acquired images, average CIE L*a*b* values4〈L ∗ a ∗ b∗〉 sL∗a∗b∗MBCCC (94.49, -0.26, 0.22)Calib Im1,Im2,Im3(94.95, -0.28, -0.53)Calib Im4 (94.86, -0.18, -0.60)Im1:White (92.16, -0.25, -1.26) (0.47, 0.89, 0.72)Im2:White (92.22, -0.14, -1.3) (0.45, 0.88, 0.69)Im3:White (91.92 -0.12, -1.12) (0.49, 0.94, 0.76)Im4:White (92.11 -0.12, -1.05) (0.43, 0.81, 0.66)Im1:Flesh (70.52, 10.87, 8.56) (3.35, 2.23, 3.27)Im2:Flesh (70.80, 10.32, 8.72) (3.44, 1.99, 2.81)Im3:Flesh (72.47, 7.54, 8.88) (3.20, 1.94, 2.79)Im4:Flesh (70.95, 8.37, 10.75) (3.24, 1.83, 2.47)Im1:Naevus (45.94, 23.72, 19.24) (8.21, 5.70, 6.78)Im2:Naevus (46.83, 23.26, 19.45) (7.80, 5.41, 6.39)Im3:Naevus (47.03, 21.30, 20.58) (7.74, 5.65, 6.45)Im4:Naevus (48.25, 21.36, 21.59) (7.71, 5.15, 5.69)TABLE IThe average CIE L*a*b* values of the different regions in the 4images and their sample standard deviations. The first 3 linesare the real and the values at calibration of the white targetincluded in the skin images.were compaired over three roughly feature-wise equal re-gions of intrest (ROI’s) of the different images. TheseROI’s are labeled ’naevus’, ’flesh’ and ’white’, see fig. 5.The ’white’ ROI is equal to one of the MBCCC targets,and therefore its theoretical L*a*b* value as well as itsvalue during both calibrations, once for image 1 to 3 andonce for image 4, is known. The results of these mea-Fig. 5. The skin image and the ROI’s used in the computationssurements can be seen in table I. The standard deviationis based on the sample variance-covariance matrix of theCIE L*a*b* values of the pixels in each ROI. As suchit is a measure of how well the ROI can be representedby an average value, and concurrently of possible errorswhen compairing these average values due to ROI mis-matches in the different images, e.g. due to the diffusenature of the border of the ’naevus’ ROI. Table II showsthe values in table I averaged over the 4 images. The’white’ ROI yields very reproducible results, and with acolor difference of the same order as in fig. 4. The factthat the lightness L* is a little low is probably due to〈L ∗ a ∗ b∗〉 sL∗a∗b∗White (92.10, -0.16, -1.19) (0.13, 0.06, 0.13)Flesh (71.69, 9.28, 9.23) (1.21, 1.58, 1.02)Naevus (49.01, 22.41, 20.21) (0.95, 1.26, 1.09)〈∆E∗ab〉 Max(∆E∗ab)White 0.16 0.20Flesh 1.60 2.13Naevus 1.91 2.18TABLE IIThe average CIE L*a*b* values over the 4 images in table I,their sample standard deviations and the average and maximalpercepual color difference.the fact that the target was not measured in the centerof the images. This indicates a non-uniformity in thelightsource which can easily be corrected in future usingan image of the ’white’ target. The other 2 ROI’s resultin standard deviations which are still small compairedto those in table I, and in ∆E∗ab < 2.2. This means thatwith a little care in the selection of the ROI’s it must bepossible to make quite precise and repeated colorimet-ric measurements of certain skin areas. It is clear thatthese are preliminary findings, and that more in vivomeasurements have to be made, especially concerningthe long-term follow-up (months, even years) of certainskin areas, e.g. melanocytic naevi in patients with anincreased probability of malignant melanoma.VI. ConclusionsWe have proposed a color calibration procedure whichallows colorimetrically consistent acquisition of digitalimages. These images are stored in a standard colorspace with known primaries and white-point, and assuch can be exchanged and compaired with other im-ages in the same color space, even if acquired by othermeans. They are readily displayable on CRT-based dis-plays. The feasability of making quite precise and re-peated colorimetric measurements of certain skin areashas been demonstrated, and will be the topic of furtherresearch.VII. AcknowledgmentThis project is funded by the FWO in Belgium, ref.F7801References[1] G. E. Healey and R. Kondepudy, “Radiometric CCD cam-era calibration and noise estimation,” IEEE Transcations onPattern Analysis and Machine Intelligence, vol. 16, no. 3,pp. 267–276, 1994.[2] Y.-C. Chang and J. F. Reid, “RGB calibration for color imageanalysis in machine vision,” IEEE Transactions on ImageProcessing, vol. 5, no. 10, pp. 1414–1422, 1996.[3] B. A. Wandell, “The synthesis and analysis of color images,”5IEEE Transactions on Pattern Analysis and Machine Intel-ligence, vol. PAMI-9, no. 1, pp. 2–13, 1987.[4] D. Slater and G. Healey, “The illumination-invariant recog-nition of 3d objects using local color invariants,” IEEETransactions on Pattern Analysis and Machine Intelligence,vol. 18, no. 2, pp. 206–210, 1996.[5] G. Finlayson, “Color in perspective,” IEEE Transactions onPattern Analysis and Machine Intelligence, vol. 18, no. 10,pp. 1034–1038, 1996.[6] G. D. Finlayson and M. S. Drew, “White-point preservingcolor correction,” in Fifth Color Imaging Conference: ColorScience, Systems and Applications, pp. 258–261, IS&T, SID,,1997.[7] E. Boldrin, P. Campadelli, and R. Schettini, “Learningcolkor appearance models,” in Fifth Color Imaging Confer-ence: Color Science, Systems and Applications, pp. 173–176,IS&T, SID,, 1997.[8] P. M. Hubel, J. Holm, G. D. Finlayson, and M. S. Drew,“Matrix calculations for digital photography,” in Fifth ColorImaging Conference: Color Science, Systems and Applica-tions, pp. 105–111, IS&T, SID,, 1997.[9] H. Kang, Color Technology for Electronic Imaging Devices.SPIE Optical Engineering Press, 1997.[10] N. Cascinelli et al., “A possible tool for clinical diagno-sis of melanoma: The computer,” Journal of the AmericanAcademy of Dermatology, vol. 16, no. 2, Part 1, pp. 361–367,1987.[11] J. L. Stone, R. L. Peterson, and J. E. Wolf, “Digital imag-ing techniques in dermatology,” Journal of the AmericanAcademy of Dermatology, no. 5, part 1, pp. 913–917, 1990.[12] R. Kenet et al., “Clinical diagnosis of pigmented lesions usingdigital epiluminescence microscopy,” Arch. of Dermatology,vol. 129, pp. 157–174, 1993.[13] W. Stolz et al., “Improvement of monitoring of melanocyticskin lesions with the use of a computerized acquisition andsurveilland with a skin surface microscopic television cam-era,” Journal of the American Academy of Dermatology,vol. 35, no. 2, Part 1, pp. 202–207, 1996.[14] T. Schindewolf et al., “Evaluation of different image acquisi-tion techniques for a computer vision system in the diagnosisof malignant melanoma,” Journal of the American Academyof Dermatology, vol. 31, no. 1, pp. 33–41, 1994.[15] Y. Vander Haeghen et al., “Consistent digital color imageacquisition of the skin,” in In review: EMBSC 98, Oct.-Nov.,Hong Kong, EMBS, 1998.[16] R. Hunt, The Reproduction of Colour. Fountain Press, fifthedition ed., 1995.[17] J. Holm, “Issues relating to the transformation of sensor datainto standard color spaces,” in Fifth Color Imaging Confer-ence: Color Science, Systems and Applications, pp. 290–295,IS&T, SID,, 1997.

Towards consistent color image acquisition in dermatology in Proceedings of the international congress on image science, ICPS '98, Antwerpen, 7-11/9/98, vol. 2, pp. 377-

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Towards consistent color image acquisition in dermatology in Proceedings of the international congress on image science, ICPS '98, Antwerpen, 7-11/9/98, vol. 2, pp. 377-

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