Accuracy of an Affordable Smartphone-Based Teledermoscopy System For

sensors

Article Accuracy of an Aﬀordable Smartphone-Based Teledermoscopy System for Color Measurements in Canine Skin

Blaž Cugmas 1,* and Eva Štruc 2 1 Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, 19 Rainis Blvd., LV-1586 R¯ıga, Latvia 2 Vetamplify SIA, Veterinary Services, 57/59–32 Krišjan¯ , a Valdemara¯ Str., LV-1010 R¯ıga, Latvia; [email protected] * Correspondence: [email protected]; Tel.: +371-67-033-848

Received: 21 October 2020; Accepted: 30 October 2020; Published: 31 October 2020

Abstract: Quality smartphone cameras and affordable dermatoscopes have enabled teledermoscopy to become a popular medical and veterinary tool for analyzing skin lesions such as melanoma and erythema. However, smartphones acquire images in an unknown RGB color space, which prevents a standardized colorimetric skin analysis. In this work, we supplemented a typical veterinary teledermoscopy system with a conventional color calibration procedure, and we studied two mid-priced smartphones in evaluating native and erythematous canine skin color. In a laboratory setting with the ColorChecker, the teledermoscopy system reached CIELAB-based color differences ∆E of 1.8–6.6 (CIE76) and 1.1–4.5 (CIE94). Intra- and inter-smartphone variability resulted in the color differences (CIE76) of 0.1, and 2.0–3.9, depending on the selected color range. Preliminary clinical measurements showed that canine skin is less red and yellow (lower a* and b* for ∆E of 10.7) than standard Caucasian human skin. Estimating the severity of skin erythema with an erythema index led to errors between 0.5–3%. After constructing a color calibration model for each smartphone, we expedited clinical measurements without losing colorimetric accuracy by introducing a simple image normalization on a white standard. To conclude, the calibrated teledermoscopy system is fast and accurate enough for various colorimetric applications in veterinary dermatology.

Keywords: color calibration; color constancy; smartphone teledermoscopy; colorchecker; erythema index; canine skin; veterinary dermatology; CIELAB color space; color correction; color calibration target

1. Introduction Smartphones have become a popular tool for monitoring human health and well-being [1]. Recent advances in device display, sensors, battery, and connectivity have enabled stand-alone detection of cardiovascular parameters (heart or respiratory rate), sleep patterns, activity levels, cognitive functions, and changed health conditions in skin and eyes [1]. In veterinary medicine, smartphones were similarly applied for activity monitoring, electrocardiography, and endoscopy in various pet and farm animals [2–4]. By attaching adapters, holders, and additional mechanical, electrical, or optical modules, smartphones can also be suitable for various laboratory applications [5]. Colorimetric analyses of urine- or blood-related strips [6] and immunochromatographic assays [7] are among the most popular. Furthermore, smartphones have been exploited as analyzers of the enzyme-linked immunosorbent assay (ELISA) microplates, microﬂuidic system modules, and microscopes [8]. Smartphones have also contributed to developing a new subfield of dermatology–smartphone-based teledermoscopy [9,10]. Optical systems joining capable smartphone cameras and mobile dermatoscopes

Sensors 2020, 20, 6234; doi:10.3390/s20216234 www.mdpi.com/journal/sensors Sensors 2020, 20, 6234 2 of 12 allow users to take and share quality images of their skin lesions, which could serve as preventive screening for cancer [9], especially where professional assistance is not available face to face. Compared to in-person dermatological services, smartphone-based teledermoscopy achieved the same sensitivity but lower specificity for detecting melanoma [11]. Furthermore, teledermoscopy can be complemented with image processing algorithms for automated skin lesion classification [12]. Rizvi and others even speculated that many regular consultations on skin lesions with the specialist could be replaced by the use of smartphone applications [13]. In human dermatology, diagnosing skin lesions usually relies on a dermatoscope, enabling detailed lesion observation according to the ABCD rules or 7-point checklist [14]. However, because melanomas in animals are less frequent and malignant, veterinarians typically employ dermatoscopes for studying other skin conditions such as alopecia (e.g., hair loss) [15]. Our research group recently used an affordable ($300) dermatoscope, the DermLite DL1 basic (3Gen, Inc., San Juan Capistrano, CA, USA) for the objective estimation of skin erythema severity in atopic dogs [16]. There was a high correlation (r = 0.82–0.85) and prediction ability (r2 = 0.72–0.73) between a dermatologist’s visual erythema estimates and an erythema index (EI), calculated directly from the image RGB values. However, each smartphone acquires images in the camera-dependent RGB color space due to the sensor’s unique spectral response, preventing standardized colorimetric skin assessments. One option to overcome this limitation is to perform a transformation into a device-independent color space (e.g., CIELAB [17]) with the help of color calibration targets (CCT). The optical systems and transformation between color spaces naturally cannot guarantee perfect color accuracy. Therefore, an error between the measured and reference color appears called color difference (∆E). The original, CIE76, Euclidian distance-based formula for ∆E estimation was updated twice (i.e., CIE94, CIEDE2000), but expanded versions are less often listed in the literature. The CIELAB color space-based color difference ∆E can be transposed to the human eye capability to spot a difference between two colors. The just-noticeable difference (JND) usually has a ∆E estimated to be 2.3, but the detection threshold can be up to 5.0 [17]. Based on the popular CCT, such as the ColorChecker, various optical systems achieved similar colorimetric accuracies. The stand-alone use of popular cameras (e.g., SX200 IS, Canon, Tokyo, Japan; D3100, Nikon, Tokyo, Japan; or ST60, Samsung Electronics Co. Ltd., Suwon, Korea) produced color differences (CIE76) in the range of 3.7–5.9 [18,19]. An attached dermatoscope (Dermlite Pro) lowered the CIE76 ∆E to between 2.5 and 4.0 [20]. Furthermore, a camera (IDS UI-2240-C, IDS Imaging Development Systems GmbH, Obersulm, Germany) attached to the microscope resulted in a CIE 94 color distance of 2.2 [21]. The color constancy of smartphones (Samsung Galaxy S4 and S7, iPhone 5S) was slightly worse, with ∆Es respectively ranging between 3.5–8.4 [22], 7.4–10.2 (CIE76) [23], and 3.0–4.4 (CIEDE2000) [24]. Finally, the optical system with a smartphone and a miniature microscope ensured the CIE 94 color difference between 2.0 and 2.4 [8]. This work’s overarching goal is to set up a color calibration on a typical veterinary smartphone-based teledermoscopy system and study the colorimetric accuracy of two mid-priced smartphone cameras on canine skin. First, we transformed the acquired data from device-dependent (RGB) to device-independent (CIELAB, sRGB) color space. We continued with estimating colorimetric accuracy in a laboratory setting on the standard color calibration target (CCT), ColorChecker. We also made preliminary measurements of canine skin in terms of CIELAB color and erythema indices (sRGB). Additionally, we evaluated color variability for each (intra-) and between both smartphones (inter-smartphone accuracy). Finally, we confirmed that a simple image normalization on the white standard could expedite clinical measurements without decreasing colorimetric accuracy. Sensors 2020, 20, 6234 3 of 12

2. Materials and Methods

2.1. OpticalSensors System 2020, 20, 6234 3 of 12

We employed2. Materials and two Methods mid-priced smartphones. The first (SP1) was a Nokia 6 smartphone (v.2017, HMD Global, Espoo, Finland) equipped with a 16 MP camera (f/2.0). JPEG images (90% quality, 2.1. Optical System 4608 3456 pixels) were acquired in the program Open Camera (v1.47.3, Mark Harman, Cambridge, UK) × with the followingWe employed settings: two mid photo-priced mode smartphones. (STD), whiteThe first balance (SP1) was (manual,a Nokia 6 smartphone fluorescent), (v.2017, scene mode HMD Global, Espoo, Finland) equipped with a 16 MP camera (f/2.0). JPEG images (90% quality, 4608 (steady photo),× 3456 pixels) color were effect acquired (none), in the ISO program (50),and Open focus Camera (auto). (v1.47.3, The Mark second Harman, smartphone Cambridge, UK (SP2)) was a Samsungwith Galaxy the following A51 (Samsung settings: photoElectronics mode (STD), Co. whiteLtd., balance Suwon, (manual, Korea) fluorescent), with a 48 scene MP mode camera (f/2.0). The images(steady (90% photo), quality, color 4000 effect3000 (none), pixels) ISO (50), were and taken focus in (auto). the Open The second Camera smart programphone (SP2) with was the a following × settings: photoSamsung mode Galaxy (STD), A51 (Samsung white balance Electronics (manual, Co. Ltd., Suwon, cloudy), Kor colorea) with eff aect 48 MP (none), camera ISO (f/2.0). (100), The and focus images (90% quality, 4000 × 3000 pixels) were taken in the Open Camera program with the following (auto). Tosettings: both smartphones, photo mode (STD), we white attached balance DermLite (manual, cloudy), DL1 basic color aeffect dermatoscope, (none), ISO (100), an and epiluminescence focus microscopy(auto). device To both with sma artphone 15 mms, we lens attached (Figure DermLite1). The DL1 illuminationbasic a dermatoscope consisted, an epiluminescence of four white LEDs with irradiancemicroscopy fluctuations device with being a 15 mm expected lens (Figure to 1 be). The up illumination to 0.9% [23 consisted]. Additionally, of four white a LEDs polarizer with reduced reflectionsirradiance from the fluctuations skin surface. being The expected camera to was be up held to 0.9%3.1 cm [23] away. Additionally, from the a skin, polarizer resulting reduced in a circular 2 reflections from the skin surface. The camera was held 3.1 cm away from the skin, resulting in a 3.0 cm samplingcircular 3.0 area. cm2 sampling area.

Figure 1. InFigure this 1. study, In this study, we applied we applied a Smartphone-based a Smartphone-based teledermatoscopy teledermatoscopy system system with a smartphone with a smartphone (one of two smartphones applied, SP1, Nokia 6 v.2017) and the DL1 basic dermatoscope for (one of two smartphones applied, SP1, Nokia 6 v.2017) and the DL1 basic dermatoscope for colorimetric colorimetric measurements in canine skin. measurements in canine skin. 2.2. Laboratory Measurements 2.2. Laboratory Measurements Two ColorCheckers (Figure 2, X-rite, Grand Rapids, MI, USA) served as the reference color Two ColorCheckerscalibration targets (CCTs). (Figure For2 testing, X-rite, color Grand calibration Rapids, models MI, on the USA) whole servedcolor range, as we the exploited reference color calibrationall targets patches (CCTs). on the Classic For testingColorChecker color (full calibration-range CCT models). Only the on white the whole and skin color patches range, (E5, weD-J exploited all patches7&8) on theon the Classic Digital ColorChecker SG ColorChecker (full-range (skin-range CCT) CCT). were Only employed the white for skin and color skin analysis patches. (E5, D-J 7&8) Figure 3 displays flowcharts of different color calibration procedures, described below. First, we on the DigitalSensorsacquired 2020 SG, 20 six ColorChecker, 6234 repeated images (skin-range of each CCT CCT)(Ifull/skin1 were–6), thus employed having 12 individual for skin image color sets. analysis. For each4 of 12 set separately, the brightest white patch (19 for the full-range and E5 for the skin-range CCT) served for data normalization and setting a fixed camera exposure time, which did not change within image set. A median value of central 750 pixels in both dimensions served for the calculation of the image’s RGB values: R (red), G (green), and B (blue).

Figure 2.FigureColorChecker 2. ColorChecker Classic Classic (left, (left, full-range full-range CCT) CCT) and and Digital Digital SG SG (right, (right, skin-range skin-range CCT). CCT). SkinSkin patches used inpatches our study used are in our placed study inside are placed the inside red square. the red square.

Figure 3. Flowcharts of different color calibration procedures. (a) Image sets (Ifull/skin1–6) of the selected CCT color patches were acquired and normalized against patches 19 (full-range CCT) or E5 (skin- range CCT). For each set independently, we built a transformation model between an unknown RGB and a standard CIELAB color space (LAB = f(RGB)). (b) Full normalization. RGB values of I2–6 were

first transformed to the RGB color space of I1 and later to the CIELAB based on the known f1. (c) Single-

point normalization. Normalized images (I2–6) with the brightest patch (No. 19) were directly transformed to the CIELAB color space based on the known f1. (d) Erythema Index (EI) was calculated after the colorimetric data was transformed to the standard RGB color space (sRGB).

For each image set (Figure 3a), we then built a linear-regression model between an unknown device-dependent RGB and a device-independent CIELAB color space: LAB = ffull/skin1–6(RGB). The mathematical model was based on the best performing polynomial from our previous study [23]:

∗ ∗ ∗ 퐿퐴퐵 = 푎0 + 푎1푅 + 푎2퐺 + 푎3퐵, {퐿 , 푎 , 푏 } ∈ 퐿퐴퐵, (1) where L* (lightness), a* (green-red), and b* (blue-yellow) are coordinates of the CIELAB color space, and a0–3 are regression coefficients, estimated with the function fitlm (Matlab, R2016a, MathWorks, Natick, MA, USA). The color difference (ΔE, CIE76) was calculated from fitting residuals as a Euclidean distance between estimated and true color [17]:

∆퐸 = √(∆퐿∗)2 + (∆푎∗)2 + (∆푏∗)2. (2) CIE94′s ΔE was estimated with the openly available Matlab code [25]. Based on the full-range CCT, we additionally tested a few modified color calibration procedures, which could improve, deteriorate, simplify, or expedite clinical measurements:

Sensors 2020, 20, 6234 4 of 12

Figure3 displays flowcharts of di fferent color calibration procedures, described below. First, we acquired six repeated images of each CCT (Ifull/skin1–6), thus having 12 individual image sets. For each set separately, the brightest white patch (19 for the full-range and E5 for the skin-range CCT) served for data normalization and setting a fixed camera exposure time, which did not change within image set. A median value of central 750 pixels in both dimensions served for the calculation of the Figure 2. ColorChecker Classic (left, full-range CCT) and Digital SG (right, skin-range CCT). Skin image’s RGB values: R (red), G (green), and B (blue). patches used in our study are placed inside the red square.

Figure 3. FlowchartsFlowcharts of ofdifferent diﬀerent color color calibration calibration procedures procedures.. (a) Image (a) Image sets (I setsfull/skin (1I–full6) of/skin the1–6 )selected of the selectedCCT color CCT patches color patcheswere acquired were acquired and normalized and normalized against againstpatches patches 19 (full 19-range (full-range CCT) or CCT) E5 (skin or E5- (skin-rangerange CCT). CCT). For each For set each independently, set independently, we built we builta transformation a transformation model model between between an unknown an unknown RGB RGBand a and standard a standard CIELAB CIELAB color color space space (LAB ( LAB= f(RGB= f ()).RGB (b))). Full (b) n Fullormalization. normalization. RGB RGBvalues values of I2– of6 wereI2–6 werefirst transformed ﬁrst transformed to the toRGB the color RGB space color of space I1 and of laterI1 and to the later CIELAB to the CIELABbased on basedthe known on the f1. known(c) Singlef 1-. (pointc) Single-point normalization. normalization. Normalized Normalized images images (I2–6) with (I2–6) withthe brightest the brightest patch patch (No. (No. 19) 19) were were directly transformedtransformed toto thethe CIELABCIELAB colorcolor spacespace basedbased on on thethe knownknownf f11.(. (d)) Erythema Index (EI) was calculated afterafter thethe colorimetriccolorimetric datadata waswas transformedtransformed toto thethe standardstandard RGBRGB colorcolor spacespace (sRGB).(sRGB).

For eacheach imageimage setset (Figure(Figure3 3a),a), wewe then then built built a a linear-regression linear-regression model model between between an an unknown unknown LAB f RGB device-dependentdevice-dependent RGB and and a a device device-independent-independent CIELAB CIELAB color color space: space: LAB = f=full/skinfull1–/skin6(RGB1–6(). The). Themathematical mathematical model model was wasbased based on the on best the best performi performingng polynomial polynomial from from our ourprevious previous study study [23]: [23 ]:

∗ ∗ ∗ 퐿퐴퐵 =LAB푎0 +=푎a1푅++a푎R2퐺++a 푎G3퐵+, a B , {퐿L,∗,푎a∗,,푏b∗} ∈ LAB퐿퐴퐵,, (1) (1) 0 1 2 3 { } ∈ where L* (lightness), a* (green-red), and b* (blue-yellow) are coordinates of the CIELAB color space, where L* (lightness), a* (green-red), and b* (blue-yellow) are coordinates of the CIELAB color space, and a0–3 are regression coefficients, estimated with the function fitlm (Matlab, R2016a, MathWorks, and a are regression coefficients, estimated with the function fitlm (Matlab, R2016a, MathWorks, Natick,0–3 MA, USA). The color difference (ΔE, CIE76) was calculated from fitting residuals as a Natick, MA, USA). The color difference (∆E, CIE76) was calculated from fitting residuals as a Euclidean Euclidean distance between estimated and true color [17]: distance between estimated and true color [17]: ∗ 2 ∗ 2 ∗ 2 ∆퐸 = √(q∆퐿 ) + (∆푎 ) + (∆푏 ) . (2) 2 2 2 CIE94′s ΔE was estimated with∆E the= openly(∆L∗) available+ (∆a∗) Matlab+ (∆b∗) code. [25]. (2) Based on the full-range CCT, we additionally tested a few modified color calibration procedures, CIE94 s ∆E was estimated with the openly available Matlab code [25]. which could0 improve, deteriorate, simplify, or expedite clinical measurements: Based on the full-range CCT, we additionally tested a few modified color calibration procedures, which could improve, deteriorate, simplify, or expedite clinical measurements:

(1) Longer exposure time. If the exposure time is not locked on the brightest CCT’s patch (e.g., patch 19), the camera sensor’s capacity limit can be reached, leading to saturation. We acquired additional six full-range CCT image sets (Sfull 1–6) with the exposure time locked on the slightly gray patch (full-range CCT: patch 20, neutral 8 (.23 *), L* = 81.3) and a transformation (regression) model was Sensors 2020, 20, 6234 5 of 12

built in the same manner as described before (Figure3a). Finally, the retrieved color di fferences were calculated and compared to the calibration on the image sets Ifull/skin1–6. (2) Full normalization (Figure3b) was done across the whole lightness range based on the grayscale patches (No. 19–24). As guaranteeing a color constancy by acquiring all 24 patches for each clinical measurement could be burdensome and time-consuming, we expedited the color calibration procedure by introducing an initial transformation from the subsequent (Ifull 2–6) to the first image set (Ifull 1) RGB color space: RGB1 = g(RGB2–6), where g was a quadratic polynomial. Six grayscale patch images served for the determination of function’s coefficients with the regression model fit. Finally, RGB values were converted to the CIELAB color space based on the known f 1 (Figure3a). (3) Single-point normalization (Figure3c) expedited a color calibration procedure since the normalization was implemented with only the brightest (No. 19) instead of the six grayscale patches. As before, the transformation to CIELAB color space was based on the known model f 1 from the image set Ifull 1.

2.3. Clinical Measurements Clinical measurements were ethically approved by the Administration of the Republic of Slovenia for Food Safety, Veterinary Sector and Plant Protection under the number U3440-187/2020/7. In addition to the image sets Ifull 1–6, we acquired skin images of six dogs in the inguinal region. For each measurement site, we repeated the image acquisition on the same spot. The typical skin RGB values were calculated as the pixels mean from two manually selected small squares [26]. We transformed the RGB values into the CIELAB color space based on the known models f 1–6 (Figure3a). We additionally compared skin color estimates between repetitions (intra-smartphone variability) and both smartphones (inter-smartphone variability), according to Equation (2) above. Finally, LAB skin values were determined with a single-point normalization (Figure3c) and compared to the original ﬁts. We calculated the erythema index (EI) for canine skin measurements and for the selected full-range CCT color patches (1–3, 5, 8, 9, and 19–24), which have physiological EI values. First, device-dependent RGB color data were transformed to the standard RGB color space (sRGB, Figure3d) based on the reference values of grayscale patches (No. 19–24). Then, we calculated the erythema index EIBRG:

√B R EI = · . (3) BRG G

We opted for an EIBRG since it was correlated the most with the canine erythema severity [26]. We additionally calculated EI with two additional (and faster) color calibration procedures (i.e., the full and single-point normalizations, Figure3b,c). Finally, we studied agreements on EI estimation between both smartphones and against the full-range CCT reference values.

3. Results Laboratory measurements on the full-range CCT showed that the Nokia smartphone (SP1) achieved a slightly better color accuracy than the Samsung smartphone (SP2) since their average CIE76 color differences were 6.45 and 6.60, respectively (Figure4, Table1). The average variability in color differences for each smartphone (i.e., intra-smartphone variability) was up to CIE76 ∆E of 0.12 (CIE94 ∆E = 0.08). When we studied the differences in color estimates between both smartphones (i.e., inter-smartphone variability), CIE76 ∆E increased to 3.91 (CIE94 ∆E = 2.60). When only the skin color patches were involved in the color calibration, the color differences were significantly smaller (Figure4, Table1). Sensors 2020, 20, 6234 6 of 12

Table 1. The color difference (ΔE) means and the whole ranges for individual patches (in brackets) on the full- and skin-range CCTs for the first (SP1) and second (SP2) smartphone and between both smartphones (SP1-SP2, i.e., inter-smartphone variability).

Color Calibration SP1 SP2 SP1–SP2 Full-range CCT (CIE76) 6.45 (2.52–13.19) 6.60 (1.66–12.80) 3.91 (0.36–9.30) Full-range CCT (CIE94) 3.96 (1.51–9.25) 4.46 (1.02–9.75) 2.60 (0.36–7.28) Sensors 2020, 20, 6234 Skin-range CCT (CIE76) 3.19 (0.93-9.04) 1.76 (0.47–3.65) 2.01 (0.34–7.18) 6 of 12 Skin-range CCT (CIE94) 1.74 (0.40–3.64) 1.16 (0.25–3.10) 1.05 (0.13–3.08)

Sensors 2020, 20, 6234 6 of 12

Color Calibration SP1 SP2 SP1–SP2 Full-range CCT (CIE76) 6.45 (2.52–13.19) 6.60 (1.66–12.80) 3.91 (0.36–9.30) Full-range CCT (CIE94) 3.96 (1.51–9.25) 4.46 (1.02–9.75) 2.60 (0.36–7.28) Skin-range CCT (CIE76) 3.19 (0.93-9.04) 1.76 (0.47–3.65) 2.01 (0.34–7.18) Skin-range CCT (CIE94) 1.74 (0.40–3.64) 1.16 (0.25–3.10) 1.05 (0.13–3.08)

FigureFigure 4. Mean 4. Mean CIE76 CIE76 color color di ffdifferenceserences ( ∆(ΔEE)) with with standard deviations deviations as aserror error bars bars for the for full the-range full-range or skin-rangeor skin-range CCT, CCT, obtained obtained from from the the sixsix image sets sets which which were were acquired acquired by the by first the (S firstP1, Nokia) (SP1, Nokia)or or secondsecond (SP2, (SP2, Samsung) Samsung) smartphone,smartphone, respectively. respectively. SP1 SP1–SP2–SP2 marks marks the the color color differences differences between between both both smartphones’ estimates. smartphones’ estimates.

TableFurther 1. Themore color, di theﬀerence impact (∆ ofE) means all three and modified the whole color ranges calibration for individual procedures patches (i.e., (in brackets)a longer onexposure the full- time, and skin-rangeor full-, or CCTssingle for-point the ﬁrst normalization (SP1) ands second) on the (SP2) colorimetric smartphone accuracy and between was limited both smartphones(Figure 5). On (SP1-SP2, average, i.e.,CIE76 inter-smartphone ΔE fell for 0.48, variability).when SP1 operated closer to its sensor’s capacity limit. On the other hand, P2′s CIE76 color difference increased by 0.07. Normalization procedure on the grayscaleColor Calibration patches (i.e., full normalization) SP1 or the single white patch SP2 (single-point normalizati SP1–SP2on) did Full-rangenot improve CCT or(CIE76) deteriorate 6.45 colorimetric (2.52–13.19) accuracy significant6.60 (1.66–12.80)ly; the change 3.91 in (0.36–9.30) CIE76 color

Full-rangedifference CCT(ΔE) (CIE94)was between − 3.960.16 (1.51–9.25)and 0.11. 4.46 (1.02–9.75) 2.60 (0.36–7.28) Skin-rangeFigure CCT 4. Mean (CIE76) CIE76 color differences 3.19 (0.93-9.04) (ΔE) with standard 1.76 deviations (0.47–3.65) as error bars for 2.01the full (0.34–7.18)-range Skin-rangeor skin CCT-range (CIE94) CCT, obtained 1.74from (0.40–3.64) the six image sets which 1.16 were (0.25–3.10) acquired by the first (S 1.05P1, Nokia) (0.13–3.08) or second (SP2, Samsung) smartphone, respectively. SP1–SP2 marks the color differences between both smartphones’ estimates. Furthermore, the impact of all three modified color calibration procedures (i.e., a longer exposure time, or full-,Further ormore single-point, the impact normalizations) of all three modified on the colorimetric color calibration accuracy procedures was limited (i.e., a longer (Figure 5). On average,exposure CIE76 time, ∆orE fellfull- for, or 0.48, single when-point SP1 normalization operated closers) on to the its colorimetric sensor’s capacity accuracy limit. was On limited the other hand,(Figure P20s CIE76 5). On color average, diff erenceCIE76 ΔE increased fell for 0.48, by 0.07.when Normalization SP1 operated closer procedure to its sensor’s on the capacity grayscale limit. patches (i.e., fullOn the normalization) other hand, P2′ ors CIE76 the single color whitedifference patch increased (single-point by 0.07. normalization)Normalization procedure did not improveon the or deteriorategrayscale colorimetric patches (i.e., accuracy full normalization) significantly; or the the single change white in CIE76patch (single color di-pointfference normalizati (∆E) wason) betweendid not improve or deteriorate colorimetric accuracy significantly; the change in CIE76 color 0.16 and 0.11. − difference (ΔE) was between −0.16 and 0.11.

Figure 5. Changes in CIE76 color difference (ΔE) due to the modified color calibration procedures: increased exposure time reaching the sensor’ capacity limit (Exp. Time), full (Full Norm.), and single- point normalization (Single-P Norm.). A negative change marks the improvement in colorimetric accuracy. Boxplots: the central mark represents the median; the bottom and top box edges indicate the 25th and 75th percentiles, respectively. Whiskers extend to the most extreme data point.

FigureFigure 5. Changes 5. Changes in CIE76in CIE76 color color di differenceﬀerence ((∆ΔEE) due to to the the modified modiﬁed color color calibration calibration procedures: procedures: increasedincreased exposure exposure time time reaching the the sensor’ sensor’ capacity capacity limit (Exp. limit Time (Exp.), full (Full Time), Norm.), full and (Full single Norm.),- and single-pointpoint normalization normalization (Single-P (Single-PNorm.). A Norm.). negative change A negative marks change the improvement marks the in improvement colorimetric in accuracy. Boxplots: the central mark represents the median; the bottom and top box edges indicate colorimetric accuracy. Boxplots: the central mark represents the median; the bottom and top box edges the 25th and 75th percentiles, respectively. Whiskers extend to the most extreme data point. indicate the 25th and 75th percentiles, respectively. Whiskers extend to the most extreme data point.

Sensors 2020, 20, 6234 7 of 12

Sensors 2020, 20, 6234 7 of 12 Sensors 2020, 20, 6234 7 of 12 We performed preliminary clinical measurements on the skin of six dogs to determine and compare We performed preliminary clinical measurements on the skin of six dogs to determine and the LAB values to those of human skin based on the specifications from the full- and skin-range compareWe performedthe LAB values preliminary to those clinicalof human measurements skin based on on the the specifications skin of six dogsfrom theto determine full- and skin and- CCT (Figure6a). Both, human and canine skin exhibited a similar lightness range, but the latter comparerange CCT the (Figure LAB values 6a). Both to those, human of humanand canine skin skin based exhibited on the aspecifications similar lightness from range the full, but- and the latterskin- appeared to be less red and yellow (i.e., lower a* and b* values). Furthermore, the skin color estimations rangeappeared CCT to (Figure be less 6a )red. Both and, human yellow and (i.e., canine lower skin a* exhibited and b* avalues). similar Fulightnessrthermore, range the, but skin the latter color between both smartphones resulted in the mean CIE76 color difference of 2.97 (i.e., inter-smartphone appearedestimations to between be less bothred smartphones and yellow (i.e.,resulted lower in thea* andmean b* CIE76 values). color Fu differencerthermore, of the2.97 skin(i.e., inter color- variability, Figure6b). The mean ∆E between the original and repeated color estimates of the single estimationssmartphone between variability, both Figure smartphones 6b). The resulted mean ΔE in between the mean the CIE76 original color and difference repeated of color2.97 (i.e., estimates inter- SP1 smartphone (i.e., intra-smartphone variability) was 2.18. Finally, when we studied the simplest smartphoneof the single variability,SP1 smartphone Figure (i.e., 6b) .intra The -meansmartphone ΔE between variability) the original was 2.18 and. Finally, repeated when color we estimates studied color calibration of a single-point normalization on the brightest color patch (Figure3c), the additional ofthe the simplest single colorSP1 smartphone calibration of(i.e., a single intra-smartphonepoint normalization variability) on thewas brightest 2.18. Finally, color whenpatch (weFigure studied 3c), change in color difference was around 0.47. the simplestadditional color change calibration in color of difference a single-point was around normalization 0.47. on the brightest color patch (Figure 3c), the additional change in color difference was around 0.47.

Figure 6. Clinical measurement ofof caninecanine skinskin color.color. ( a(a)) Canine Canine and and human human (full-range (full-range CCT: CCT: bold bold x-es, x- Figure 6. Clinical measurement of canine skin color. (a) Canine and human (full-range CCT: bold x- esskin-range, skin-range CCT: CCT: boxplots) boxplots skin) skin color color in in the the CIELAB CIELAB color color space. space. ( b(b)) ColorColor didifferencesfferences ((∆ΔEE)) inin skin es, skin-range CCT: boxplots) skin color in the CIELAB color space. (b) Color differences (ΔE) in skin color between between both both smartphones’ smartphones estimates’ estimates (SP1–SP2), (SP1 between–SP2), between the original the and original repeated and measurement repeated color between both smartphones’ estimates (SP1–SP2), between the original and repeated (Repeated),measurement and (Repeat when aed single-point), and when normalization a single-point was normalization applied (Single-P was Norm.). applied Boxplots: (Single-P the Norm.). central measurement (Repeated), and when a single-point normalization was applied (Single-P Norm.). Boxplots:mark represents the central the mark median; represents the bottom the median and top; the box bottom edges and indicate top box the edges 25th andindicate 75th the percentiles, 25th and Boxplots: the central mark represents the median; the bottom and top box edges indicate the 25th and 75threspectively. percentiles, Whiskers respectively. extend Whiskers to the most extend extreme to the data most point. extreme The data outlier poin ist. markedThe outlier as ais plusmarked (+). 75th percentiles, respectively. Whiskers extend to the most extreme data point. The outlier is marked (as retrieveda plus (+). from († retrieved the CCT from specifications). the CCT specifications). as† a plus (+). († retrieved from the CCT specifications). We additionally discovered significantsignificant absolute differences differences between the acquired and referential RGB Wevalues additionally studying discovered the grayscale significant patches onabsolute thethe full-rangefull differences-range CCTCCT between (Figure(Figure the7 7)). . acquired and referential RGB values studying the grayscale patches on the full-range CCT (Figure 7).

Figure 7. Absolute differences (ΔRGB) between the acquired and normalized referential RGB values Figure(sRGB) 7. comparing Absolute (differences dia) ffbotherences smartphones ( ΔRGB∆RGB) between (SP1, SP2), the (acquired b) shorter and (full normalized -range CCT’s referential patch 19) RGB and valueslonger ((patchsRGB) 20)comparing exposure ( a ) times, both smartphones and (c) separat (SP1, e SP1′sSP2), RGB( b) shorter channels. (full (full-range - Unitsrange a.u.CCT’s are patch retrieved 19) and and from longer longer the (patchnormalization 20) exposure against times, times, the white andand (standard(cc)) separateseparat (i.e.,e SP1 SP1′s patches0s RGB RGB 19 channels. channels. or 20). Units a.u. are are retrieved retrieved from the normalization against the white standard (i.e., patches 19 or 20). The first smartphone (SP1) generally produced smaller RGB differences on the bright patch (i.e., The first smartphone (SP1) generally produced smaller RGB differences on the bright patch normalizedThe first referential smartphone RGB (SP1) values generally of around produced 0.8), but smaller the SP2 RGB-based differences system on performed the bright better patch with(i.e., (i.e., normalized referential RGB values of around 0.8), but the SP2-based system performed better with normalizeddarker images referential (Figure RGB 7a). values However, of around both 0.8), smartphones’ but the SP2 sensors-based system responses performed were better completely with darker images (Figure7a). However, both smartphones’ sensors responses were completely depressed darkerdepressed images while (Figureacquiring 7a). the However,black patch both with smartphones’a normalized referential sensors responsesRGB value were(sRGB completely) of around depressed0.2 a.u. Furthermore, while acquiring prolonging the black the patch exposure with time a normalized by the image referential normalization RGB value on (thesRGB gray) of patch around 20 0.2(instead a.u. Furthermore, of the white prolongingpatch 19) significantly the exposure affected time by the the sensor’s image normalization response with on the the increased gray patch RGB 20 (instead of the white patch 19) significantly affected the sensor’s response with the increased RGB

Sensors 2020, 20, 6234 8 of 12 while acquiring the black patch with a normalized referential RGB value (sRGB) of around 0.2 a.u. Furthermore, prolonging the exposure time by the image normalization on the gray patch 20 (instead of the white patch 19) significantly affected the sensor’s response with the increased RGB values up to 0.05Sensors in 2020 the, referential 20, 6234 RGB range between 0.50 and 0.66 a.u. (Figure7b). Although the di fferences to8 of the 12 standard grayscale CCT patches were smaller, the absolute differences remained significant, that is up tovalues 0.20. up Finally, to 0.05 we in found the referential that even individualRGB range RGB between sensor 0.50 elements and 0.66 have a.u. di (Figurefferent spectral7b). Although responses the withdifferences channel-specific to the standardRGB di grayscalefferences ofCCT up patches to 0.04 a.u. were (Figure smaller,7c). the absolute differences remained significant,Finally, that we studied is up to di 0.20.fferences Finally, in weerythema found estimates that even ( individualEIBRG) with RGB various sensor color elements calibration have proceduresdifferent spectral (Table respons2). Thees transformation with channel-specific to the RGB standard differences RGB color of up spaceto 0.04 (sRGB) a.u. (Figure significantly 7c). BRG improvedFinally the, EI weBRG studiedagreement differences since absolute in erythema differences estimates in erythema (EI estimates) with various dropped color to around calibration 0.04 comparedprocedures to (Table the calculations 2). The t inransformation the device-dependent to the standard RGB color RGB space color (0.29–0.60). space (sRGB) We also significantly found that theimproved full normalization the EIBRG agreement on six CCT since patches absolute did notdifferences outperform in erythema the shorter estimates and simpler dropped normalization to around with0.04 compared a single white to the patch. calculations Repeated in the measurements device-dependent on the RGB CCT color resulted space (0.29 in the–0.60).EIBRG Wedi alsfferenceso found (i.e.,that intra-smartphone the full normalization variability) on six of CCT 0.005 patches and 0.003 did for not SP1 outperform and SP2, respectively. the shorter Clinical and simpler skin measurementsnormalization exhibitedwith a single an increased white patch intra-smartphone. Repeated measurements variability ofon 0.008 the CCT0.007 resulted and 0.011 in the 0.011EIBRG differences (i.e., intra-smartphone variability) of 0.005 and 0.003 for SP1± and SP2, respectively.± for SP1 and SP2, respectively. Single differences in EIBRG of the selected CCT patches and canine skin betweenClinical skin both measurements smartphones are exhibited shown inan Figureincreased8. intra-smartphone variability of 0.008 ± 0.007 and 0.011 ± 0.011 for SP1 and SP2, respectively. Single differences in EIBRG of the selected CCT patches and canineTable skin 2. betweenAbsolute dibothfferences smartphones in EIBRG between are shown CCT’s in referential Figure 8. data and both smartphones’ estimates (SP1, SP2) with different EI color calibration procedures. 1: EI was calculated directly from the

device-dependentTable 2. Absolute SP1 differences0s and SP2 0ins EI RGBBRG between color spaces, CCT’s 2: referential Colorimetric data and data both was smartphones transformed’ to theestimates standard (SP1, RGB SP2) color with space different (sRGB) EI color by the calibration full normalization procedures on. 1: sixEI was grayscale calculated patches, directly 3 and from 4: Colorimetricthe device-dependent data was S transformedP1′s and SP2′s to RGB the color standard spaces, RGB 2: Colorimetric (sRGB) color data space was by transformed the single-point to the normalizationstandard RGB on the color brightest space white(sRGB) CCT by the patch full (No. normalization 19). on six grayscale patches, 3 and 4: Colorimetric data was transformed to the standard RGB (sRGB) color space by the single-point Color Calibration CCT–SP1 CCT–SP2 SP1–SP2 normalization on the brightest white CCT patch (No. 19). 1. Without (RGB) L 0.40 0.93 0.60 1.16 0.29 0.40 ± ± ± 2. Full Norm. (sRGB)ColorL Calibration0.04 0.06CCT–SP1 CCT 0.04–SP20.09 SP1–SP2 0.03 0.04 L ± ± ± 3. Single-P Norm. (sRGB)1. WithoutL (RGB)0.04 0.060.40 ± 0.93 0.60 0.04 ± 1.160.10 0.29 ± 0.40 0.03 0.04 L ± ± ± 4. Single-P Norm.2. (sRGB) Full Norm.C (sRGB) //0.04 ± 0.06 0.04 ± 0.09 0.03 ± 0.04 0.02 0.01 3. Single-P Norm. (sRGB) L 0.04 ± 0.06 0.04 ± 0.10 0.03 ± 0.04 ± L Laboratory settings with the full-range CCT. C Clinical skin measurements. 4. Single-P Norm. (sRGB) C / / 0.02 ± 0.01 L Laboratory settings with the full-range CCT. C Clinical skin measurements.

FigureFigure 8.8. Bland-AltmanBland-Altman plotsplots onon erythemaerythema index index agreement agreement between between both both smartphones. smartphones.EI EIBRGBRG isis basedbased onon ((aa)) thethe full-rangefull-range CCTCCT patchespatches (No:(No: 1–3,1–3, 5,5, 8,8, 9,9, andand 19–24)19–24) oror ((bb)) caninecanine skin.skin. MeanMean didifferenceﬀerence andand 95%95% limitslimits ofof agreementagreement ((±1.961.96 STD)STD) areare markedmarked asas fullfull andand dotteddotted lines,lines, respectively.respectively. ± 4. Discussion In this work, we characterized the color calibration using a typical veterinary smartphone-based teledermoscopy system. We studied its colorimetric accuracy in both laboratory (CIELAB color space with the color calibration target ColorChecker) and clinical settings (RGB-based erythema index of canine skin). We discovered that the calibrated teledermoscopy system is accurate enough since the CIELAB-based color differences (ΔE) were up to 6.6. Furthermore, estimating the severity of skin erythema with an erythema index led to errors lower than 3%. Finally, we showed that we could achieve comparably accurate colorimetrical measurements with a simple image normalization on the white surface without the need for time-consuming, full-color calibration.

Sensors 2020, 20, 6234 9 of 12

4. Discussion In this work, we characterized the color calibration using a typical veterinary smartphone-based teledermoscopy system. We studied its colorimetric accuracy in both laboratory (CIELAB color space with the color calibration target ColorChecker) and clinical settings (RGB-based erythema index of canine skin). We discovered that the calibrated teledermoscopy system is accurate enough since the CIELAB-based color differences (∆E) were up to 6.6. Furthermore, estimating the severity of skin erythema with an erythema index led to errors lower than 3%. Finally, we showed that we could achieve comparably accurate colorimetrical measurements with a simple image normalization on the white surface without the need for time-consuming, full-color calibration. In the laboratory settings on the full-range CCT, the proposed teledermoscopic setup with a smartphone and a dermatoscope achieved a decent color constancy with the average CIE76 color difference between 6 in 7 (Figure4). Based on a polynomial of degree one with only four estimated parameters, a robust transformation model resulted in the color differences in the range of existing studies (∆E between 2.5 and 10.2 [8,18–23]). However, some of the studies (e.g., Zhang et al. [8]) employed the quadratic polynomial with ten parameters included. When the same polynomial was applied to our measurements, the CIE76 ∆E decreased to 3.8 and 0.9 for the full- and skin-range CCT, respectively. Nevertheless, we do not recommend using a complex polynomial with a small number of measurements (in our case: 14–24) since it can easily lead to overfitting. Furthermore, we mostly listed the CIE76 color difference, which seemed to have an increased value in the used color range compared to the CIE94 and CIEDE2000 standards. For example, ∆E decreased to 3.9 (CIE94) or 3.8 (CIEDE2000) when the updated equations for color difference were applied. Furthermore, adding a complex polynomial to the transformation model resulted in the color difference (CIE94) of 2.3, which corresponds to the lower limit of the reported ∆E range [8]. Wang and Zhang [18] showed that utilizing CCT with a narrower color range halved the color differences (e.g., from 10.4 to 5.4). We observed a similar phenomenon since the transformation models on the skin-range CCT had a significantly lower ∆E (Figure4, ∆E of 2–3). We also discovered comparably low color differences between the color estimates from both smartphones. Since the retrieved ∆E fell within the range of the human eye’s capability to spot a color difference (i.e., just noticeable difference, JND), our results point towards the adequacy of smartphones for dermatological use. Selecting a longer exposure time (Figure5) based on the gray patch (No. 20) increased the image’s RGB values. On the one hand, this improved the contrast of dark images, which would otherwise have similar, depressed RGB values. For example, longer exposure times decreased the percentage of low-intensity color patches (normalized RGB < 0.01) from 8.9% to 8.3% and from 15.0% to 13.1% for SP1 and SP2, respectively. On the other hand, prolonged exposures can lead the camera sensor to be close to its saturation charge level. The image pixels of the brightest patch (No. 19) held almost maximal RGB values, i.e., 0.92 and 0.96 for SP1 and SP2, respectively. Additionally, as shown in our previous study [23], brighter images with higher contrast can sometimes contribute to a better color constancy (e.g., with the first smartphone SP1). However, the effect of a longer exposure time can also have a negligible or even adverse impact, probably due to image saturation. Simultaneously, full range or single-point normalizations (Figure3b,c) were more straightforward color calibration procedures. Both approaches sped up the clinical measurements without affecting the color constancy of both smartphones (Figure5). Therefore, a single-point normalization is the most clinically appropriate since it is the fastest and the same accurate as full normalization. We showed similarities between the lightness of human and canine skin in Figure6a. However, it seems that canine skin is less red and yellow. Its average CIELAB’s a* and b* were 10.0 and 6.9, compared to 15.8 and 15.9 (full-range CCT), 20.2 and 25.3 (skin-range CCT) of human skin, resulting in color differences (CIE76 ∆E) of 10.7 (canine vs. human full-range CCT skin) and 10.3 (human full- vs. skin-range CCT skin). One could object that the color difference between our estimates and the full-range CCT skin specifications appeared due to the average smartphone error (∆E = 6.5, Figure4). However, our models ( ffull 1–6) overestimated both skin patches (No. 1, 2) of Sensors 2020, 20, 6234 10 of 12 the full-range CCT for ∆E of 6.0 (mean estimated and referential a* and b* values were 18.8 & 21.1, and 15.8 & 15.9, respectively). Therefore, misestimates due to the smartphone and model errors would compensate in the worst-case scenario, keeping the color difference between canine and human skin of around 10. On the other hand, the color differences between full- and skin-range CCT are expected since the semi-gloss finish of the skin-range CCT causes overestimates in a* and b* for ∆E of around 7 [27]. We should note that these conclusions are based on the standard human skin colors of the ColorChecker. If the normal canine skin color range was estimated, in vivo human skin measurements would contribute to the more real color comparison. However, these data will not improve the proposed optical system’s accuracy study since the actual colors (i.e., gold standard) would remain unknown. Comparing skin color estimates between smartphones (Figure6b) resulted in the ∆E of 3.0, similar to the results on the full-range CCT (Figure4). However, we can attribute a significant portion of this color disagreement to the variability in clinical measurements (∆E = 2.2). Since sampling the representative canine skin was done manually, it was a challenge to guarantee entirely repeatable skin area selection. Furthermore, a single-point normalization proved, again, to be sufficient for color calibration after the basic color transformation model for each smartphone was made. This work and many other studies relied on the colorimetric parameters retrieved after the transformation between RGB and CIELAB color spaces, which guaranteed a certain level of colorimetric standardization. However, when a less experienced reader encounters parameters characterized directly in the device-dependent RGB color space, he or she could assume that these results are standardized and comparable between smartphones as well. As we showed in Figure7 and Table2, performing only an image normalization with a single reference point resulted in a significant disagreement (up to 60%) between both smartphones and the reference. We can attribute these misestimates in EI to two basic reasons. First, smartphone cameras (and their separate RGB channels) have individual spectral responses with different sensitivities to detect a certain lightness level (Figure7a,c). For example, the first smartphone images had significantly lower absolute RGB values compared to the second smartphone. Secondly, the smartphone camera response is not linear; therefore, setting a specific exposure time affected the acquired data in the skin lightness range (Figure7b). Therefore, we needed to construct a transformation to the device-independent standard RGB (sRGB) color space before any RGB-based parameter calculations (Figure3d). In our previous study [16], we estimated the EIRGB range of canine skin between 1.0 (healthy skin) and 2.0 (severely erythematous skin). After the transformation to sRGB, our teledermatoscopic setup achieved intra-smartphone EI variability up to 0.01, which corresponds to a 0.5–1.0% margin of error. Additionally, disagreements in the erythema index between both smartphones were, on average, around 0.03, matching an error between 1.5–3%. These results are superior to some of the other smartphone-based optical systems that assess skin chromophores such as hemoglobin [28], which reported disagreements between smartphones exceeding 25%. Finally, we shall point out that only six dogs were enrolled in this study. However, it seems that 17 clinical (Figure8b), in addition to the numerous laboratory measurements are su fficient for confirming the colorimetric reliability of the proposed optical system. Moreover, the results indicated that the measurement site (re-)selection and image sampling are significant factors for the color disagreement (Figure6b). Therefore, the measuring and sampling procedures should be better specified before any comprehensive clinical study.

5. Conclusions Although smartphone cameras acquire images in an unknown RGB color space, a simple color space transformation ensured accurate colorimetric measurements with color diﬀerences ∆E (CIE76) of 1.8–6.6 (CIE94 ∆E = 1.1–4.5). Since the results are within the range of JND (i.e., the human eye’s capability to spot a color diﬀerence), the proposed teledermoscopic optical setup with a smartphone and dermatoscope has the potential to replace the visual skin color assessment. We assume that we could achieve even better color constancy with the help of canine skin-tailored color patches. Sensors 2020, 20, 6234 11 of 12

However, native canine skin color should be determined ﬁrst. Besides the erythema index, we could study additional colorimetric parameters for erythema estimation (e.g., a* of the CIELAB color space). To conclude, the proposed aﬀordable teledermatoscopy system has the capability to be employed in colorimetric studies in veterinary dermatology.

Author Contributions: Conceptualization, and data processing, B.C.; methodology, B.C. and E.Š.; measurements, E.Š.; writing—original draft preparation, B.C. and E.Š.; writing—review and editing, B.C. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Marie Skłodowska-Curie Actions of the European Union (IF, DogSPEC, 745396) and by the Latvian State Education Development Agency under the grant agreement 1.1.1.2/VIAA/3/19/455 (GoBVM). Acknowledgments: We would like to express our gratitude to Inga Sakn¯ıte (Vanderbilt University Medical Center, Nashville, TN, USA), Thierry Olivry (NC State University, Raleigh, NC, USA), Janis¯ Sp¯ıgulis and Uldis Rub¯ıns (University of Latvia, Riga, Latvia) for all the help with preparing this paper. We also thank Katja Lužar and Urška Kovˇcefrom the veterinary clinic Zamba (Celje, Slovenia) for their help in measuring canine skin color. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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