Identified Early Stage Mycosis Fungoides from Psoriasis and Atopic

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Identiﬁed early stage mycosis fungoides from psoriasis and atopic dermatitis using non-invasive color contrast enhancement by LEDs lighting

Yu-Ping Hsiao · Hsiang-Chen Wang · Shih-Hua Chen · Chung-Hung Tsai · Jen-Hung Yang

Received: 26 June 2014 / Accepted: 3 September 2014 © Springer Science+Business Media New York 2014

Abstract Mycosis fungoides (MF) is the most common type of cutaneous T cell lymphoma (CTCL) and may progress internally over time. MF is clinically categorized as poorly deﬁned areas of erythema, ﬂat patches, thin plaques, and tumors. The diagnosis of early stage of MF (patch and early plaque mycosis fungoides) has been a major diagnostic challenge in dermatology because of the lack of highly characteristic immunophenotypical markers and the

Y.-P. Hsiao Dermatology Department, Chung Shan Medical University Hospital, No.110, Sec. 1, Jianguo N. Rd., South Dist., Taichung City 40201, Taiwan

Y.-P. Hsiao · C.-H. Tsai School of Medicine, Institute of Medicine, Chung Shan Medical University, No.110, Sec. 1, Jianguo N. Rd., South Dist., Taichung City 40201, Taiwan

H.-C. Wang · S.-H. Chen Graduate Institute of Opto-Mechatronics, National Chung Cheng University, 168, University Rd., Min-Hsiung, Chia-Yi 62102, Taiwan e-mail: [email protected]

C.-H. Tsai Department of Pathology, Chung Shan Medical University Hospital, Taichung City 40201, Taiwan

J.-H. Yang School of Medicine, Tzu Chi University, No.701, Zhongyang Rd., Sec. 3, Hualien 97004, Taiwan

J.-H. Yang (B) Department of Dermatology, Buddhist Tzu Chi General Hospital, No. 707, Sec. 3, Chung Yang Rd., Hualien 97004, Taiwan e-mail: [email protected]

H.-C. Wang · S.-H. Chen Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University, 168, University Rd., Min-Hsiung, Chia-Yi 62102, Taiwan 123 Y.-P. Hsiao et al. heterogeneity of clinical presentations of MF. In this study, the spectrum of each picture ele- ment of the patient’s skin image was obtained by multi-spectral imaging technology (MSI). Spectra of normal or pathological skin were collected from 30 patients (10 early stage MF, 10 psoriasis vulgaris, and 10 atopic dermatitis). An algorithm combined with multi-spectral imaging and color reproduction technique is applied to find the best enhancement of the difference between normal and abnormal skin regions. Accordingly, an illuminant with specific intensity ratio of red, green, and blue LEDs is proposed, which has optimal color enhancement for MF detection. Compared with the fluorescent lighting commonly in the use now, the color difference between normal and inflamed skin can be improved from 11.8414 to 17.4002 with a 47% increase, 8.7671 to 12.8544 with a 26.3% increase, and 11.0735 to 17.2634 with a 34.3% increase for MF, psoriasis, and atopic dermatitis patients, respectively, thus making medical diagnosis more efficient, so helping patients receive early treatment.

Keywords Mycosis fungoides · Biomedical optical imaging · Color contrast enhancement · Multispectral imaging · Image reconstruction techniques · Illumination design

1 Introduction

Mycosis fungoides (MF) is a heterogeneous group of malignant lymphomas that revealed the propensity for neoplasmic T lymphocytes to infiltrate the skin. The clinical diagnosis is always a challenge for physicians because the diversity of manifestations that MF could be the red, scaly patches or thickened plaques that often mimic chronic eczema, atopic dermatitis, or psoriasis (Willemze et al. 2005; Frye et al. 2012; Lee and Hwang 2012). A study among cutaneous lymphoma patients in Holland and Austria showed that 1,476 of 1,905 cases were MF patients, constituting 77.5% of the total number of patients. The study was conducted by the World Health Organization and the European Organization for Research and Treatment of Cancer in 2005 (Willemze et al. 2005). MF commonly occurred in patients aged from 40 to 69 and were often diagnosed in males, with an occurrence rate of two males for every one female. People with dark skin also exhibited higher susceptibility to the disease than those with white skin (with an incidence ratio of 1.6) (Willemze et al. 2005). Cancerous lymphocytes are classified as primary or secondary. Primary cancerous lymphocytes are attracted to the skin through the lymphocytes in blood by the cytohormone, whereas secondary lymphoma cells are brought to the skin by blood circulation from the lymph node (Whittaker et al. 2003). Pathologic changes in mycosis fungoides, the most common type of CTCL, refer to the process in which mutant T lymphocytes break out of the corium from the capillaries, continuing upwards to attack the epidermal layer (Girardi et al. 2004). In most MF cases, patients first experience problems such as dry, reddish, and itchy skin. Some patients present with local skin depigmentation, experience itchiness, or have speckles such as rashes (World Health Organization 2009). It could be misdiagnosed as ordinary psoriasis, atopic dermatitis, or eczema if the doctor lacks relevant experience in differential diagnosis. Current methods used to detect MF include incision biopsy, blood tests, and lymphography (Hong Kong Cancer Fund 2007; Willemze and Dreyling 2010). Lymphography is similar to computerized tomography (CT) in which a radio contrast agent is injected into the lym- phatic vessel; incision biopsy involves cutting open the skin for histopathological diagnosis; and blood tests are conducted using a needle head to pierce the skin. The aforementioned procedures are invasive, painful and psychologically stressful. These techniques must also be performed repeatedly for subsequent monitoring, and the results can be confirmed only 123 Non-invasive color contrast enhancement by LEDs lighting after a considerable period of time. Among the disadvantages of conventional detection methods are that they are invasive, painful, and time-consuming. In addition, conventional detection methods can only be performed by doctors with extensive experience and skills. Consequently, real-time diagnosis becomes difficult. The application of spectroscopy and illumination in biomedicine has recently been inves- tigated (Yamaguchi et al. 2005; Yamaguchi 2001; Lee et al. 2009; Park et al. 2007; Hashimoto et al. 2011). The real-time property of multi-spectral image reproduction technology is shown to exhibit the greatest potential. For one image, we can find the individual spectrum of each pixel point by multi-spectral imaging (MSI). Calculation of the different spectra can enhance the color contrast between the normal and the pathological images, thus increasing diagnostic efficiency (Wang and Chen 2012; Wang et al. 2010, 2013). In this study, we collected pictures from thirty patients (ten patients with MF, ten patients with psoriasis vulgaris, and ten patients with atopic dermatitis, respectively). The inclusion criteria were early stage MF with patches or plaques phases, psoriasis vulgaris, and atopic dermatitis. There are four stages of mycosis fungoides depending on the degree of skin manifestation, nodal involvement, and internal organ metastasis. We recruited patients with early stage mycosis fungoides with patches or plaques phases. The limitations of this study were that tumor-stage mycosis fungoides were excluded, and the penetration depth of LED was <1mm(Cowen et al. 2007). After the spectra showing different pathological changes and the normal region were obtained, spectra of a commercial RGB LED with peak wavelengths at 460, 530, and 617nm for blue, green, and red respectively, are applied to illuminate the normal skin and the pathological skin and calculate the color difference by changing the intensity ratio of each peak. Principal component analysis (PCA) was conducted for the normal spectra and the pathological spectra of the three MF patients and then the eigenvector of MF (six basic functions) was obtained. The spectra of all patients with similar presentations were then analyzed using the MF eigenvector, and the principal component score of all pathological and normal spectra was determined. Skin diseases with similar symptoms were distinguished according to use some special LEDs lighting. Thus, skin lymphoma, psoriasis, and atopic dermatitis were identified.

2 Materials and methods

2.1 Images of cutaneous lymphoma, psoriasis, and atopic dermatitis

All images of the patients and the pathological sections in this study were provided by the Dermatology Department of Chung Shan Medical University Hospital. Initially, pictures of ten MF patients were taken. However, the initial symptoms of MF were similar to those of psoriasis and atopic dermatitis; therefore, images of ten psoriasis and ten atopic dermatitis patients were also taken to represent the control group. This study was reviewed and approved by the Chung Shan Medical University Hospital (IRB No CS12126) institutional review board. These images, which are clearer than those taken with a camera, also reveal ery- themas in the pathological regions. Figure 1a shows the pathological result of a 35-year-old man with MF. Figure 1d reveals the pathological changes in patient CA. The yellow arrows in this ﬁgure indicate epidermotropism of atypical lymphocytes. The increased and bizzare lymphocytes rushed into the epidermis; thus, patients CA has been diagnosed with cutaneous lymphoma. Figure 1b shows the pathological region of a 42-year-old male psoriasis patient PA. The symptom of initial psoriasis is similar to that of MF: patches of slight erythema in the pathological region, as shown in Fig. 1b. Figure 1e shows a section image with a 123 Y.-P. Hsiao et al.

Fig. 1 aÐc The images of the pathological skin of MF patients CA, psoriasis patients PA, and atopic dermatitis patients AA; dÐf the histopathological results of patient CA, PA, and AA, respectively. pink asterisk representing the parakeratotic corneal layer, a blue dotted arrow indicating the absence of the granular layer, a hollow green star denoting suprapapillary thinning of the epidermis, a solid green star indicating psoriasiform hyperplasia of the epidermis, and a green arrow denoting tortuous capillaries in the papillary dermis. Given the aforementioned histo pathological mechanisms, the condition is conﬁrmed to be psoriasis. Figure 1c shows images of pathological regions of atopic dermatitis belonging to a 17-year-old female. Figure 1f shows the section image of (c). The blue asterisk represents a normal basket weave stratum corneum layer; the blue arrow indicates a granular layer; the purple star denotes spongiotic epidermis; and the orange arrow represents lymphocytes with exocytosis. The thicker horny layer, the present granular layer, and other histopathological mechanisms conﬁrm that the patients have atopic dermatitis. In the pathological region of a cutaneous lymphoma patient, the characteristics that determine the pathological differences among the three kinds of skin diseases are the monoclonal and atypical lymphocytes gathered into the surface of the epidermis, which show epidermotropism. Lymphocytes increase, and abnormal lymphocytes collect in the epidermal layer. The stratum corneum of the pathological region in a psoriasis patient thickens. The papillary dermis shows dilated capilaries and normal lymphocytes accumulating in the epidermal layer. The stratum corneum of the pathological region of an atopic dermatitis patient thickens, and the epidermis shows edema and spongiosis. In these three pathological changes, keratinocytes show cell edema. The differences among the three pathological changes are obvious. However, patches of slight erythema are a common clinical characteristic, as shown in Fig. 1aÐc.

2.2 Multi-spectral imaging technique

Figure 2 shows the estimation processes of the color image reproduction with multi-spectral image data. The multi-spectral imaging (MSI) can be divided into three parts: the principal components analysis for spectra data reduction, color correction for images, and calculation 123 Non-invasive color contrast enhancement by LEDs lighting

Fig. 2 Schematic diagram of the proposed method used in estimating the spectral reflectance of each pixel of an image using a digital camera of transformation matrix to find the relationship between the digital camera and spectrophotometer. The spectral reflectance functions of the 24 patches on the color checker are measured using the spectroradiometer. The color checker is an array of 24 printed color squares, including spectral simulations of light and dark skin, foliage, and others (McCamy et al. 1976). Fluorescent lamps (Kino Flo, Mega 4Bank) are used as the illuminant in the setup. The illuminant consists of a ballast and a lamp that has four tubes. The ballast keeps the lamp flicker-free at any speed or angle of the camera shutter by controlling the high output power of the tubes. The correlated color temperature (CCT) of the illuminant is approximately 6,500K. An xÐy axis precise-translation-stage table is used in moving the color checker to determine the uniform measurement of the spectral reflectance function of each color patch. The power of the illuminant is equally distributed on each color patch of the color checker. In the measurement system, the illuminant is warmed up for approximately 30min before the spectrum is measured, and the layout of the setup is based on the illumination/measuring (45◦/0◦) geometry (Berns 2000). The illumination is projected at an incident angle of 45◦. The spectroradiometer is placed at the 0◦ angle, facing the center of the object. After measuring the spectrum, the image of the color checker is taken using a digital camera in the conditions (1/5 s exposure time, an opening of the diaphragm with f/4, and ISO value with 200), with the reference white color set as the illuminating condition of D65, which is commonly used as the standard illuminant (International Commission on Illumination) (Lam and Xin 2002). Color correction is required to achieve a more accurate estimation of the spectra. The CIE (the Commission Internationale de l’Éclairage) XYZ tristimulus values of the color checker are calculated from the spectroradiometer and set as the standard values. The CIE XYZ is one of the first mathematically defined color spaces, created by the International Commission on Illumination (Smith and Guild 1931). The RGB values of each pixel in the image from the camera are captured using a computer program and transferred into CIE XYZ values (Wang and Chen 2012). The color relationship between the spectrophotometer and the camera is found by using the third-order polynomial regression for the red, green and blue components separately, and the regression matrix[C]is determined as follows: [C]=[A]pinv[F] (1) 123 Y.-P. Hsiao et al. where [F]= 1, R, G, B, RG, GB, BR, R2, G2, B2, RGB, R3, G3, B3, RG2, RB2, GR2, T GB2, BR2, BG2 (2) and “R”, “G”, and “B” are the respective RGB values of the color checkers captured by the camera. The corrected RGB values are obtained from Eq. 3. Here [K] presents the RGB values captured from any image that expanded into a format such as the original matrix [F]. The calculation processes of the CMCCAT2000-corrected RGB values of the digital camera are obtained from reference (Li et al. 2002). The corrected RGB values of the color checkers are then transferred into CIE XYZ values and arrayed as a matrix, [β]. [Corrected RGB] = [C][K] (3) Finally, a transform matrix [M] between the spectrophotometer and the camera is obtained as follows: [M]=[α]pinv[β] (4) The spectral reflectance functions can be determined using the linear combination of several principal components (PCs) after the PC analysis method. The coefficients of the linear combination are referred to as the PC coefficients in this study. The PC coefficients and the new RGB values of the digital camera exhibit a linear relationship. The new RGB values and the corresponding PC coefficients are analyzed to obtain the transformation matrix between the digital camera and the spectroradiometer. After obtaining the transformation matrix, the spectral reflectance functions of any given object surface can be reconstructed using a multi- spectral image acquisition system. The PC coefficients of the spectral reflectance function of the object can be calculated using the new RGB values and the obtained transformation matrix. Spectral reflectance can be reconstructed by calculating the linear combination of the PCs with the obtained PC coefficients. The process involved in obtaining the RGB values of each pixel after capturing the image is corrected to achieve a more accurate estimation of the spectra. Color correction is implemented to match the color performance of the camera with that of the spectroradiometer. From the spectroradiometer, the CIE XYZ tristimulus values and corresponding RGB values from the spectra of 24 color checkers are calculated and set as standard values. From the camera, images under the same lighting conditions are captured, and RGB values of each pixel can be retrieved using the computer program. Finally, the color relationship between these two devices can be determined using the third-order polynomial regression for red, green, and blue components separately. The output format of commercial digital cameras is sRGB (RAW image files), wherein the reference white color is illuminated by D65 (Anderson et al. 1996), different from the artificial lights used in measuring the spectra of the 24 color checkers in the present study.

3 Results and discussions

3.1 Reﬂection spectra reproduction

Figure 1 show three clinical photographs of patients for MF, psoriasis, and atopic dermatitis patients. All images have been captured under the same conditions (light source: ﬂuorescent lamp; illuminance: 200 Lux). From these images, the locations of erythema and their color changes can be clearly identiﬁed. By using the color reproduction system mentioned above, 123 Non-invasive color contrast enhancement by LEDs lighting

Fig. 3 The simulated reflection spectra of the abnormal skin of the ten MF patients (a), ten psoriasis patients (b), and ten atopic dermatitis patients (c). Thirty points of each skin lesion location is calculated in each patient’s body through doctor’s discrimination. d The average spectrum calculation results of normal skin, MF, psoriasis, and atopic dermatitis the reflection spectrum of each lesion at any site in these images can be estimated rapidly. Figure 3 show simulated reflection spectra of the abnormal skin of the ten MF patients (a), ten psoriasis patients (b), and ten atopic dermatitis patients (c). Thirty points of each skin lesion location is calculated in each patient’s body through doctor’s discrimination. Figure 3d presents the calculation results of the average spectrum of the normal skin and the skin with three kinds of pathological changes. The average spectrum of the latter has a lower reflection rate than that of the former. The reason is that pathological changes alter the structures of the stratum corneum, epidermal layer, and dermis, affecting the reflection of light on the skin surface. The very dark color of the pathological skin is also attributed to this alteration. The differences among the three types of skin disease are visually slight; therefore, the average spectra of MF, psoriasis, and atopic dermatitis are almost the same. Their main difference is in the adsorption of hemoglobin near the 530nm wave band. An increase in hemochrome causes a reduction in the reflection rate in this wave band. The difference near the short wave (about 450nm) mainly comes from the increase in the number of abnormal lymphocytes and the reduction in reflection rate. The cell nucleus of the cancer cell becomes enlarged, increasing the adsorption of the short wave and reducing the reflection rate (Jen et al. 2014; van der Poel et al. 1990; Gorgidze et al. 1998).

3.2 Color contrast enhancement by LEDs lighting

Skin cancers are a significant health problem worldwide, and researchs are constantly looking for methods of early diagnosis. The traditional biopsy method is invasive, as well as requiring 123 Y.-P. Hsiao et al. a time-consuming and high-cost pathological analysis. Skin cancer, for example, is studied by many researchers for the possibility of using illuminants for diagnosis. These studies are based on confirmed cases of skin cancer; studies of illumination for early detection are few. Previous studies show that LEDs have good performance not only for exciting autofluorescence, but also for enhancing the color contrast of microvasculature in narrowband imaging (Park et al. 2007; Nishino et al. 2012; Lee et al. 2009; Li 2005; Kaarna et al. 2010). The main idea of this study is to find the optimal color lighting which can enhance the color contrast between the abnormal and normal skin for optimal visual perception and detection of MF. However, as indicated in previous studies, white light might not be the best choice, so multicolored LEDs should be tested in the same way. A suitable mix of colored LEDs can be expected to be found. A computer program has been written to resolve the complex computation process. Spectra of a commercial RGBY LED with peak wavelengths at 617, 530, 460, and 586nm for red, green, blue, and yellow, respectively, are applied to illuminate the skin and calculate the color difference by changing the intensity ratio of each peak. By using this method, the color enhancement ability of every single point in the CIE (the Commis- sion Internationale de l’Éclairage) 1931 chromaticity diagram is calculated (Smith and Guild 1931). The distribution of the maximum color difference also is found. After the computation of color difference for all illuminants, the maximum, the minimum and the median values ∗ , ∗ ∗ are determined, and denoted as Eab max Eab min,and Eabmid, respectively. Finally, a color difference distribution diagram is plotted by the program, which represents the average color differences by using the formula of RGB digital counts (Rd, Gd, Bd) as follows : = − ( ∗ − ∗ )[ /( ∗ − ∗ )] Rd 255 Eab max Eab 255 Eab max Eab min (5) = − ∗ − ∗ [ /( ∗ − ∗ )] Gd 255 Eab Eabmid 255 Eab max Eabmid (6) = − ( ∗ − ∗ )[ /( ∗ − ∗ )] Bd 255 Eab Eab min 255 Eab max Eab min (7) ∗ where “Rd”, “Gd”, and “Bd” are the degrees of difference between a color difference Eab ∗ , ∗ ∗ and Eab max Eab min,and Eabmid, respectively. The program is applied to the selected regions in Fig. 1aÐc. By changing the intensity ratio of the red, green, blue, and yellow peaks about 2.5 million different light sources spectra are calculated for RGBY LEDs by varying the intensity ratio by intervals of 0.025, the color differences between the normal and inflamed regions are calculated. Figure 4a shows color difference distribution diagram which represents the average color difference between normal and inflamed skins of ten MF patients under the illumination of commercial RGBY LEDs, in which “x” and “y” are the chromaticity coordinates of the illuminant in the CIE 1931 standard colorimetric system (colour figure online). The color bar at the right-side of the diagram refers to the color of the corresponding average color difference calculated using Eqs. 5Ð7, “R”, “G”, “B”, and “Y” denote the chromaticity coordinates of red, green, blue, and yellow LEDs, respectively. The chromaticity coordinates of any mixing ratio of these LEDs will be bounded in the area of these four points. A large color difference will appear red, a small one, blue. It can be seen that the illuminant with a specific intensity ratio of red, green, blue, and yellow LEDs with (x, y)=(0.4205, 0.5198) has the best color contrast enhancement ability, producing a color difference of 17.4002, as shown in the inserted plate at the top right in the diagram. Figure 4b, c shows a color difference distribution diagram which represents the average color difference between normal and inflamed skins of ten psoriasis and ten atopic dermatitis patients, where the distribution of the maximum values appears at a similar position to that in Fig. 4a, and the color difference is 11.0735 and 17.2634, respectively. From color difference distribution diagrams, Fig. 4aÐc can be found to the trend with the area covered by the maximum color difference is too large, the reason is because the resolution 123 Non-invasive color contrast enhancement by LEDs lighting

Fig. 4 Color difference distribution diagrams of normal and inﬂamed regions in the skin of (a) ten MF patients, (b) ten psoriasis patients, and (c) ten atopic dermatitis patients under the illumination of different types of LEDs. d Chromaticity distribution diagram of the illumination light sources of the maximum color difference for MF, psoriasis, and atopic dermatitis as green, blue,andred regions, respectively of the scale bar is not obvious. Therefore, we take advantage of the multi-spectral imaging technology to produce excellent to poor color difference distribution diagram. A 2.5 million color difference values for each patient, and each value corresponds to a set of chromaticity coordinates. Using this correspondence, 30 patients with maximum color difference value taken out before 1,000 group, and presented in the graph of the chromaticity coordinates. On the graph will have each patient area by 1,000 chromaticity coordinates overlapping part of the area reserved for the same lesion, to skin lymphoma marked in green; Similarly available atopic dermatitis marked red compared to psoriasis to blue in Fig. 4d. Therefore, we can know that the similar visual symptoms of skin lesions, color difference can enhance visual recognition through different color lighting sources, further identify different diseases.

3.3 Color images reproduction under the new illumination

In order to know the real visual perception of MF, psoriasis, and atopic dermatitis after the color contrast enhancement from the complex computation process above. Figure 5 show the optimal lighting spectra of RGBY LEDs for (a) MF, (b) psoriasis, and (c) atopic dermatitis color contrast enhancement. The chromaticity coordinates of the three light are (x,y)=(0.4271,0.5251), (x,y)=(0.3486,0.3872), and (x,y)=(0.3450,0.3805) of MF, psoriasis, and atopic dermatitis, respectively. The relative intensity ratio of RGBY LEDs are 0.5815:0.9361:0.0308:0.9999, 0.5791:0.9358:0.7523:0.9688, and 0.5922:0.9068:0.7494: 123 Y.-P. Hsiao et al.

Fig. 5 Optimal color lighting spectra for (a)MF,(b) psoriasis, and (c) atopic dermatitis with color contrast enhancement

0.9701 for MF, psoriasis, and atopic dermatitis, respectively. In color science, the CIE XYZ color space deﬁnes all the colors in terms of three imaginary primaries based on the human visual system. X, Y and Z denote tristimulus values of a color stimulus (S(λ)) and are expressed as

780 nm 780 nm 780 nm X = k S(λ)x¯(λ)dλ, Y = k S(λ)y¯(λ)dλ, Z = k S(λ)z¯(λ)dλ (8) 380nm 380nm 380nm where, x¯(λ), y¯(λ),andz¯(λ) are the color matching functions, S(λ) represents the spectral radiance at a certain wavelength λ which is the product of the spectral power distribution of the illuminant I(λ) and the spectral reflectance function of the object R(λ), and k is a constant (Wyszecki and Stiles 1982). The unit of the spectral power distribution is measured in power units, watts. The normalization constant k in equation is defined differently for relative and absolute colorimetry. In absolute colorimetry, k is set equal to 683 lumen/W, and making the system of colorimetry compatible with the system of photometry. For relative colorimetry, k is defined by equation 100 k = (9) 780 S(λ)y¯(λ)dλ 380 The normalization for relative colorimetry in Eq. (9) results in tristimulus values that are scaled from zero to approximately 100 for various materials. In this study, we use relative colorimetry during estimation processes. The spectral radiometric quantity for each pixel of one color image is simulated, then the new light source spectrum in S(λ) (as Fig. 5aÐc) is replaced to reproduce new color images with different light sources. Color images of the same position of skin based on different color light sources in the same conditions are demonstrated. In Fig. 6a, c, e show the color images from camera under the illumination of a fluorescent lamp for MF, psoriasis, and atopic dermatitis patients respectively. In Fig. 6b, d, f show color images reproduction of MF, psoriasis, and atopic dermatitis patients are under replacing the optimal LEDs light source of Fig. 4aÐc, respectively. In Fig. 6a, b, the blue arrows present the inflamed skins of a MF patient for highlighting the areas of interest. Under the illumination of the fluorescent lamp and optimal LEDs lamp, the average color difference between the normal and inflamed skins of the ten MF patients is 11.8414 and 17.4002, respectively. The color contrast enhancement is about 47% for MF patients. For psoriasis, and atopic dermatitis patients, the average color difference between the normal and inflamed skins is about 8.7671 and 12.8544 under the fluorescent lamp, 11.0735 and 17.2634 under the optimal LEDs lamp, respectively. The color contrast enhancement is 123 Non-invasive color contrast enhancement by LEDs lighting

Fig. 6 Color images from camera are under fluorescent lamps of (a)MF,(c) psoriasis, and (e) atopic dermatitis patients. The color images reproduction of (b)MF,(d) psoriasis, and (f) atopic dermatitis patients are under replacing the optimal LEDs light source of Fig. 3aÐc, respectively about 26.3 and 34.3% for psoriasis, and atopic dermatitis patients, respectively. It is obvious from visual inspection that the color enhancement ability of the optimal LEDs in Fig. 6b, d, f are better than that of the fluorescent lamp in Fig. 6a, c, e. The significant differences in the visual inspection, the normal and inflamed skins in color changes from skin color to slightly red under the illumination of the fluorescent lamp and from yellow-green to slightly black under the illumination of the optimal LEDs lamp. For the color lighting, the optiaml color illuminates of psoriasis and atopic dermatitis are correlated color temperature 4,000Ð5,000K of white lights. The illumination color for visual recognition of MF is yellow-green color lighting. In the future, if we can develop a smart lighting environment through changes in lighting colors, doctors will be able to work in a more efficient visual identification space.

4Conclusion

In this study, we analyzed the trend of the average spectrum of the three diseases, skin lesions of 30 patients by the MSI technique. The differences among the three types of skin disease 123 Y.-P. Hsiao et al. are visually slight; therefore, the average spectra of MF, psoriasis, and atopic dermatitis are almost the same. The difference near the short wave (about 450nm) mainly comes from the increase in the number of abnormal lymphocytes and the reduction in reflection rate. The cell nucleus of the cancer cell becomes enlarged, increasing the adsorption of the short wave and reducing the reflection rate. We use the computer program to the proportion of the RGBY LEDs individual strength to enhance the color contrast in the images of the three diseases, lesions and normal areas. The color contrast enhancement is about 47, 26.3 and 34.3% for MF, psoriasis, and atopic dermatitis patients, respectively. The chromaticity coordinates of optimal color lighting are (0.4205,0.5198), (0.3476, 0.3785), and (0.3486,0.3872) for MF, psoriasis, and atopic dermatitis patients. This study provides a novel idea, the use of different lighting colors to increase the doctors on the visual identification MF lesions.

Acknowledgments This research was supported by National Science Council, The Republic of China, under the Grants of NSC 100-2221-E-194-043, 101-2221-E-194-049, 102-2221-E-194-045, 102-2622-E-194-004- CC3 and Chung Shan Medical University Hospital (CSH-2011-C-024 and CSH-2013-C -018).

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