Human Thermal Models for Evaluating Infrared Images

Transcription

1 IR IMGE: COURTESY DR. JOHN KEYSERLINGK Human Thermal Models for Evaluating Infrared Images Comparing Infrared Images Under arious Thermal Environmental Conditions Through Normalization of Skin Surface Temperature Naoto Kakuta, Shintaro Yokoyama, and Kunihiko Mabuchi Department of Mechanical Engineering and Intelligent Systems, University of Electro-Communications Graduate School of Engineering, Hokkaido University Graduate School of Information Science and Technology, University of Tokyo f there were a method to obtain normal Iskin surface temperature distribution, all thermographers could use it in combination with conventional subjective diagnosis to evaluate thermal infrared (IR) images. However, taking the range of skin surface temperature into consideration, there is a practical limitation in using acquired IR images to define a normal skin surface temperature distribution for each person [], []. On the other hand, the skin surface temperature distribution may be obtained from computer simulation. nd although computer simulation cannot completely predict the skin surface temperature distribution, it is advantageous in that a user can use it to arbitrarily change parameters reflecting environmental and physiological conditions. Several computer models that calculate thermal conditions within the human body have been proposed for diverse purposes such as analyzing hyperthermia (e.g., []-[]), predicting thermal physiological responses under severe conditions (e.g., [6], [7]), or evaluating the degree of comfort of a thermal environment (e.g., [8], [9]). However, none of them has been applied to IR imaging. To address this point, we have developed a computer model that simulates the heat transfer phenomenon within the human body and predicts the internal temperature, including the skin surface temperature [0], []. We first applied the model to comparing IR images under various thermal environments [], the conditions of which have a strong influence on skin surface temperature. This influence needs to be eliminated to enable abnormal areas in IR images to be detected and evaluated [], []. s an example, when two IR images are compared under different thermal environmental conditions, it is necessary to be able to convert one image into another under the same conditions, oo convert both images into ones under standard thermal environmental conditions. This article describes the outline of our human thermal model and the method we employed for converting IR images. Human Thermal Model Bio-Heat Transfer Equations Simulating heat transfer within the human body requires the formulation of a bio-heat transfer equation and modeling of the human body. To date, there have been many discussions on bio-heat transfer equations and a number of different equations have been proposed (e.g., []-[6]). From these, we used the following equation for bio-heat transfer in tissue [0], [7]: T(,) ρ() r c() r = λ() r T(,) t + w(,) ρbcb[ Tab(, ) T( r, t)] + Hab(,)[ Tab(,) T(,)] + Hvb(,)[ Tvb( r,) t T(,)] + M(,) () where t: time [s], r: coordinates vector in tissue, T: temperature [K], ρ: density [kg/m ], c: specific heat [J/(kg K)], λ: thermal conductivity [W/(m K)], w: blood perfusion rate per unit volume of tissue due to capillaries [m /(m s)], M: metabolic heat production rate [W/m ], H ab : coefficient for heat transfer between tissue and arterial blood per unit volume of tissue [W/(m K)], H vb : coefficient for heat transfer between tissue and venous blood per unit volume of tissue [W/(m K)], subscript b: blood, ab: arterial blood, and vb: venous blood. The first term on the right side in () is the thermal conduction term. The second November/December 00 IEEE ENGINEERING IN MEDICINE ND BIOLOGY 079-7/0/$ IEEE 6 uthorized licensed use limited to: University of Illinois. Downloaded on ugust, 009 at 7: from IEEE Xplore. Restrictions apply.

2 term shows the heat exchange between the entering arterial blood and the tissue through the capillary wall. Here it is assumed that perfect heat transfer between the blood in the capillary bed and the tissue occurs due to the extremely large surface area of the capillaries. In consequence, the temperature of the blood leaving the capillary bed is equal to the temperature of the tissue. However, this assumption does not hold for larger vessels. Therefore, the third and the fourth terms, respectively, show the heat transfer through the large vessels between the tissue and the arterial blood, and between the tissue and the venous blood. Ovehe entire body, however, the heat exchange efficiency through arteries or veins is apparently lowehan that through capillaries. Hence, we set H ab, H vb, and H av to be zero. The fifth term, M, represents the metabolic heat production rate. Equations () and () are heat transfer equations fohe arterial and venous circulatory system, respectively. rterial blood pool Tab(,) ρb c b ab(,) t = fab(,) ρbcb [ Tam( r, t) Tab(,)] + H (,)[ T(,) ρ ab ab Tab(,)] d ab + Hav( r,)[ t Tvb(,) Tab(,)] () enous blood pool Tvb(,) c (,) t = fvb(,) ρbcb[ Tvn(,) Tvb( r,)] t + [ w( r, t) ρ c + H ( r, t)] b b vb vb b b [ T( r, t) Tvb( r, t)] d vb + Hav(,)[ Tab(,) Tvb(,)] () where : blood pool volume [m ], : volume of tissue which the blood pool governs [m ], f: blood flow rate of blood pool [m /s], and H av : coefficient of heat transfer between arteries and veins for countercurrent flow [W/K]. Subscript am: adjacent arterial blood pool, vn: adjacent venous blood pool. The first term on the right side in () shows the transport of heat to the blood pool in question from the adjacent blood pool. The second term shows the heat exchange between the blood pool and the vb neighboring tissue. The third term expresses the countercurrent heat exchange between the artery and the vein. Equation () consists of the same terms, but the second term takes perfusion blood in tissue ( wρ b c b ) flowing into the venous blood pool into consideration. Thermal radiation, conduction, and convection are the forms of heat transfer from the skin surface to the environment, and sweat evaporation is a form of heat loss from the body qs = qc + qr + qeva + qd () where q s : heat transfer rate at skin surface [W/m ], q c : convective heat transfer rate [W/m ], q r : radiant heat transfer rate [W/m ], q eva : evaporative heat transfer rate [W/m ], q d : conductive heat transfer rate [W/m ]. The convective heat transfer rate is generally defined by using a convective heat transfer coefficient. In this study, the convective heat transfer coefficient for a cylinder was used [8], [9]. The radiant heat transfer rate follows Stefan-Boltzmann s law [0], []. It is assumed here that evaporative heat loss is equal to insensible perspiration rate [], [] under conditions that induce little perspiration, such as room temperature. Conductive heat transfer at the skin surface with a solid is not considered in this study. To simulate body temperature, it is Bone Muscle Fat Skin necessary to solve the above equations simultaneously with an efficient algorithm and adequate property values. Geometric Model of the Human Body We adopted a 6-cylinder-segment model as the geometric model of the human body [Figure (a)]. Each extremity segment (upper arm, forearm, hand, thigh, calf, and foot) is divided into four concentric layers: bone, muscle, fat, and skin [Figure (b)]. Each of the other segments (head, neck, thorax, and abdomen) has another layer on the inside of the bone corresponding to an internal organ. It is assumed that the properties of each layer are homogeneous and the longitudinal and angular coordinates are not taken into account. In each segment, there are a large arterial blood pool and a venous blood pool corresponding to the main artery and the main vein, respectively [Figure (c)]. Heat transfer between two segments occurs through the large blood pools. In each layer, there are an arterial blood pool and a venous blood pool corresponding to the arteriole and the venules, respectively. Numerical for Bio-Heat Transfer Equations The numerical calculation outline of the bio-heat transfer equations is as fol- Bone Bone Large Large Large Large Muscle Muscle (a) (b) (c). (a) 6-cylinder-segment model. (b) Concentric multilayered model fohe extremities. The head, neck, thorax, and abdomen segments have internal organ layers. (c) Schematic of the blood circulatory system model. Symbols and represent the arterial and venous blood pools, respectively. In one segment, there is a pair of large arterial and venous blood pools corresponding to the main artery and vein. The blood pools are connected to each other in adjacent segments. In each layer, there is a pair of arterial and venous blood pools corresponding to the arteriole, venules, and capillaries. Fat Fat Skin Skin 66 IEEE ENGINEERING IN MEDICINE ND BIOLOGY November/December 00 uthorized licensed use limited to: University of Illinois. Downloaded on ugust, 009 at 7: from IEEE Xplore. Restrictions apply.

3 lows: ) each equation [()-()] of each layer is transformed into an equation with cylindrical coordinates; ) the equations are discretized spatio-temporally for numerical calculation; ) simultaneous equations fohe whole body at a time are composed and calculated from the discretized equations; ) the simultaneous equations are solved by a numerical method such as the Gauss method; ) the simultaneous equations are calculated sequentially with time. The detailed method is presented in [0], []. s a result, the developed computer program simultaneously calculates the distributions of internal temperature, heat flux, and blood temperature of all segments with time by inputting thermal environmental factors such as aiemperature, mean radiant temperature, air velocity, and relative humidity of each segment. The measurement results we obtained suggest that the skin surface temperature distribution does not change in the chest, abdomen, forehead, and neck as it does in other regions. Skin Blood Flow Regulation human thermal model needs to include a program fohermoregulation that mainly consists of three regulation systems: skin blood flow (SBF) regulation, perspiration, and thermogenesis. In particular, SBF regulation plays an important role in the vicinity of the thermal neutral zone of the human body []. Since there is no experimental data about SBF regulation ovehe whole body, we assumed () for all segments of the human body according to the empirical equations for SBF of the forearm []-[8]: ( SBF) i = ζi[ 66. ( Thy Thy0) ( Tsm Tsm0)] + ( SBF0 ) i () where ( SBF) i : skin blood flow rate of the ith segment of the human body [ml/(00ml min)], T hy : hypothalamic temperature [K], T sm : mean skin temperature [K], T hy0 : set point of T hy [K], T sm0 : set point of T sm [K], ( SBF 0 ) i : skin blood flow rate in comfort of the ith segment [ml/(00ml min)], ζ i : coefficient for skin blood flow regulation of the ith segment [ ]. alues of ζ i we used are head:.6, neck:.0, thorax:., abdomen:.0, upper arm:., forearm:.0, hand:., thigh: 0.8, calf:., foot:.0 [9]. In the computer program, each time the numerical calculation of the bio-heat transfer equation is performed, SBF is renewed by input of up-to-date T hy and T sm. However, there are some problems with (), a simple linear equation, that need to be discussed. Needless to say, the SBF model must be improved to incorporate the results obtained in the latest physiological research. Body Temperature Results Figure shows the results obtained for body temperature calculated undehe condition that both the aiemperature and the mean radiant temperature were maintained at C for a 60-min period, then changed to C and maintained at that temperature for a subsequent 60-min period. The skin surface temperatures of all (a) (c).6 (.). (.8). (.9)..6 (7.) (7.).8 (7.0). (7.).8 (7.).8 (7.0).6 (.). (.8). (.9) Temperature [ C] 8 Foot Hypothalumus Thorax Thigh Forearm.0 (.).0 (.7) 7. (8.8).0. (.9) (.7). (.9).0 (.8). (.8). (.9).0 (.).0 (.7) 7. (8.8).8 (.7).9 (.).8 (.7).9 (.) Ta =.0 C.0 C Time [min] 9. (.6). (.) (a) (b) (c). (.) 9. (.6). Simulated body temperature profile. iemperature and mean radiant temperature were maintained at C for a 60-min period, then changed to C and maintained at that temperature for a subsequent 60-min period. ir velocity and relative humidity were maintained at 0.m/s and 0.0%, respectively. (a) Skin surface temperatures after 60 min. The value in parentheses is the temperature at the center of the segment. (b) Hypothalamus temperature (temperature at the center of the head) and skin surface temperatures of four segments with time. (c) Skin surface temperatures after 0 min. The value in parentheses is the temperature at the center of the segment. November/December 00 IEEE ENGINEERING IN MEDICINE ND BIOLOGY 67 uthorized licensed use limited to: University of Illinois. Downloaded on ugust, 009 at 7: from IEEE Xplore. Restrictions apply.

4 Temperature [ C] Temperature [ C] Radius [cm] (a) Head Radius [cm] (c) Thigh 60 min 0 min 7 60 min 0 min Temperature [ C] Temperature [ C] min 60 min Radius [cm] (b) Forearm 60 min 0 min Radius [cm] (d) Foot. Internal temperature profiles in the head (a), forearm (b), thigh (c), and foot (d). The horizontal axis is the radius. That is, the right side of the graph corresponds to skin surface and the left side corresponds to the center of a segment. Original IR Image + Offset Converted IR Image. Outline of the conversion of infrared images. Thermal Environmental Factors Calculated Skin Temperature Calculated Skin Temperature Human Thermal Model Thermal Environmental Factors for Standard segments at 60 min and at 0 min are shown in Figure (a) and (c), respectively. s can be seen, the skin surface temperatures of the peripheral segments were lowehan those of the trunk. The mean skin temperatures at 60 min and at 0 min are.8 C and. C, respectively. Figure (b) shows that the decreasing rates of the skin surface temperature vary among the segments chiefly because their thermal capacities and metabolic heat production rates are different. The diminution from 60 min to 0 min of the core temperature (hypothalamic temperature) is 0. C, which is much smallehan that of the skin surface temperature. Here, we present a few comments on how SBF regulation affects the temperature of each region. The SBF-regulated hypothalamic temperature (Figure ) was. C highehan the nonregulated temperature [() was not used] at 0 min. The SBF-regulated skin surface temperature of the thigh was. C lower than the nonregulated temperature at 0 min. That is to say, when SBF decreased, the skin surface temperature was lowered and the heat loss from the body surface was inhibited. Figure shows the calculated internal temperature profiles of the head, forearm, thigh, and foot. It is obvious that the form of the temperature curve varies among the segments. Especially in the head, since the brain has relatively large blood flow and metabolic rates, it can be seen that the internal temperature is constant (7. C at 60 min) within approximately 6 cm. It is difficult to verify the calculated internal temperature profiles because there is far less measurement data on internal temperature than on skin surface temperature. We therefore have to discuss the data validity on the basis of fragmentary data. The temperature gradient in the skin is essentially largehan that in the deep tissue (muscle) []. The central temperatures of the thorax, the abdomen, and the head can be compared with temperatures of the esophagus, rectum, and brain [], [] (tympanum), respectively. lthough we cannot say so definitely, we find that the calculated temperature is the same as the general tendency. Converting Infrared Images Infrared Image Conversion Method We converted IR images obtained under various thermal environments into those under anothehermal environment with the results calculated by the human 68 IEEE ENGINEERING IN MEDICINE ND BIOLOGY November/December 00 uthorized licensed use limited to: University of Illinois. Downloaded on ugust, 009 at 7: from IEEE Xplore. Restrictions apply.

5 thermal model. Figure shows a flow diagram for converting IR images. First the IR image of an objective segment such as the hand, the forearm, ohe calf is picked out. Meanwhile, inputting the original thermal environmental factors (aiemperature, radiant temperature, air velocity, and relative humidity) of each segment, the human thermal model calculates the skin surface temperature of each segment. lso under another environment we require, the skin surface temperature is calculated. Finally, the intensity corresponding to the difference (offset) between the two skin surface temperatures is added to or subtracted from the intensity of the original IR image.. c. c.9 7c.9 7c (a) (b). 7c. 7c. 7c. 7c.0 7c. 7c Results Typical results of the converted IR images are shown in Figures and 6. Thermographic measurements were performed with the Thermal ision Laird (Nikon, Japan). The subjects wore only shorts and remained seated in a temperature-controlled room. The aiemperature control protocol was the same as that for the simulation condition used in Figure (b). The relative humidity was fixed at 0% throughout all experiments. Immediately after an initial 60-min. period at C, the aiemperature was changed to 6 C (Figure ) and C (Figure 6) and maintained until the 0-min. point. The subject whose results are shown in Figures and 6 was a healthy Japanese male (age = 8 years; height = 8 cm; weight = 7 kg). Figure (a) and (b) displays the IR images at 60 min and 0 min, respectively. Figure (c) is the IR images converted from the original ones [Figure (b)] into those at C using the calculated offsets. Figure 6 shows the images in for a C temperature in the same manner. In Figure, it is obvious that the converted IR images of the thigh, the chest, and the forearm approximate their original ones at C [Figure (a)] more than those at 6 C [Figure (b)]. The temperatures of the foot and the hand in Figure (c) are highehan those in Figure (a), which means that the calculated offset exceeded the real temperature difference. The offsets used for converting the images of the foot, thigh, chest, forearm, and hand were.,.9,.,.6, and.6 C, respectively. s will be described in more detail later, since vasoconstriction and vasodilation noticeably occur and the volume (thermal capacitance) is small, the. c.7 7c (c). IR images of ) the top side of the foot, ) the front of the thigh, ) the chest, ) the forearm, and ) the back of the hand remaining in a C ambient temperature environment for 60 min (a) and subsequently remaining in a 6 C environment for 60 min (b). The images in the bottom row (c) are those converted from the images in (b) into those at C. temperature color legend of the same scale is used in (a), (b), and (c) (:.0-.0 C; -: C). The number in the image represents the temperature at the symbol (a) (b) (c) 6. IR images of ) the top side of the foot, ) the front of the thigh, ) the chest, ) the forearm, and ) the back of the hand remaining in a C ambient temperature environment for 60 min (a) and subsequently remaining in a C environment for 60 min (b). The images in the bottom row (c) are those converted from the images in (b) into those at C. temperature color legend of the same scale is used in (a), (b), and (c) (: C; -: C). The number in the image represents the temperature at the symbol +.. 7c c c..8.8 November/December 00 IEEE ENGINEERING IN MEDICINE ND BIOLOGY 69 uthorized licensed use limited to: University of Illinois. Downloaded on ugust, 009 at 7: from IEEE Xplore. Restrictions apply.

6 Temperature ( C) Temperature ( C) Temperature ( C) Temperature Difference ( C) Temperature Difference ( C) (a) (b) (c) (d) Forehead Neck Chest bdomen Upper rm Forearm Hand (Back) Thigh Calf Foot (Instep) 7. Mean value ± SD from seven subjects and calculated value of the skin surface temperature remaining in a C ambient temperature environment for 60 min (a), and subsequently remaining in a 6 C (b) and C (c) environment for 60 min. (d) and (e) show the differences between the skin surface temperature at C and 6 C, and C and C, respectively. Point of measurement fohe forehead: cm above the point between eyebrows; neck: right side near carotid; chest: midpoint of the nipples; abdomen: umbilicus; upper arm: front (on biceps muscle); hand: middle of the back of the hand; thigh: front (on quadriceps muscle); and foot: middle of the instep. (e) temperature distribution of peripheral segments such as the hand and the foot change significantly. Individual differences in temperature distribution are also found to occur. Careful consideration is necessary to analyze the results fohese segments. In Figure 6, however, the converted images of the foot and the hand are in relatively close agreement with the original ones, while those of the thigh and the forearm are not. The offsets used in Figure 6 fohe foot, thigh, chest, forearm, and hand were.,.9,.8,., and 6. C, respectively. The skin surface temperatures used for converting IR images are summarized in Figure 7. Seven healthy Japanese males (mean age = 8 ± 9 yrs; height = 7 ± 6 cm; weight = 70 ± 8 kg) were studied. It can be seen in Figure 7(a) and (b) that the discrepancies between the calculation and the measurement fohe hand, calf, and foot are relatively large. lso, in these segments, standard deviations (SD) of the measured temperatures were relatively large (hand: 0.8 C, calf:. C, foot:. C at C; hand: 0.8 C, calf:. C, foot:. C at 6 C). The calculated values of the hand were far below the mean measurement values (calculation: 8.9 C, measurement:.8 C at 6 C). Contrary to this, the calculated values of the calf and the foot were higher. In Figure 7(d), since these discrepancies were canceled, the calculated temperature differences of the calf and the foot apparently compared well with the mean measurement ones. In the case of the ambient temperature of C [Figure 7(c)], the discrepancies of all segments can be observed. Hence, for all segments, the differences between calculated values at C and at C were significantly larger than those between the measurement values at C and at C [Figure 7(e)]. Discussion The 6-cylinder-segment model is a rough geometric model. Nevertheless, as can be seen in Figures and, the calculated temperatures of the core and the skin surface in the thermal neutral zone compared well with the real temperatures, which indicates that relatively adequate physiological properties could be assigned to each segment. This agreement is an essential condition for applying a human thermal model to IR imaging. Under severe thermal environmental conditions, however, the discrepancy between the calculated temperature and the real one 70 IEEE ENGINEERING IN MEDICINE ND BIOLOGY November/December 00 uthorized licensed use limited to: University of Illinois. Downloaded on ugust, 009 at 7: from IEEE Xplore. Restrictions apply.

7 was relatively large. This tendency can be seen in Figure 7. The calculated values agree with the mean measurement values for all but the hand at 6 C [Figure 7(d)]. Therefore, the accuracy of the converted IR image seems to depend on individual variations rathehan simulation accuracy. Under C ambient temperature condition [Figure 7(e)], however, it is obvious that simulation accuracy needs to be improved because the data obtained by calculations are significantly highehan the averaged data from measurements. ctual skin surface temperature distribution within a segment is nonuniform. Our model cannot in theory simulate such a distribution. Towards this end, the geometry must be modified through reconstruction from tomograms (e.g., MRI images) and computer graphics technology [], []. Even though a -D model resembles the human body form better than a cylindrical segment model, modeling of vasculaissue and assigning physiological properties remain as subjects for future study. The fact that individual differences in skin surface temperature exist also needs to be taken into account. Therefore, the usability limits of the present model need to be clarified. The measurement results we obtained suggest that the skin surface temperature distribution does not change in the chest, abdomen, forehead, and neck as it does in other regions. s shown in Figures and 6, there was little difference between the temperature of the converted image of the chest and the original image. We cannot claim complete validity from this result alone. Nonetheless, we should regard such regions, where the skin surface temperature can be precisely simulated, as important for evaluating IR images. This is because temperatures fohese regions can be used as a standard for normalizing the skin surface temperature distribution or for producing an index comparing the vascular activity level in the skin. On the other hand, the skin surface temperature distributions of the finger, hand, and foot change greatly since their vasoconstriction and vasodilation are active. However, it may be possible to evaluate the distribution of the skin blood flow change, for example, because the difference between the IR image converted by our method and the original image theoretically depends on the vasculahermal contribution. Hence, analyzing the converted images may reveal the individual characteristic of the vascular activity. In any case, in future work we must investigate in detail how similahe converted IR image of each region is to the original image under various thermal environments. Skin surface temperature depends not only on the thermal environment at one particular point in time but also on the internal temperature determined by a previous thermal environment. Hence, even under identical thermal environmental conditions the skin surface temperature distributions are often different due to differences in the internal temperatures. thermographer will ask a subject to rest for a certain period (more than 0 min is recommended []) before taking an IR image. nd even though the body temperature will not become steady within such a short period, a human thermal model can simulate the histories of the skin temperature and the body temperature from inputs of the histories of the thermal environmental conditions. It can also simulate how the skin surface temperature distribution of a subject who moved from a hot (or cold) environment will change. When converting IR images with a human thermal model, the thermal environment history must be taken into consideration. But there are few cases in which the thermal environment has been recorded, except for experiments in a test room such as those conducted in this study. The history of the thermal environmental conditions thus has to be estimated from the environment the subject has stayed in. Or, as an alternative, the measured core temperature can be used for compensating the simulated skin surface temperature. mong the advantages of using a human thermal model for IR imaging is that it makes it possible to provide common information to all researchers and to quantify the effect of certain factors on the skin surface temperature. Researchers can thus make use of the various calculated results to quantitatively analyze IR images. In this study, the calculated temperatures were applied to eliminate the influence of the thermal environment. We believe that our human thermal model can cover other diverse applications. Conclusions To evaluate IR images obtained under various thermal environmental conditions, we proposed a human thermal model with which IR images obtained under certain thermal environmental conditions can be converted into images under other conditions. The model was based on a numerical calculation of the bio-heat transfer equations that express heat transfer phenomena within the human body. 6-cylinder-segment model was used as the geometry of the human body. Comparisons of IR images with their converted images indicate that this method is effective in eliminating the influence of the thermal environmental conditions. However, the difference between the converted images and the original ones varies among segments. In future work, we will use this method to investigate the IR images of several subjects under various thermal environments. cknowledgment This work was supported in part by a grant titled Research fohe Future Program # 97I000 from the Japan Society fohe Promotion of Science. Naoto Kakuta received the Ph.D. degree in human engineering from Hokkaido University, Japan, in 999. He worked on biomedical engineering at the Center for Collaborative Research, the University of Tokyo, as a research associate in the Japan Society fohe Promotion of Science from He has been a research associate in the Department of Mechanical Engineering and Intelligent Systems, the University of Electro-Communications, Japan, since 00. His interests focus on measurement and simulation of heat transfer within a tissue and application of thermography for biomedical fields. Shintaro Yokoyama was born in Japan in 98. He received the B.S. degree in 97 and the M.S. degree in 97 in design engineering from Kyushu Institute of Design, Japan. He served from as a research associate in the Faculty of Engineering, Hokkaido University, Japan, and received the Ph.D. degree from Showa University, Japan, in 98. Since 988, he has been an associate professor in the Faculty of Engineering, Hokkaido University. His research interests include computer modeling in thermal physiology, development of a simulator of human-environment system, and a variety of issues about indoor air quality. Dr. November/December 00 IEEE ENGINEERING IN MEDICINE ND BIOLOGY 7 uthorized licensed use limited to: University of Illinois. Downloaded on ugust, 009 at 7: from IEEE Xplore. Restrictions apply.

8 Yokoyama is on the Board of the Society of Heating, ir-conditioning and Sanitary Engineers of Japan; the Japan Society of Physiological nthropology; the Japan Ergonomics Society, the Japanese Society of Biometeorology; and the Society of Human-Environment Systems. Kunihiko Mabuchi was born in Japan in 9 and graduated from the Faculty of Medicine, the University of Tokyo, in 976. fter working as a postgraduate student in medical engineering in the Institute of Medical Electronics, Faculty of Medicine, the University of Tokyo, he received the M.D. and Ph.D. degrees from the University of Tokyo in 986. He worked on biomedical engineering as a research associate in the Institute of Medical Electronics, Faculty of Medicine, from and as an associate professor in the Research Center for dvanced Science and Technology from From , Mabuchi was a professor in the Center for Collaborative Research, the University of Tokyo. Since pril 00, he has been a professor in the Graduate School of Information Science and Technology, the University of Tokyo, and his main research interests include development of artificial organs, application of thermography for biomedical fields, and application of virtual reality techniques for medicine. He is currently on the Board of Trustees of the Japanese Society of Thermology and the Japanese Society of Heat Transfer, and he is on the Council of the Japanese Society of rtificial Organs. ddress for Correspondence: Dr. Naoto Kakuta, Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, -- Chofugaoka, Chofu, Tokyo, 8-88 Japan. Tel: Fax: kakuta@rao.ymdlab.mce.uec.ac.jp. References [] K. Mabuchi, T. Chinzei, I. Fujimasa, S. Haeno, K. Motomura, Y. be, and T. Yonezawa, Evaluating asymmetrical thermal distributions through image processing, IEEE Eng. Med. Biol. Mag., vol. 7, pp. 7-, 998. [] I. Fujimasa, T. Chinzei, and I. Saito, Converting far infrared image information to other physiological data, IEEE Eng. Med. Biol. Mag., vol. 9, pp. 7-76, 000. [] R.B. Roemer and T.C. Cetas, pplications of bioheat transfer simulations in hyperthermia, Cancer Res., vol., pp , 98. []. Shitzaer and R.C. Eberhart, Eds., Heat Transfer in Medicine and Biology. New York: Plenum, 98. [] G.M.J. an Leeuwen,.N.T.J. Kotte, and J.J.W. Lagendijk, flexible algorithm for construction of -D vessel networks for use in thermal modeling, IEEE Trans. Biomed. Eng., vol., pp , 998. [6] R.G. Gordon, R.B. Rober, and S.M. Horvath, Mathematical model of human temperature regulatory system-transient cold exposure response, IEEE Trans. Biomed. Eng., vol., pp. -, 976. [7] S. Yokoyama and H. Ogino, Developing computer model for analysis of human cold tolerance, nn. Physiol. nthropol., vol., pp. 8-87, 98. [8].P. Gagge, J..J. Stolwijk, and Y. Nishi, n effective temperature scale based on a simple model of human physiological regulatory response, SHRE Trans.,vol.77,pp.7-6,97. [9] P.O. Fanger, Thermal Comfort. Copenhagen, Denmark: Danish Technical Press, 970. [0] S. Yokoyama, N. Kakuta, and K. Ochifuji, Development of a new algorithm for heat transfer equation in the human body and its applications, ppl. Human Sci., vol. 6, no., pp. -9, 997. [] S. Yokoyama, N. Kakuta, T. Togashi, Y. Hamada, M. Nakamura, and K. Ochifuji, Development of prediction computer program of whole body temperatures expressing local characteristic of each segment, Part Bio-heat equations and solving method, (in Japanese), Trans. SHSE Japan, vol. 77, pp. -, 000. [] N. Kakuta, S. Yokoyama, and K. Mabuchi, Development of a human thermal model and its applications fohermographic diagnosis, in Proc. st Joint BMES/IEEE EMBS Conf., tlanta, G, 999, p. 0. [] H.H. Pennes, nalysis of tissue and arterial blood temperatures in the resting human forearm, J. ppl. Physiol., vol., pp. 9-, 98. [] H. rkin and. Shitzer, model of thermoregulation in the human body, presented at SME Winter nnual Meeting, New Orleans, L, paper no. 8-W/HT-66, 98. [] J.W. Baish, P.S. yyaswamy, and K.R. Foster, Heat transport mechanisms in vasculaissues, SME J Biomech. Eng., vol. 08, pp. -, 986. [6] H. rkin, L.X. Xu, and K.R. Holmes, Recent developments in modeling heat transfer in blood perfused tissues, IEEE Trans. Biomed. Eng., vol., pp , 99. [7] E.H. Wissler, mathematical model of the human thermal system, Bull. Math. Biophysics, vol. 6, pp. 7-66, 96. [8] P.H. Oosthuizen and S. Madan, Combined convective heat transfer from horizontal cylinders in air, SME J Heat Transfer, vol. C-9, pp. 9-96, 970. [9] P.H. Oosthuizen and S. Madan, The effective of flow direction on combined convective heat transfer from cylinders to air, SME J. Heat Transfer, vol. C-9, pp. 0-, 97. [0] M.L. Toison, Infrared and Its Thermal pplications. The Netherlands: Philips Tech. Lib., 96. [] N. Kakuta, S. Yokoyama, M. Nakamura, and K. Mabuchi, Estimation of radiative heat transfer using a geometric human model, IEEE Trans. Biomed. Eng., vol. 8, pp. -, 00. [] Y. Kuno, Human Perspiration. Springfield, IL: Charles C. Thomas, 96. [] K. Ohara and T. Ono, Regional relationship of water vapor pressure of human body surface, J. ppl. Physiol., vol. 8, pp. 09-0, 96. [] H. Hensel, Thermoreception and Temperature Regulation. London and New York: cademic, 98. [] C.R. Wyss, G.L. Brengelmann, J.M. Johnson, L.B. Rowell, and M. Niederberger, Control of skin blood flow, sweating, and heart rate: Role of skin vs. core temperature, J. ppl. Physiol., vol., no. 6, pp. 76-7, 97. [6] C.R. Wyss, G.L. Brengelmann, J.M. Johnson, L.B. Rowell, and M. Niederberger, ltered control of skin blood flow at high skin and core temperature, J. ppl. Physiol., vol. 8, no., pp. 89-8, 97. [7] C.B. Wenger, M.F. Roberts, J..J. Stolwijk, and E.R. Nadel, Forearm blood flow during body temperature transients produced by leg exercise, J. ppl. Physiol., vol. 8, no., pp. 8-6, 97. [8] M.. Savage and G.L. Brengelmann, Control of skin blood flow in the neutral zone of human body, J. ppl. Phys., vol. 80, no., pp. 9-7, 996. [9] S. Yokoyama, N. Kakuta, T. Togashi, Y. Hamada, M. Nakamura, and K. Ochifuji, Development of prediction computer program of whole body temperatures expressing local characteristic of each segment, Part nalysis of the mathematical model fohe control of skin blood flow, (in Japanese), Trans. SHSE Japan, vol. 78, no. -8, 000. [] P. Webb, Temperatures of skin, subcutaneous tissue, muscle and core in resting men in cold, comfortable and hot conditions, Euro. J. ppl. Phys., vol. 6, pp. 7-76, 99. [] J.G. Stone, R.R. Goodman, K.Z. Baker, C.J. Baker, and R.. Solomon, Direct intraoperative measurement of human brain temperature, Neurosurgery, vol., no., pp. 0-, 997. [] J.N. Hayward and M.. Baker, Role of cerebral arterial blood in the regulation of brain temperature in the monkey, m. J. Phys., vol., pp. 89-0, 968. []. Spitzer, M.J. ckerman,.l. Scherzinger, and D. Whitlock, The visible human male: technical report," J. m. Med. Inform. ssoc., vol., no., pp. 8-, 996. [] J. Werner, M. Buse, and. Foegen, Lumped versus distributed thermoregulatory control: Results from a three-dimensional dynamic model, Biol. Cybern., vol. 6, pp. 6-7, 989. [] R.J. Clark and M.L. de Calcina-Goff, Guidelines for standardization in medical thermography, Biomed. Thermo., vol. 9, no., pp. 9-, IEEE ENGINEERING IN MEDICINE ND BIOLOGY November/December 00 uthorized licensed use limited to: University of Illinois. Downloaded on ugust, 009 at 7: from IEEE Xplore. Restrictions apply.