Digital Twin for Imaging Skin - Revision history

Mtm3285 at 19:53, 13 December 2024

2024-12-13T19:53:35Z

← Older revision		Revision as of 19:53, 13 December 2024
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	Melanie:		Melanie:
			*Improvement of skin modeling in MCMatlab for better fit to experimental data
			*Generating reflection/color charts in ISETCam
			*Presentation and final report

	Clarisse:		Clarisse:
	*Improvement of skin modeling in MCMatlab for better fit to experimental data		*Improvement of skin modeling in MCMatlab for better fit to experimental data
	*Simulating sensor ~~repsonse~~ to skin reflections		*Simulating sensor response to skin reflections
	*Processing raw pixel data to predict distinguishability		*Processing raw pixel data to predict distinguishability
	*Presentation and final report		*Presentation and final report

CwoodahlS: /* Melanin and Blood Oxygen Level Sensing */

2024-12-13T19:50:08Z

Melanin and Blood Oxygen Level Sensing

← Older revision		Revision as of 19:50, 13 December 2024
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	==Results==		==Results==
	===Melanin and Blood Oxygen Level Sensing===		===Melanin and Blood Oxygen Level Sensing===
	Using MCMatlab with the absorption and scattering coefficients discussed in the background and methods section, the output spectrums are obtained for varying skin melanin content and blood oxygen levels. Higher melanin counts show reduced sensitivity to blood oxygen level changes. For this reason, shown are the two melanin content extremes, with type I skin containing a low percentage of melanin, and type VI containing a high percentage of melanin. In figure 9, the expected features in the type I epidermis can be observed between 500 nm and 600 nm from blood absorption, as well are the expected significant jump in reflection past 600 nm. In the type VI epidermis we observe the expected reduction of these features caused by the increased melanin count.		Using MCMatlab with the absorption and scattering coefficients discussed in the background and methods section, the output spectrums are obtained for varying skin melanin content and blood oxygen levels. Higher melanin counts show reduced sensitivity to blood oxygen level changes. For this reason, shown are the two melanin content extremes, with type I skin containing a low percentage of melanin, and type VI containing a high percentage of melanin. In figure 9, the expected features in the type I epidermis can be observed between 500 nm and 600 nm from blood absorption, as well are the expected significant jump in reflection past 600 nm (see Figure 3). In the type VI epidermis we observe the expected reduction of these features caused by the increased melanin count (see Figure 3).

	[[File:TypeI and Type6 Epi Reflections.png\|600 px\|center\|thumb\|Figure 9. a) The reflection spectrum from MCMatlab for a low melanin content, type I epidermis. The reflection values between 600 nm and 700 nm show dependency on blood oxygen content. b) The reflection spectrum of MCMatlab for a high melanin content, type VI epidermis.]]		[[File:TypeI and Type6 Epi Reflections.png\|600 px\|center\|thumb\|Figure 9. a) The reflection spectrum from MCMatlab for a low melanin content, type I epidermis. The reflection values between 600 nm and 700 nm show dependency on blood oxygen content. b) The reflection spectrum of MCMatlab for a high melanin content, type VI epidermis.]]

Mtm3285 at 19:47, 13 December 2024

2024-12-13T19:47:55Z

← Older revision		Revision as of 19:47, 13 December 2024
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	[[File:DTISEq10.png\|630 px\|center]]		[[File:DTISEq10.png\|630 px\|center]]

	The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|100 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|30 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|~~100~~ px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|30 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|~~100~~ px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|30 px]].		The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|100 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|30 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|95 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|30 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|105 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|30 px]].

	Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.		Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.

Mtm3285 at 19:47, 13 December 2024

2024-12-13T19:47:01Z

← Older revision		Revision as of 19:47, 13 December 2024
Line 53:		Line 53:
	[[File:DTISEq10.png\|630 px\|center]]		[[File:DTISEq10.png\|630 px\|center]]

	The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|70 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|30 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|70 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|30 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|70 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|30 px]].		The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|100 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|30 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|100 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|30 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|100 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|30 px]].

	Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.		Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.

CwoodahlS: /* Introduction */

2024-12-13T19:46:30Z

Introduction

← Older revision		Revision as of 19:46, 13 December 2024
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	==Introduction==		==Introduction==
	Blood oxygen saturation is an important health indicator. When blood oxygen levels drop below a certain threshold, extremely dangerous, even life-threatening, conditions can occur due to the lack of oxygen reaching vital organs. Platforms to monitor blood oxygen saturation have therefore been a sought-after solution, as seen in the development of pulse oximeter finger monitors and the addition of monitors onto smartwatches, such as the Apple Watch. ~~Both of these types of~~ monitors use reflected light to perform measurements since oxygen levels affect the reflectance characteristics of blood within the skin.		Blood oxygen saturation is an important health indicator. When blood oxygen levels drop below a certain threshold, extremely dangerous, even life-threatening, conditions can occur due to the lack of oxygen reaching vital organs. Platforms to monitor blood oxygen saturation have therefore been a sought-after solution, as seen in the development of pulse oximeter finger monitors and the addition of monitors onto smartwatches, such as the Apple Watch. These monitors use reflected light to perform measurements since blood oxygen levels affect the reflectance characteristics of blood within the skin.

	Another skin chromophore that affects reflectance characteristics of skin is melanin content. The distribution of melanin throughout the skin is an important indicator for skin cancers and skin health, and can affect blood oxygen measurements, making it imperative to consider.		Another skin chromophore that affects reflectance characteristics of skin is melanin content. The distribution of melanin throughout the skin is an important indicator for skin cancers and skin health, and can affect blood oxygen measurements, making it imperative to consider.

Mtm3285 at 19:46, 13 December 2024

2024-12-13T19:46:11Z

← Older revision		Revision as of 19:46, 13 December 2024
Line 53:		Line 53:
	[[File:DTISEq10.png\|630 px\|center]]		[[File:DTISEq10.png\|630 px\|center]]

	The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|50 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|25 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|50 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|25 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|50 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|25 px]].		The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|70 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|30 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|70 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|30 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|70 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|30 px]].

	Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.		Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.

Mtm3285 at 19:45, 13 December 2024

2024-12-13T19:45:26Z

← Older revision		Revision as of 19:45, 13 December 2024
Line 53:		Line 53:
	[[File:DTISEq10.png\|630 px\|center]]		[[File:DTISEq10.png\|630 px\|center]]

	The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|25 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|50 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|50 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|50 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|50 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|50 px]].		The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|50 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|25 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|50 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|25 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|50 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|25 px]].

	Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.		Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.

Mtm3285 at 19:44, 13 December 2024

2024-12-13T19:44:03Z

← Older revision		Revision as of 19:44, 13 December 2024
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	[[File:DTISEq9.png\|180 px\|center]]		[[File:DTISEq9.png\|180 px\|center]]

	where [[File:DTISvar14.png\|95 px]], and [[File:DTISvar15.png\|18 px]] depends on the size of the scatter under consideration. For skin tissue, this is assumed to be 0.6. These absorption and scattering functions for blood and skin can be used to model and understand reflectance data for various skin types. In Figure 3, examples of measured reflectance spectra are plotted for various skin types [2].		where [[File:DTISvar14.png\|95 px]], and [[File:DTISvar15.png\|16 px]] depends on the size of the scatter under consideration. For skin tissue, this is assumed to be 0.6. These absorption and scattering functions for blood and skin can be used to model and understand reflectance data for various skin types. In Figure 3, examples of measured reflectance spectra are plotted for various skin types [2].

	[[File:DTSIFigure3.png\|600 px\|center\|thumb\|Figure 3. Experimentally measured skin reflectance spectra for different skin types]]		[[File:DTSIFigure3.png\|600 px\|center\|thumb\|Figure 3. Experimentally measured skin reflectance spectra for different skin types]]
Line 53:		Line 53:
	[[File:DTISEq10.png\|630 px\|center]]		[[File:DTISEq10.png\|630 px\|center]]

	The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|20 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|20 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|50 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|20 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|25 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|50 px]].		The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|25 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|50 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|50 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|50 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|50 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|50 px]].

	Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.		Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.

CwoodahlS: /* Results */

2024-12-13T19:43:52Z

Results

← Older revision		Revision as of 19:43, 13 December 2024
Line 102:		Line 102:
	==Results==		==Results==
	===Melanin and Blood Oxygen Level Sensing===		===Melanin and Blood Oxygen Level Sensing===
	Using MCMatlab with the absorption and scattering coefficients discussed in the background and methods section, the output spectrums are obtained for varying skin melanin content and blood oxygen levels. Higher melanin counts show reduced sensitivity to blood oxygen level changes. For this reason, shown are the two melanin content extremes, with type I skin containing a low percentage of melanin, and type VI containing a high percentage of melanin. ~~We can see~~ the expected features in the type I epidermis between 500 nm and 600 nm from blood absorption, as well are the expected significant jump in reflection past 600 nm. In the type VI epidermis we observe the expected reduction of these features caused by the increased melanin count.		Using MCMatlab with the absorption and scattering coefficients discussed in the background and methods section, the output spectrums are obtained for varying skin melanin content and blood oxygen levels. Higher melanin counts show reduced sensitivity to blood oxygen level changes. For this reason, shown are the two melanin content extremes, with type I skin containing a low percentage of melanin, and type VI containing a high percentage of melanin. In figure 9, the expected features in the type I epidermis can be observed between 500 nm and 600 nm from blood absorption, as well are the expected significant jump in reflection past 600 nm. In the type VI epidermis we observe the expected reduction of these features caused by the increased melanin count.

	[[File:TypeI and Type6 Epi Reflections.png\|600 px\|center\|thumb\|Figure 9. a) The reflection spectrum from MCMatlab for a low melanin content, type I epidermis. The reflection values between 600 nm and 700 nm show dependency on blood oxygen content. b) The reflection spectrum of MCMatlab for a high melanin content, type VI epidermis.]]		[[File:TypeI and Type6 Epi Reflections.png\|600 px\|center\|thumb\|Figure 9. a) The reflection spectrum from MCMatlab for a low melanin content, type I epidermis. The reflection values between 600 nm and 700 nm show dependency on blood oxygen content. b) The reflection spectrum of MCMatlab for a high melanin content, type VI epidermis.]]

CwoodahlS: /* Melanin and Blood Oxygen Level Sensing */

2024-12-13T19:42:20Z

Melanin and Blood Oxygen Level Sensing

← Older revision		Revision as of 19:42, 13 December 2024
Line 102:		Line 102:
	==Results==		==Results==
	===Melanin and Blood Oxygen Level Sensing===		===Melanin and Blood Oxygen Level Sensing===
	Using MCMatlab with the absorption and scattering coefficients discussed in the background and methods section, the output spectrums are obtained for varying skin melanin content and blood oxygen levels. Higher melanin counts show reduced sensitivity to blood oxygen level changes. For this reason, shown are the two melanin content extremes, with type I skin containing a low percentage of melanin, and type VI containing a high percentage of melanin.		Using MCMatlab with the absorption and scattering coefficients discussed in the background and methods section, the output spectrums are obtained for varying skin melanin content and blood oxygen levels. Higher melanin counts show reduced sensitivity to blood oxygen level changes. For this reason, shown are the two melanin content extremes, with type I skin containing a low percentage of melanin, and type VI containing a high percentage of melanin. We can see the expected features in the type I epidermis between 500 nm and 600 nm from blood absorption, as well are the expected significant jump in reflection past 600 nm. In the type VI epidermis we observe the expected reduction of these features caused by the increased melanin count.

	[[File:TypeI and Type6 Epi Reflections.png\|600 px\|center\|thumb\|Figure 9. a) The reflection spectrum from MCMatlab for a low melanin content, type I epidermis. The reflection values between 600 nm and 700 nm show dependency on blood oxygen content. b) The reflection spectrum of MCMatlab for a high melanin content, type VI epidermis.]]		[[File:TypeI and Type6 Epi Reflections.png\|600 px\|center\|thumb\|Figure 9. a) The reflection spectrum from MCMatlab for a low melanin content, type I epidermis. The reflection values between 600 nm and 700 nm show dependency on blood oxygen content. b) The reflection spectrum of MCMatlab for a high melanin content, type VI epidermis.]]

← Older revision		Revision as of 19:44, 13 December 2024
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	[[File:DTISEq9.png\|180 px\|center]]		[[File:DTISEq9.png\|180 px\|center]]

	where [[File:DTISvar14.png\|95 px]], and [[File:DTISvar15.png\|18 px]] depends on the size of the scatter under consideration. For skin tissue, this is assumed to be 0.6. These absorption and scattering functions for blood and skin can be used to model and understand reflectance data for various skin types. In Figure 3, examples of measured reflectance spectra are plotted for various skin types [2].		where [[File:DTISvar14.png\|95 px]], and [[File:DTISvar15.png\|16 px]] depends on the size of the scatter under consideration. For skin tissue, this is assumed to be 0.6. These absorption and scattering functions for blood and skin can be used to model and understand reflectance data for various skin types. In Figure 3, examples of measured reflectance spectra are plotted for various skin types [2].

	[[File:DTSIFigure3.png\|600 px\|center\|thumb\|Figure 3. Experimentally measured skin reflectance spectra for different skin types]]		[[File:DTSIFigure3.png\|600 px\|center\|thumb\|Figure 3. Experimentally measured skin reflectance spectra for different skin types]]
Line 53:		Line 53:
	[[File:DTISEq10.png\|630 px\|center]]		[[File:DTISEq10.png\|630 px\|center]]

	The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|20 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|20 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|50 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|20 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|25 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|50 px]].		The Radiative Transfer Equation describes how radiation undergoes absorption, scattering, and extinction processes as it propagates in a medium [7]. [[File:DTISvar16.png\|20 px]] is the spectral radiance of a beam of light, which can be thought of as an intensity per solid angle for a specific wavelength. The Radiative Transfer Equation measures changes in the spectral radiance of a beam over some distance in a volume described by the coordinates x, y, and z. These changes are quantified by the sum of the incident spectral radiance, [[File:DTISvar17.png\|25 px]], weighted by an extinction coefficient, [[File:DTISvar18.png\|50 px]], plus the scattered spectral radiance, [[File:DTISvar19.png\|50 px]], weighted by a scattering coefficient, [[File:DTISvar20.png\|50 px]], plus the spectral radiance of thermal emission, [[File:DTISvar21.png\|50 px]], weighted by an absorption coefficient, [[File:DTISvar22.png\|50 px]].

	Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.		Because these processes are stochastic, solving this equation requires numerical methods like Monte-Carlo. In this case, the Monte-Carlo simulation from MCmatlab uses small timesteps to advance the motion of a photon within a specified volume for a specified number of photons and initial trajectories [6]. The size of the timesteps varies randomly as photons propagate through the medium with given attenuation properties. Absorption in the medium is modeled by numerically reducing the energy of a photon. This can also result in termination of the photon if it falls below a certain threshold.The portions of the photons that don’t experience absorption are given an angular change in their trajectory in order to emulate scattering.