DE4313258A1 - Method and device for the quantitative measurement of the texture of the human skin surface by means of registration, reproduction and analysis of image information - Google Patents

Description

The invention relates to a method for quantitative Determination of the texture of the human skin surface and an apparatus for performing the method.

For qualitative analysis of the structure of the human Skin surfaces in medicine and cosmetics have been around for two Decades of roughness parameters, like them for example in DIN 4762 or also in ISO 4287/1 are set. Since methods are generally used in vivo the surface measurement technology, will representatively measuring a replicate. The Measurement itself is done by groping, where mechanical and optical processes are used.

In a known mechanical method for Determination of the parameters for the stratum corneum  with an electrical stylus device in individual Vertical cuts on the surface profile of Replicas the skin surface scanned with a probe tip. In doing so, those obtained against a reference line Profile deviations fed to an evaluation computer.

From the measured variables, the latter can determine characteristic values such as those frequently used in dermatological research
the arithmetic mean roughness value R _α ,
the root mean square roughness R _q and
the maximum roughness depth R _max
carry out. In various studies, the measurement of individual profile sections of replicas of the skin surface and the subsequent determination of roughness parameters showed, for example, the skin-smoothing effectiveness of cosmetics, the influence of aging and disease on the roughness parameters of the skin was demonstrated and the characterization of dry skin was carried out.

This known method is generally in DIN 4768 described and regarding the application to the human skin - especially with regard to the Detection of anisotropy - in German Patent specification 27 19 341 laid down.

Typical for such a microtopographic measurement with an electrical stylus is that only individual profile sections are evaluated, so just a linear one and not a flat one Information is available.  

In some recent works, one has already been Measure a large number of parallel profile cuts by one improved three-dimensional impression of the skin relief to investigate. Because of the mechanical Sampling was associated with the high expenditure of time Laser Profilometry for the first time recently used. But even in this case it is for one accurate 3D measurement of a skin area a not to estimate unimportant measuring time. Also done So far only an evaluation with this method based on that for individual profile cuts defined roughness parameters, in which already by definition, no information about the Skin texture is included.

Image analytical processes have also already been used isolated to describe the human skin used, with gray scale images of replicas as Basis for subsequent evaluation procedures served. In particular Corcuff (Corcuff, P .; de Rigal, J .; Leveque, J.L .: Skin Relief and Aging; Journal of the Society of Cosmetic Chemists 34; 177-190; 7/1983) developed a method to mechanical To substitute roughness measurement. The method is based on the fact that when lighting a replica at a defined angle of illumination from the Length of the shadows of the profile tips one Height information calculated from the gray scale images can be. A significant correlation between image analytical and profilometric roughness values could not yet be shown. Also there was no reason to do so in the grayscale images contained information about the skin field this image processing method to a to use further analysis, since the evaluation Hand of binary images, that is, images that are only  the values black and white can take place. A further analysis was therefore not used.

Result from the known measuring methods mentioned hence parameters (any integral means), which are fundamentally unable to selective description of a fixed measuring point on a Skin surface d. H. they are leaving Basically texture recognition and texture description out.

No information can also do that in the German Patent 34 33 195 described image analytical Deliver procedures in which the depth of skin folds and the sulcus cutis using a Video camera can be determined. Because of that opto-electronic converter generated video signal too evaluating surface is made by choosing more suitable Thresholds binarized.

The invention is therefore based on the object Corcuff's method mentioned at the beginning to further develop the roughness parameters at image-analytical microtopography of the skin surface determine the areal extent of the stratum Consider corneums.

A method is used to solve this problem proposed by the content of the claims is marked. The content of the claims is too Part of this description.

When considering both natural picture scenes human as well as gray-scale images Eye certain areas that have different structures and contain patterns. Human too  Skin surface with its characteristic field exhibits such a more or less pronounced Structuring on. The appearance of such patterns will commonly referred to as texture. The following will hence as an essential property of a texture the repetition of small within a picture Areas that contain a particular structure Understood.

The development of the invention is based on a Texture analysis, in which one turns the Grayscale images of the skin or replicas from the served human skin surface, however again only evaluated binary images and thus a few texture parameters can be determined. The Step a) and b) according to the invention, however, deliver Characteristics and parameters, which provide information about the Connections and dependencies between neighboring gray values or also gray value regions deliver deliver. They are essentially based on the Development of the methods presented in the invention the texture analysis of human skin, the determination the parameters of the co-occurrence matrix (9) and the Run-length matrix (10).

Production of gray scale images

The gray value images examined below are taken from the skin or replicas (material: silicone rubber compound Silflo® from the manufacturer Flexico, for contrast enhancement mixed with the finest iron powder) of the human skin via a microscope and a CCD camera. The replica is illuminated by a cold light source, the position of the light source relative to the replica according to Fig. 1 being fixed. Starting from the center of the replica, the angle between the horizontal plane and the illumination source is defined as α, the angle of incidence; β, the illumination angle, denotes the angle between a selected reference line in the horizontal plane and the projection of the illumination source onto it. The gray-scale images of the replicas of the human skin surface or skin are fed to an evaluation computer via an image processing system and stored in a square matrix G = [g (i _z , i _s )] with the running indices i _z in the row or i _s in the column direction .

The format of the matrix is (N, N). The matrix elements g (i _z , i _s ) are the quantized gray values g _{w of} the N² pixels. Typically 256 different gray value levels are used in digital image processing, ie the gray values lie in the interval [O, N _g ] N _g denotes the maximum gray value that occurs. The gray value g _w = 0 is interpreted as black, the gray value g _w = 255 as white.

A device according to the invention for producing the gray scale images and for determining the skin texture is described in Fig. 1 and consists of a cold light source for illuminating the sample (skin, replica), a microscope and a C'C'D camera for recording the gray scale images and an image processing system and an evaluation computer for the gray value images. The devices are commercially available.

Statistical parameters

Various parameters are known from the statistics, which in general terms to the present two-dimensional and discrete image templates applied can be. Based on the definitions for the Moments of a distribution can use these parameters for a gray scale image of the skin surface can be determined:

Zero-order moments:

Central moments of the nth order:

The following statistical parameters become Characterization of distributions used:

As with the definition of these parameters there is no information about the assignment of individual pixels to each other in the image plane, as shown below allow presented parameters. Just like that Use of only for individual profile cuts defined roughness parameters for a 3D relief only a limited meaningfulness about the structure of a You get surface with the use of these statistical parameters yet no statements about Allocations of individual gray values to each other in the Area. They are only well suited to one Impression about the distribution of all that occur in the picture To deliver gray values.

Such statistically determined parameters are therefore not to define the areal extent of the Suitable for stratum corneums as they only make statements about the Make gray value distribution. To determine the entire texture, therefore, further, Parameters according to the invention, as they are known from the so-called. Co-Occurrence-Matrix (a) and the Run-Length-Matrix to be delivered.  

Characteristic values of the co-occurrence matrix

When determining the parameters of the invention Co-occurrence matrix will be the relationships that between the single point of a texture and its Environment exist, used to be descriptive Filter out texture parameters. Haralick developed the basic principles of this procedure and applied it to a problem with the evaluation of aerial photographs (Haralick, R.M .; Shanuga, K .; Dinstein, I.: Texture Features for Image Classification; IEEE transactions on Systems, Man and Cybernetics 3; 610-621; 1979).

The content of the investigation using the co-occurrence matrix is the gray value transition. This is understood to mean the combination that is formed by two pixels within a gray value matrix. This is to be illustrated using the sequence [00012]. The combinations [0,0] appear twice and the combinations [0,1] and [1,2] each as gray value transitions to the right neighbor. The co-occurrence matrix C _{Φ , l} now contains the number of all combinations of the gray value transitions between two pixels of the gray value matrix G. The individual elements c _{Φ , l} (i, j) of the co-occurrence matrix are then calculated as follows:

_{Let the} gray value g _{w1 of} a first pixel of the gray value _matrix be i, the gray value g _w2 of another pixel of the gray value _matrix be j. The matrix element c _{Φ , l} (i, j) contains the number of gray value combinations [i, j] that occur in G. The positional relationship between the two examined pixels is expressed by the indices Φ and l of the co-occurrence matrix C _{Φ , l} . The displacement angle Φ is the angle that lies between the connection of the two pixels under consideration and the column direction in the gray value matrix G. Four directions of the square gray value matrix are considered: row and column directions and the two diagonal directions. The displacement length 1 denotes the distance that lies between the pixels.

If you now consider a gray-scale image with 256 Grayscale levels is formed, so the Co-Occurrence Matrix have the format 256.256). Around keep the computing effort as low as possible, the gray values are therefore re-quantized. Is common a reduction to 32 or 64 gray values, so that the co-occurrence matrices then created the format (32.32) or (64.64).

An image with a large, low-contrast Structure creates a co-occurrence matrix whose Elements strongly aligned along the main diagonals are. A fine, high-contrast structure creates in contrast, a wide distribution of the elements in the Co-Occurrence Matrix.

You can now use the co-occurrence matrix calculate various parameters, the conclusions on the type of texture, e.g. B. coarse or fine, allow or also show orientations of a texture and such allow a differentiation of the individual textures. The characteristic values are briefly presented below be used for the investigation of the Grayscale images of human skin are used become. The elements of the co-occurrence matrix are  first by the sum of all entries in of the matrix normalized.

The element c (i, j) of C _{Φ , l contains} the number of gray value combinations [i, j] that occur in G.
Contrast:

The contrast is a measure of the variation of gray values in an image. A lower value is therefore calculated for a low-contrast image than for an image with many gray value changes. The elements of the co-occurrence matrix that are further away from the main axis are emphasized.
Entropy:

The behavior of this characteristic value is similar to that of the contrast, ie a lower entropy value is calculated for a homogeneous image than for a finely structured one.
Second angular moment:

This characteristic value is a measure of the homogeneity of an image. A larger characteristic value is calculated for a homogeneous image than for an inhomogeneous image.

The correlation is a measure of the dependency of the Rows and columns of the co-occurrence matrix among themselves. An inhomogeneous picture therefore has a little correlation.

Characteristic values of the run-length matrix

One way to get characteristic values of grayscale images because of the relationships and dependencies that exist between individual pixels, to calculate represent the characteristic values of the run-length matrix. First Approaches to this method have been developed by Galloway (Galloway, M.M .: Texture Analysis Using Gray Level Run Matrices; Computer Graphics and Image Processing 4, 172-179, 1975).

The gray value matrix G is examined in the method according to the run lengths of a gray value. The number of successive image points that have the same gray value within an examination direction is understood as the run length l _g . This should be briefly illustrated using the sequence [2223322]. For the gray value g _w = 2, one gets the barrel length l _g = 3 and once the barrel length l _g = 2, the gray scale value g _w : 3 occurs here with the barrel length l _g = 2.

The elements r _Φ (g _w , l _g ) of the run-length matrix R _Φ for a gray value matrix G then contain the number with which a gray value g _{w occurs} with the run length l _g . The index Φ indicates the direction of examination in which the individual run lengths are to be determined. As with the co-occurrence matrix, the row, column and the two diagonal directions of the gray scale matrix are examined for skin analysis.

For N _g different grayscale levels and a grayscale matrix of the format (N, N) results for the run-length matrix R _Φ as the maximum size N _g × N. Therefore, the grayscale values are usually re-quantized here, so that only 32 or 64 different grayscale levels can be used to calculate these matrices.

A homogeneous image will have large elements r _Φ (g _w , l _g ) on the far right, whereas the run-length matrix of an inhomogeneous image that has only very short run lengths l _g will appear as 'left-handed'. A texture aligned linearly along an examination direction will have large matrix elements in the long run lengths, whereas the run length examination carried out perpendicular to this orientation will have large elements for the short run lengths. The alignment of a texture can be recognized on the basis of this analysis method.

Here, too, characteristic values are calculated, which allow a further differentiation between different textures. The new parameters used in skin surface analysis are briefly presented below. N _l denotes the maximum run length occurring in the matrix R _Φ .
Short Run Emphasis:

This characteristic value emphasizes the short run lengths. As a result, larger characteristic values are calculated for finely structured gray-scale images than for large-area ones.
Long run emphasis:

This characteristic is exactly the opposite of the short Run empasis. A coarse picture gets one larger characteristic value than a finely structured.

Just the comparison of these two parameters in different directions of investigation enables to recognize a preferred orientation of the texture.

Gray level nonuniformity:

If the run lengths are evenly distributed over all gray values, the smallest value is calculated. The value of the parameter becomes large due to high run length values.
Runlength Nonuniformity:

Here, with an even distribution of Gray values over the run lengths the value of this Parameters minimal.

Influence of the lighting angle β
It was first examined to what extent the texture parameters of human skin depend on the illumination angle β, since such a dependence is important for the positioning of the replicas under the microscope. This was done in 5 ° steps from β = 0 ° to β = 360 ° grayscale images of skin or a replica created from the volar forearm of a 25-year-old male subject with healthy skin. The skin of the volar forearm already shows a pronounced direction of the main fold when viewed with the naked eye. The questions are now of interest:

In which value range are the parameters typically for different lighting angles β?
Can preferred directions such as the clearly visible main fold direction be detected using the texture parameters?
For the investigation, the angle of incidence α was set to constant α = 28 °. For β = 0 °, the replica, whose grayscale image for this direction of illumination is shown in Fig. 2, was aligned perpendicular to the clearly visible main fold direction. To determine the texture parameters, 256 × 256 gray-scale image sections with a size of 5.12 × 5.12 mm² were evaluated.

The parameters of the co-occurrence matrix C _{Φ , l} = C _{90 °, 1} and some parameters of the run length matrix R _o = R _{90 °} above the direction of illumination β = 0 are shown in Fig. 3 as examples for different run lengths and directions of investigation ° to β = 360 °, because the course of the texture parameters is particularly concise for this shift angle Φ and this shift length l. A clear dependency in all parameters on the direction of illumination can be seen. The parameter Run Percentage R _RP , not shown in Fig. 3, behaves similarly to the Short Run Emphasis R _SRE , the Gray Level Nonuniformity R _GLN and the Run Length Nonuniformity R _RLN . A more or less pronounced periodicity of the texture parameters is very clearly visible, whereby extreme values of the texture parameters, depending on their definition, consistently occurred at β = 0 °, 90 °, 180 °, 270 °, either when illuminated perpendicular to the main fold direction or in the main fold direction . A preferred direction can therefore be easily quantified on the basis of a change in the direction of illumination with all texture parameters.

This should be illustrated using a parameter, the contrast C _CON . For the illumination angle β = 0 °, ie perpendicular to the main _fold direction, C _CON has a minimum in the direction Φ = 90 °, ie parallel to the main fold direction. If one proceeds in this direction of investigation, i.e. along the main fold direction, the gray value transitions in the co-occurrence matrix C _{90 °, 1 are} characterized by a large number of combinations of the same or closely spaced gray values, as can be easily understood from Fig. 2 . The elements of the co-occurrence matrix therefore lie closely around their main diagonal, the parameter contrast C _CON is ergo small, since according to the definition equation (8) the distance to the main diagonal is squared in this parameter. In contrast to this, when the illumination is from the direction β = 90 °, that is, along the main fold direction, even minor skin irregularities in the fold direction are emphasized; the gray value transitions then contain more combinations of gray values that are further apart. The co-occurrence matrix along the main diagonals is thus covered in a wider band and the contrast C _CON reaches a maximum for illumination in the main fold direction β = 90 ° for the displacement angle Φ = 90 °.

For the further examinations, all replicas were provided with a mark, which determines the orientation of the replica to the body axis, since it can be clearly seen from Fig. 3 that all texture parameters cover a more or less large value range when the illumination angle β is varied. The Replicas were then aligned under the microscope using this mark.

Characterization of the skin surface different Body parts

Texture parameters of eight different skin areas of a 24-year-old male subject with healthy skin were also determined, whereby impressions according to Fig. 4 are taken at the following eight places:

• a. Face (so-called crow's feet in the area of the Temples),
• b. Neck,
• d. Upper arm,
• e. Forearm, volar,
• f. Abdomen,
• c. Lumbar region,
• G. Thigh, ventral,
• H. Lower leg, dorsal.

The typical, sometimes very different skin structure of these individual skin areas is also documented in Fig. 4. The replicas were created in a size (approx. 10 cm²) so that they could be evaluated on six different measuring fields. The texture parameters were determined from these measurement fields, and the mean values, the largest and the smallest occurring value of the texture parameters of each body part were then determined from the parameters thus obtained.

Some particularly meaningful texture parameters are discussed below. Fig. 5 shows the eight skin areas as bar graphs

• - of the statistical characteristic values, the mean value M _gw and the excess _γ ₂ of the gray value distribution,
• - From the parameters of the co-occurrence matrix G _{0 °, 1} the parameters second angular moment C _ASM , correlation and contrast as open bars are also some parameters of the co-occurrence matrix G _{135 °, 1} as filled and the matrix G _{90 °, 1} plotted as dotted bars,
• - from the parameters of the run length matrix R _{0 °} the parameters Short Run Emphasis R _SRE , Long Run Emphasis R _LRE and Run Length Nonuniformity R _RLN .

In Fig. 5, the band is plotted as a bar, in which the respective parameter determined on six different measuring fields of a skin area is located. The average of these six values is indicated by a horizontal line through the bar.

Using the eight different gray-scale images in Fig. 4, it is possible for a viewer to differentiate between the skin areas, although the roughness parameters of the skin areas under consideration are sometimes very close to one another. In contrast to the roughness parameters, the texture parameters allow a relatively simple classification of the individual skin areas.

Even the evaluation of the statistical parameters in Fig. 5 shows that the individual skin areas can be roughly differentiated with these parameters. The mean gray _scale value m _{gw of} the gray scale distribution shows considerable differences in the individual skin areas. This is due to the different degrees of expression of the skin folds, which in turn causes more or less shading from the lighting. It is unfavorable for a characterization of different skin areas, however, that the band in which the six characteristic values of a skin area are located is sometimes quite large. In contrast, the excess of the gray value distribution is the most suitable parameter for differentiating the skin areas from the statistical characteristic values, since here there is predominantly a very narrow band of the six characteristic values. The statistical parameters for differentiating the skin areas are not yet sufficient for the value ranges of some areas of the skin, for example in the neck and abdomen or in the upper and lower legs. Here, further information according to the invention from the characteristic values of the co-occurrence matrix and the run-length matrix is required for a classification.

Using the parameters of the co-occurrence matrix C _{0 °, 1} , distinctions can be made directly for almost all skin areas under consideration. For example, the areas of the upper arm, lower leg, lower leg and lumbar region can be reliably detected using the contrast C _CON alone. Small values of this parameter are obtained for the lower and upper thighs and for the upper arm. A structure identified by folds that are not deep is found here. In contrast, a strong contrast determines the images of the neck and abdomen in the direction Φ = 0 °, since the skin fold direction is perpendicular to this direction; For Φ = 90 ° the contrast in these two skin areas is therefore significantly less pronounced, shown in Fig. 5 as a dotted bar.

If the information from the second angular moment C _{ASM is} also added, a broad classification of the skin areas is possible apart from the distinction between face and forearm as well as neck and abdomen, which each include similar parameter ranges. The information shown from a displacement angle aus and a displacement length l is not sufficient for the latter combinations. If, however, a further displacement length is evaluated, as shown in Fig. 5 for the contrast for Φ = 135 ° as a solid bar in some skin areas, a complete classification of all skin areas based on the co-occurrence parameters used is given.

It does not show the same tendencies as the contrast shown entropy; which are each six values but can be found in a slightly narrower volume also useful to differentiate the individual skin areas can be used. The correlation shows very little for Φ = 0 ° for the individual skin areas strong differences; for other displacement angles Φ but clearer differentiations are possible.

The parameters of the run-length matrix can also be used to characterize the skin surface. Short Run Emphasis R _SRE and Long Emphasis R _LRE move in opposite directions, as was to be expected by definition. The Short Run Emphasis in particular, with its six values from the various measuring fields, is always in a very narrow band and thus delimits individual skin areas well. The run percentage shows a similar trend to the short run emphasis. The run length non-uniformity R _{RLB characteristically} shows the greatest values for the replicas of the neck and abdomen in the direction Φ = 0 °. This parameter accordingly shows a behavior which is inversely proportional to both the co-occurrence parameter C _CON and the gray level non-uniformity R _{GLN (} not shown). The run percentage R _RP is similar to R _RLN .

After a characterization for the eight skin areas based on the texture parameters, it is possible to reverse the classification of different skin areas, so from one add a set of texture parameters to any skin area determine and then the skin area of one Assign part of the body. This appears in particular with regard to the classification of clinical pictures of human skin of interest.

Investigation of the effect of cosmetic treatment

The influence of cosmetics on human skin could by different authors based on Roughness parameters can be demonstrated. It was based on a comparative investigation between Profilometry and texture analysis examined whether a Application of cosmetics to the skin using the Texture parameters can be shown. For this purpose, a 25-year-old female subject daily for 3 weeks Replicas from the thigh above the right knee created and both profilometric as well evaluated by texture analysis. During the first seven There was no cosmetic treatment for days; the the following 15 days the skin was treated daily with the commercial W / O cream pH5-Eucerin® (manufacturer: Beiersdorf AG, Hamburg).

For laser profilometric measurement, cf. also Saur (Saur, R .; Schramm, U .; Steinhoff, R .; Wolff, HH: structural analysis of the skin surface by computer-aided laser profilometry; dermatologist 42; 499-506; 1991) was an area of 5.12 × 5.12 mm² measured a resolution of 20 µm in 256 parallel sections and from this the arithmetic mean roughness R _a according to DIN 4762: surface roughness, terms, surface and their parameters; Beuth Verlag, Berlin; 1/1989. Fig. 6 shows the R _a values determined over the treatment period, whereby a decrease in the mean roughness value can be seen. Towards the end of the treatment, there was a slight decrease in the skin-smoothing effect of the preparation, which was expressed in a slight increase in the R _a values, although the arithmetic mean roughness still determined significantly below that before the application of pH5-Eucerin® Values.

Comparing to Fig. 6, the following can be determined for the texture parameters: Some texture parameters have a behavior similar to the course of the arithmetic mean roughness value, so they are obviously sensitive to the detection of a cosmetic treatment. The application of a cosmetic preparation does not affect the curve shape of other texture parameters. The following applies in particular:

A treatment effect was clearly visible on the basis of the parameter

• - correlation C _COR ,
• - Contrast C _CON ,
• - Run Length Nonuniformity R _RLN ;

these parameters are plotted in Fig. 7. While the correlation shows a decrease corresponding to the R _a value due to application of the cosmetic preparation and a slightly increasing tendency towards the end of the treatment, the contrast is inversely proportional to the arithmetic mean roughness; the latter course also shows the run length non-uniformity.

The treatment effect was weak but not significantly recognizable on the basis of the parameters second angular moment C _ASM , entropy C _ENT , run percentage R _RP , short run emphasis R _SRE , long run emphasis R _LRE and gray level nonuniformity R _GLN .

No pronounced tendency in changing the The parameters of cosmetic treatment showed statistical parameters.

It should be noted that in all of the parameters of co-occurrence matrix and run-length matrix given here, a more or less pronounced change due to cosmetic treatment compared to the untreated skin can be seen, while the parameters of the gray value distribution are unsuitable. The correlation is proportional to the arithmetic mean roughness, while the values of contrast and run length non-uniformity have the behavior that is inversely proportional to R _a . The change in the texture parameters due to a treatment has a numerical value that is somewhat greater than the height of the band in which the six parameters obtained on different measuring fields of a skin area lie; however, the change is significantly smaller than the differences between individual skin areas. A treatment effect can therefore be characterized by the simple image analysis method described.

With the method according to the invention a quantitative skin examination new parameter set presented, which enables a right differentiated characterization of human Skin surface. Characteristics of the Co-Occurrence Matrix and the Run-Length Matrix as well also statistical parameters of the gray value distribution to the predominantly used so far Roughness parameters are a sensible alternative. On the one hand, the method presented has one significantly less time required for a measurement with subsequent evaluation as a determination of Roughness parameters, on the other hand, can be determined by the Variety of easy-to-determine texture parameters a stronger distinction between individuals Skin characteristics occur. The description of eight different skin areas with the help of Texture parameters is a first step in the direction a comprehensive mathematical description of the Skin surface of the human body.

The possibilities are relatively easy Changes in the skin surface due to influences such as cosmetic treatment by means of image analysis To characterize texture studies are in of this invention. Based on these Results were able to give some texture parameters that have a treatment effect clearly and parameters in which only slight trends were recognizable. Certainly can by Texture analysis but more or less pronounced, possibly also pathological changes in the Skin surface can be detected.  

This is particularly important with regard to Quantitatively simplified in the future Examination procedure of the human skin surface.

A particular advantage of the method according to the invention is that it is directly on the living skin can be applied because the measurements are fast can be done. So it is not necessary in an elaborate intermediate step, replicate the To produce skin surfaces.

Claims (5)

1. A method for the quantitative measurement of the texture of the human skin surface by registration, reproduction and analysis of image information, characterized in that gray-scale images of the skin or an imprint of the skin surface (replica) are produced with targeted illumination of the skin or replica at a defined illumination angle and mathematical parameters via the gray value distribution via digital image processing and that a) additional images of differently contrasted flat structures are produced and parameters via the gray value transitions are determined and / or b) the run lengths of a gray value are analyzed with regard to their standard distribution and their distribution functions as well as significant deviations. 2. The method according to claim 1, characterized characterized that you have characteristic values or parameters the co-occurrence matrix and / or the Run-length matrix used. 3. The method according to claim 1, characterized in that the correlation C _COR , the contrast C _CON and / or the run length non-uniformity R _{RLN is} used as parameters or parameters. 4. The method according to claim 1 for metrological Determination of the texture before and / or after treatment the human skin surface with cosmetic or pharmaceutical agents.   5. Device for performing the method according to Claim 1, characterized in that it consists of a cold light source for illuminating the sample (Skin, replica), a microscope and one CCD camera for recording the gray scale images and an image processing system and one Evaluation computer with the described Computer program for the gray scale images exists.