WO2007135531A2

WO2007135531A2 - Image processing system for the analysis of osteoclastic activity

Info

Publication number: WO2007135531A2
Application number: PCT/IB2007/001297
Authority: WO
Inventors: Stefano Zeno Maria Brianza; Marco Cerrato; Patrizia D'amelio; Giovanni Carlo Isaia
Original assignee: Università Degli Studi Di Torino
Priority date: 2006-05-19
Filing date: 2007-05-15
Publication date: 2007-11-29
Also published as: ITTO20060366A1; WO2007135531A3

Abstract

A system is described for the analysis of osteoclast activity that includes a module (20) for detecting (30) at least one image (P) or a support for culturing osteoclasts (10) , a digitising module (40) configured for producing from said detected image (P) a digital image (Pd, Pg) , a processing module (50) configured for identifying and quantifying areas of eroded matrix (A) in said digital image (Pd, Pg) , . According to the invention said processing module (50) comprises at the least one display module (55) configured for displaying said digitised image (Pg) and a pointing device (60) for selecting (350) one or more points (wm) distinctive of an eroded area (A) in said digitised image (Pg) , said processing module (50) also being configured for calculating (355) one or more threshold values (MREF; S (1) , S (2) , S (3) ) as a function of the brightness of one or more selected points (wm) distinctive of an eroded area (A) , and to re-code each point (x, y) of the digitised image (Pd, Pg) at two levels in a coded image (Pe) , attributing a first level (450) or a second level (440) to the corresponding point of the coded image (Pe) as a function of the result of a comparison operation (435) between a function of the brightness (V(x,y) ) of said point (x, y) of the digitised image (Pd) and said one or more threshold values (MREF; S (1) , S (2) , S (3) ) , identifying as areas of eroded matrix (A) sets of adjacent points of the coded image (Pe) presenting said first level (450) , quantifying geometric parameters of said areas of eroded matrix (A) as a function of said set of adjacent points of the coded image (Pe) presenting said first level (450) .

Description

"Image processing system for the analysis of osteoclastic activity"

TEXT OF THE DESCRIPTION The present invention relates to image processing techniques whose purpose is to study osteoclast activity in vitro, in particular to techniques that entail quantifying the resorption of a culture support for osteoclasts, that is a bone matrix or a similar culture substrate, by analysing an image of that support.

Physiopathological evaluation of osteoclast activity in various diseases involving the bone, such as osteoporosis, Paget' s disease or neoplastic conditions, or in estimating osteoclast response to different treatments, is usually performed on the basis of image analysis.

Bone resorption occurs due to the activity of osteoclasts, multi-nuclear cells formed from the fusion of bone-marrow precursors. An imbalance between bone deposition and its resorption, with an increased number of osteoclasts or their increased activity, is thought to underlie many metabolic diseases such as osteoporosis and Paget' s disease, already mentioned, but also to underlie neoplastic diseases involving the bone, such as myeloma and solid tumours. Osteoclasts are also indicated as potential killers of cancer cells in a bone microenvironment provided they are appropriately transfected with a suicide enzyme.

The importance of quantifying the formation of osteoclasts, their activity and their response to pharmaceutical agents thus appears evident in order to understand the physiopathological processes involved in bone metabolism. In particular, the reference technique used today to define osteoclast functionality is the erosive capability of osteoclasts.

In general the erosive capability of osteoclasts and the efficacy of in vitro treatments may be estimated by quantifying biochemical markers released into the corresponding culture medium, or by visually quantifying the portion of matrix eroded by the cells, that is by identifying the eroded areas in a bone matrix on microscopic images and quantifying the geometric parameters of these areas. Slices of cortical bone, slices of dentin and films of hydroxyapatite are currently the most widely-used culture techniques to analyse the erosive activity of osteoclasts.

Visual quantification techniques aim to evaluate and quantify the degree of resorption of the mineralised matrix by the cultured osteoclasts. Quantification of resorption activity on slices of bone or dentin is based both on two- dimensional analysis of resorption lacunae of variable depth, and on quantification of the volume resorbed, through stereomicroscopic techniques, such as for example that reported in Adamopoulos IE, Sabokbar A., Wordsworth B. P., Carr A., Ferguson D.J., Athanasou N.A. "Synovial fluid macrophages are capable of osteoclast formation and resorption" J Pathol, 2006, 208 (1) : 35-43. The degree of resorption may be detected, with variable accuracy, through light transmission techniques. Two-dimensional analyses are more accurate on hydroxyapatite film, thanks to the reduced and controlled thickness. Thus, it is known to be possible to quantify erosive capability on the basis of two-dimensional analysis of resorption lacunae of variable depth, based on optical methods. An example of such a technique may be found in US patent US 5, 849, 569.

In particular, culture supports may be examined employing an inverted light microscope and then, on the acquired images, the total area of resorption may be estimated visually. Matrix resorption may be detected for example by a modification of light transmission linked to changes in the thickness. The above-mentioned stereomicroscopic analysis of resorbed volumes is performed on slices of dentin or bone that show three-dimensional resorption zones of varying depth. The use of hydroxyapatite film of thickness specified during production simplifies this technique: since the film is thin and of uniform thickness, the quantity of material removed may be correlated with the area of the lacuna created.

In order to analyse osteoclast resorption lacunae, a technique is known whereby the eroded areas are drawn manually using a grid superimposed over the reflected light microscopic image, as for example that described by Massey H. M., Flanagan A.M., "Human osteoclasts derive from CD14- positive monocytes" Br J Haematol, 1999, 106 (1) : 167-70.). However, this system, which relies on manual drawing, inevitably leads to results that are inaccurate and imprecise . The present invention aims to produce a solution for the analysis of osteoclast activity that provides more precise and accurate evaluations, and gives the operator the possibility to continuously evaluate the quality of the result obtained during the analysis process. According to the present invention, this purpose is achieved thanks to an image processing system and a corresponding image processing procedure and computer program product directly loadable into the memory of a digital computer, having the characteristics described specifically in the attached claims.

The invention will now be described with reference to the attached drawings, supplied as non-limiting examples, in which:

- figure 1 shows a flowchart relating to a first part of a procedure implemented by the system according to the invention;

- figure 2 shows a flowchart of a second part of the procedure implemented by the system according to the invention; - figure 3 shows a flowchart relating to a third part of the procedure implemented by the system according to the invention;

- figure 4 shows a flowchart of a fourth portion of the procedure implemented by the system according to the invention;

- figure 5 shows a flowchart relating to a fifth part of the procedure implemented by the system according to the invention; figure 6 shows a flowchart relating to a sixth part of the procedure implemented by the system according to the invention;

- figure 7 shows a schematic diagram of the system according to the invention;

- figures 8, 9, 10 and 11 show diagramatically the images processed by the system according to the invention at different operative steps.

The system proposed is substantially a semiautomatic image analysis system for the quantitative study of osteoclast resorption activity of a bone matrix or, in general, of a similar culture substrate. This system is capable of determining the percentage of the matrix examined that has been resorbed by cultured osteoclasts, counting the areas identified as eroded and determining their dimensions, or sizes, and providing as output the results in numerical terms. According to a further aspect of the invention, colour coding procedures are provided for as an aid to visual interpretation of the image by an operator. The procedure implemented by the system according to the invention is useful to simulate human visual capability and, by detecting grey levels, this system is capable of comparing the grey level of each pixel of the acquired image with those surrounding it. This semiautomatic system also implements procedures capable of enabling the operator to control the quality of the evaluation process in real time. Figure 7 shows a block diagram of an image processing system for analysing osteoclast activity according to the invention. This system includes, indicated with reference number 10, a culture support. This culture support may be a slice of bone tissue, a slice of dentin, or a BD BioCoat™ Osteologic™ Bone Cell Culture System well, or otherwise a different thin culture support. This culture support 10 serves for the culture of osteoclasts or their precursors.

The support 10 is examined through an optical inverted light microscope 20, illuminated by a lamp 30, which detects an image P of that support. In order to obtain images P that are legible, the support 10 containing the specimens must be illuminated in an appropriate manner. Regulation of the microscope according to the Kohler technique is recommended to acquire good-quality images. The optical microscope 20 is in its turn connected for image acquisition to a digital camera, indicated with reference number 40, that provides a digitised image Pd as output. The format of the image used is preferably the common 1536 x 1048 format, with 24 bits per pixel (16,000,000 colours). The digitised image Pd produced by the digital camera 40 is in JPEG format.

In the presence of an appropriate connection, the images may be saved directly in a computer 50, as shown in figure 7. Otherwise the images may be saved in the internal memory of the camera 45 for subsequent transfer to a computer 50. Thus the digital camera 40 provides digitised images Pd to a computer 50. The computer 50 comprises a monitor 55 to display the images and a mouse 60 as pointing device to enable an operator to interact with the images displayed on the monitor 55. This computer 50, in a preferred embodiment, is a Pentium IV personal computer, that employs a 2.66 GHz CPU and has a memory of 512 MegaBytes of RAM.

According to the preferred embodiment shown in figure 1, an acquisition card is not required in the computer 50, but known transfer protocols are employed, for example through USB connection, to transfer the digitised images Pd from the digital camera 40 to the computer 50.

An image processing procedure to analyse osteoclast activity implemented by means of the computer 50 will now be described.

Figure 1 shows, by means of a flowchart, a first calibration procedure 200 that is part of the procedure according to the invention. This calibration procedure 200 entails that, for each session of digitised images Pd acquired by means of the digital camera 40, the operator is requested to perform brightness calibration and, if desired, also image-size calibration.

In order to speed up the procedure, each loaded image Pd is reduced to 1024 x 768 pixels. In particular, with reference to the chart in figure 1, at the beginning of the procedure 200 which is at the beginning of the analysis session, a brightness calibration step 210 is provided for. For this brightness calibration step 210 it is preferable to use an empty support 10 connected to the microscope, in particular an empty well of the same thickness as the wells covered with matrix. By acting on the potentiometer of the microscope 20 to adjust the brightness of the lamp 13, the reading precision is increased. The user is also provided with a function to check the compatibility of the brightness set with a value judged to be optimal.

The brightness calibration takes place by detecting again through the digital camera 40 pixel by pixel the distribution of light on the specimen and saving the values detected in a data file in the computer 50. Spatial calibration is usually achieved by using the photograph of a calibrated slide.

Thus, at the brightness calibration step 210, the distribution of brightness throughout the image is acquired and the values obtained are saved in a natural correction matrix N, likewise relative to 1024 x 768 pixels, on the hard disk of the computer 50 in a step 215, and then immediately loaded into the RAM memory in a step 218. In the same step, an artificial brightness distribution Art is loaded into the RAM memory, likewise 1024 x 768 pixels, for the purpose of correcting any microscope configuration errors . The values of the natural correction matrix N preferably already at this stage are raised to the fifth power. The artificial distribution Art is determined empirically and inserted in advance in the data available to the procedure. In a selection step 220, the operator is asked whether or not to perform a dimensional calibration operation. If yes, a step 225 of conversion from pixels to units of measurement is performed and . the operator then moves on to an image analysis procedure 300. If no, the image analysis procedure 300 directly follows the selection step 220. The correction values obtained through the brightness calibration 210 and stored in the RAM memory of the computer 50 are used throughout the entire processing procedure of a single photographic session, helping to speed up execution.

In figure 2, reference number 300 indicates an image analysis procedure that, in a step 310, provides for image loading 310, in which the JPEG digitised image Pd is loaded. This JPEG digitised image Pd is converted to a grey scale and saved as a copy image Pg in BMP format on the hard disk of the computer 50. This takes place in an image copying step indicated with reference number 315. During this image copying step 315 a format conversion operation is also provided for, in which a single colour in the digitised image Pd is made to correspond to a grey level in the copy image Pg, again on 24 bits, but with identical values for each of the 8 bits that make it up. Note that the entire subsequent processing procedure operates on this grey levels copy image Pg obtained through the image copying step 315, leaving the original digitised image Pd acquired by the camera 40 unchanged. This copy image Pg is automatically loaded onto the computer 50 and Gamma, brightness and contrast corrections are performed on it. Note that, throughout the procedure according to the invention, the operator is guided by means of univocal messages consisting of a string of text, and at each step the operator is enabled to perform exclusively the operations appropriate to the phase of the analysis 300, coding 400 and processing 500 procedures; all other actions are disabled.

Thus, subsequent to the image copying step 315 the first step requested of the operator is that of selecting eroded areas to operate a specific correction for the image under analysis. This step is indicated with reference number 320 and requires the operator to select, on the screen 55 using the mouse 60, three areas that the operator recognizes as eroded, in three different portions of the copy image Pg displayed on the screen 55, respectively in a central, an intermediate and a peripheral area. The selection operation performed by the operator at the request to select areas 320 leads to the determination, for each selection made, through mouse, keyboard, or other pointing device, of a point corresponding to a pixel. Figure 8 shows diagrammatically the copy image Pg on the screen 55 and three selected points wl, w2, w3 that indicate eroded areas.

For each selected point wl, w2, or w3, in a respective mean calculation step 321, 322, 323, a mean intensity value is determined, that is the brightness or grey level, calculated on a predetermined neighborhood IM represented by a grid of 7 x 7 pixels surrounding each selected point. Thus, from steps 321, 333, 323, three averaged values are obtained, respectively Ml, M2 and M3.

In particular, the analysis procedure 300 comprises an alteration step 335, that loads the indexes n, k initialised at step 315 to calculate correction values Vcorr with which to alter in a non-linear manner the result of steps 321, 322, 323 that calculate the averaged values Ml, M2, M3. In other words, the alteration step 335 corrects the averaged values Ml, M2, M3 by applying to the neighborhoods IM surrounding the selected points wl, w2, w3 the multiplication of the natural correction matrix N by the artificial distribution Art, element by element, respectively raised to the indices n, k. Note that, in general, the elements of the natural correction matrix N and of the artificial distribution Art are the inverse of the light intensity values at points of the area examined under the microscope during the brightness calibration process. Thus an operation of raising them to the power n or k has substantially the effect of flattening the surface determined by the natural correction matrix N and the artificial distribution Art and, therefore, of approaching the averaged values Ml, M2, M3.

Whenever, in an evaluation step 325, it is calculated that the deviation among the three averaged values Ml, M2 and M3 is above 5%, the index n is increased by 1 in a step 330 until its maximum permitted value is reached, verified in a step 326, and provided to the alteration step .335 to again alter the averaged values Ml, M2, M3. If the evaluation step 325 is again not satisfied, at a step 327 the index k is increased by 1 while the index n is returned to its initial value. Raising them to powers of the index n, k causes the averaged values Ml, M2, M3, as was said, to converge. Whenever, in an evaluation step 325, it is calculated that the deviation among the averaged values Ml, M2, M3 is less than 5%, the optimal correction value evaluated is extracted in a step 340, then in a correction step 345 the optimal correction value Vcorr (x, y) obtained at step 335 is applied to the entire copy image, pixel by pixel, determining a corrected image Pr. In other words, both in the definition of the three averaged values Ml, M2, M3 and in the subsequent correction of the entire image, the brightness of each point or element considered of the neighborhood IM or of the entire image matrix corresponding to the image Pg is corrected by multiplying its value by the product of the elements of the matrices N and Art at that point, each of which matrices being elevated to an appropriate exponent determined at step 325.

In a second step 350 requesting area selection, then, the operator is again invited to select any area that he or she recognizes as eroded, indicating a reference point wm with the mouse 60. Through this request step 350 a mean reference value MREF is defined, which is updated to the correction, on a predetermined IREF neighborhood of 7 x 7 pixels surrounding the selected reference point wm. This mean reference value MREF is calculated in a specific step 355 in which a phase variable F is also initialised at one. This phase variable F is a counter of the iterations of an image coding procedure 400 that follows the calculation step 355.

This image coding procedure 400 substantially corresponds to recoding the corrected digitised image Pr into a coded image Pe only represented through two levels, that is the colours black and white. This image coding procedure 400 is shown in detail in figure 3, and provides for an evaluation step 410 that checks whether the phase variable F is less than or equal to a specified iteration value, in the embodiment illustrated here equal to 3. In the negative case, that is if there are no further coding iterations to be performed, the control passes to an artefact exclusion procedure 500. In the positive case, on the contrary, the phase variable F is increased by one in an increment step 415 and then in a mean value calculation step 420, for each pixel of the image, the mean brightness value is calculated on a neighborhood of dimensions equal to a mask MSK(F), that is a mask corresponding to the phase F in execution.

The mean value calculation step 420 thus entails calculating, for each pixel (x, y) where x and y indicate the spatial co-ordinates of the pixel, a mean brightness value M(x, y) of the mask MSK around the pixel (x, y) :

+MSK, +JVfSK

∑V(x,y) M_MSK_s, V_κ-- ₊ MSK _ιf , 1 -, , ,

Δs was said, the mask MSK has values that depend on the phase F in execution, that is it is an MSK(F) mask. The MSK(F) masks relating to the different phases F are loaded from the memory in a specific loading step 425. In that loading step 425, by way of example, we have MSK(I) =3, MSK(2)=4, MSK(3)=5, thus corresponding to mask areas of 49, 81 and 121 pixels. With V(x, y) is indicated the value of the pixel, that is its brightness or grey level.

The mean brightness value M(x, y) of the mask MSK M(x, y) is compared with a threshold value S(F) function of the phase F.

This is represented in figure 3 through step 435 in which the mean brightness value M(x, y) of the mask MSK(F) calculated at step 420 is loaded, and by the steps 431, 432, 433 of loading threshold values in function of the phase F in execution. In step 431, the mean reference value MREF for the first phase F is loaded in a threshold variable S(I). In steps 432 and 433, the threshold variables S (2) and S (3) are calculated on the basis of the discrimination operated in the preceding phase F . If the mean brightness value of the mask M(x, y) compared in a discrimination step 435 with the threshold value S(F), is found to be greater than or equal to the reference mean S(F) the colour white is attributed to the pixel (x, y) in a step 450. Whenever the mean brightness value of the mask M(x, y) is below the reference threshold S(F), on the other contrary, in a step 440 the pixel (x, y) is overwritten with a black pixel. Note that the step of overwriting with a white pixel 450 is subordinated to the execution of a checking block 445 in which it is evaluated whether the phase F has reached its maximum value of 3.

The results of each comparison performed at the discrimination step 435 are written in a virtual matrix updated on the video in real time progressively line by line. When the phase F is changed, the processed pixel copy matrix becomes the matrix object of the new analysis. The converted greys scale image is always present on the background of each phase.

The discrimination process may be reiterated at will according to the judgement of the operator and the passage to a subsequent phase F is conditional on operator approval. The two phases F subsequent to the first, with the loading steps 432 and 433 of the thresholds S (2) and S (3), have as their goal that of refining the discrimination operation performed by the operator. Indeed, there must be congruence between the margins of the eroded areas and those of the areas detected through the proposed procedure, just as matrix artefacts and residues must be adequately attributed.

When the operator is not satisfied with the result, he or she has the possibility of acting on the comparison threshold S(F) increasing or decreasing its value in steps 431, 432 and 433. In particular, in the user interface shown on the screen 55 it is possible to prearrange a command in the form of a slider cursor so as to permit the operator to vary, at steps 431, 432 or 433, with continuity the mean reference value MREF, that thus becomes a variable threshold S(I), and those of the thresholds S (2) and S (3) in the corresponding phases F, so as to display on the screen 55 in rapid succession the effects of discrimination at different thresholds, but with the same dimension of the mask MSK(F) corresponding to the phase F in execution. Naturally, for this purpose, it is possible to configure the procedure to repeat each phase F at will and at the desired scanning speed, compatible with the characteristics of the computer 50 used. That is, through execution of a series of discrimination steps 435 in a series of phases F the operator may instantly check on the video the quality of the discrimination operated. In other words, he or she may detect whether by changing the brightness threshold certain characteristics of interest (the eroded areas) remain or disappear as artefacts.

Although a corresponding control block is not shown in figure 4, continuation to the subsequent procedure must be also authorised by the operator. Thus the coding procedure 400 is substantially comprised of three analogous phases F whose goal is to reproduce with the highest possible accuracy, in an image coded in white, corresponding to the eroded area, and in black, corresponding to the non-eroded area, those portions of the support 10, that is of the slide, that the operator recognizes as eroded and as unaltered.

The operator is then requested to make an evaluation of this discrimination, through the area selection steps 320, 350 and the virtual matrix is updated on the video, while the computer 50 performs the calculation operations. The function that implements the comparison for the discrimination is identical for all three phases F. These phases differ, as illustrated with reference to steps 420 and 425, for the dimensions of the comparison mask MSK and for the reference value, that is the threshold value. The image is run from top left to bottom right, proceeding line by line. Scanning requires a number of pixels to be neglected, surrounding the image, equal to half minus one of the pixels of the largest mask. For each pixel, the mean calculated on a neighborhood of pixels that increases with the phase F is determined: F=I: 7x7 F=2 : 9x9 F=3: 11x11. In the last phase F, that is with F=3, the response at the verification step 445 is positive. In the virtual matrix that is overwritten, the white pixels are replaced by pixels of the grey scale image. In this manner the operator can check the congruence of the analysis with the original image.

Before continuing with counting the areas and the proper quantification operation, the operator is given the opportunity, in an artefact exclusion procedure 500, to eliminate portions of image that are clearly altered by artefacts, such as artefacts due to accidental strikes with the tip of the instrument used for seeding, macroscopic matrix alterations, or agglomerated residues.

Figure 4 thus shows an artefact exclusion procedure 505 that is performed through a step 510 at which the operator is asked whether he or she wants to exclude artefacts. If yes, the next step is 515 which is a manual tracing step on the screen 55 of the computer 50 of the areas to be excluded by the operator using the mouse 60. Subsequently, the control is passed to a step 520 in which the eroded areas are counted, a subsequent step 530 in which the dimensions of the areas are evaluated, and a further step 540 in which the percentage of eroded area is calculated. Each of these steps 520, 530, 540 provides its results as output both to a disc output procedure 525 and to a screen output procedure 535.

As shown in figure 5, the screen output procedure 535 comprises a step 605 in which the percentage of eroded area is displayed, a first colour coding step of the dimensions of the areas 610, a second colour coding step of the percentage of eroded area 615, as well as a step 620 in which a dimension-frequency histogram is created. Thus the results obtained are expressed on the video as percentages and through two types of colour coding. The first coding, relating to step 610, associates a colour of brightness proportional to the size of the area. Figure 11 shows a representation of the principle of a display on the screen 55 of an image Pel subjected to the first coding 610. The dimensions of a single area A offer information on the activity of each individual cell. The second coding, expressed in step 615, associates a shade of a colour, in particular green colour, having brightness proportional to the total percentage of eroded area. The resorption percentage may be considered an image of the total aggressiveness of the cultured cells. An example of the utility of the second coding performed at step 615 is the evaluation of osteoclast activity in patients with osteoporosis before and after treatment with a specific drug.

In the screen output procedure 535, in a step 625 it is possible to save the graphic results on disk 525. In any case the procedure terminates with a saving on disc 525 detailed in figure 6. This saving operation comprises a step 635 to create a text document that contains all historical information of each individual analysis. Furthermore, at a step 640 a continually-updated spreadsheet is automatically created containing the ordered set of all data of all analysis sessions. This automatism is configured such as to make the data immediately available for statistical analysis. The results reported in the text document at step 635 are also displayed on the video as part of the step 620 to create the dimension-frequency histogram.

Thus from the above description it is clear that the proposed system and image processing procedure for the analysis of osteoclast activity configure a quick, accurate, economic and precise tool to enable research to be conducted avoiding laborious and imprecise iterations, maintaining full control over the results obtained. In particular, to advantage, the use of colour coding enables the operator to quickly evaluate the results obtained. The system and procedure proposed are capable of determining the percentage of the matrix examined that has been resorbed by the cultured osteoclasts, counting the eroded areas and measuring their dimensions, and providing the results in numerical terms and through two types of colour coding useful for visual interpretation.

Advantageously, implementation of a semiautomatic procedure that is useful to simulate human visual capabilities and the use of grey level measurement enable the grey level of each pixel to be compared with those surrounding it, subsequently implementing simple calculations that enable the operator to control the process in real time.

Advantageously, the possibility of storing the data in the mass storage memory, in particular in table form or as image data, enables the analysis to be deferred indefinitely over time, even if the support wells are subsequently damaged or lost, or if the matrix undergoes degeneration.

Furthermore, the proposed system enables the analysis to be continually reiterated until satisfactory results are obtained.

Furthermore, the proposed system enables the colour coded images also to be saved, thus making it possible to analyse and compare them subsequently.

Furthermore, the proposed system enables inter-operator and intra-operator investigations to be made: access to the computer for analysis procedures is restricted to a user database, and data are saved under the name of the operator who accesses the program.

Of course, without prejudice to the principles of the invention, details of production and embodiments may be widely varied with regard to what is described and illustrated, without thereby departing from the scope of the invention.

Claims

1. System for analysing osteoclast activity comprising a module (20) for detecting (30) at least one image (P) or a support for culturing osteoclasts (10), a digitising module (40) configured for producing from said detected image (P) a digital image (Pd, Pg) , a processing module (50) configured for identifying and quantifying areas of eroded matrix (A) in said digital image (Pd, Pg) , characterised in that said processing module (50) comprises at the least one display module (55) configured for displaying said digitised image (Pg) and a pointing device (60) for selecting (350) one or more points (wm) distinctive of an eroded area (A) in said digitised image (Pg), said processing module (50) also being configured for calculating (355) one or more threshold values (MREF; S(I), S (2), S (3)) as a function of the brightness of one or more selected points (wm) distinctive of an eroded area (A) , and to re-code each point (x, y) of the digitised image (Pd, Pg) at two levels in a coded image (Pe) , attributing a first level (450) or a second level (440) to the corresponding point of the coded image (Pe) as a function of the result of a comparison operation (435) between a function of the brightness (V(x,y)) of said point (x, y) of the digitised image (Pd) and said one or more threshold values (MREF; S(I), S(2), S(3)), identifying as areas of eroded matrix (A) sets of adjacent points of the coded image (Pe) presenting said first level (450) , quantifying geometric parameters of said areas of eroded matrix (A) as a function of said set of adjacent points of the coded image (Pe) presenting said first level (450) .

2. System according to claim 1, characterised in that said processing module (50) is configured for calculating (355) as a function of the brightness or said one or more points (wm) distinctive of an eroded area (A) a mean brightness value (MREF) associated to a neighborhood (IREF, MSK(F)) of said one or more selected points (wm) distinctive of an eroded area (A) .

3. System according to claim 2, characterised in that said processing module (50) is configured for calculating (420) at each point (x, y) of the digitised image (Pg) a mean brightness value (M(x, y) ) on a first neighborhood (MSK(I)) of said point (x, y) and compare (435) said mean brightness value (M(x, y) ) with said mean value (MREF) associated to a neighborhood (IREF) of said one or more selected points (wm) distinctive of an eroded area (A) .

4. System according to claim 3, characterised in that said processing module (50) is configured for repeating for one or more successive iterations (F) and one or more further neighborhood (MSK(F)) said operation (420) of calculating a mean brightness value (M (x, y) ) for each point (x, y) of the digitised image (Pg) .

5. System according to claim 4, characterised in that said processing module (50) is configured for employing in said one or more successive iterations (F) one or more neighborhood s (MSK(F)) of dimension greater than or equal to said first neighborhood (IREF) .

6. System according to claim 4 or claim 5, characterised in that it includes the operation, in one and the same iteration (F) and with one and the same neighborhood (MSK(F)), of varying a threshold value S(F) and displaying the consequent coded image (Pe) .

7. System according to one or more of the above claims, characterised in that it is configured for displaying (610) on said display module (55) said coded image (Pe) associating to the points of a same area identified as being eroded (A) a colour with brightness proportional to the dimensions of said eroded area.

8. System according to one or more of the above claims, characterised in that it is configured for displaying (615) said areas identified as eroded (A) with dots of a same colour and a same brightness proportional to a calculated percentage of eroded area of the digitised image (Pd) .

9. System according to one or more of the above claims, characterised in that said processing module (50) is configured such that by means of said pointing device (60) a number of points (wl, w2, w3) distinctive of equivalent eroded areas can be selected in a preliminary manner (320) and such that corresponding values (Ml, M2, M3) associated to neighborhood s (IM) of said points (wl,w2, w3) selected in a preliminary manner can be evaluated to control a correction operation (345) of the digitised image (Pd) .

10. System according to one or more of the above claims, characterised in that said processing module (50) is configured for performing said correction operation (345) of the digitised image (Pd) applying an optimal correction distribution (340) to the points of the digitised image (Pg) to obtain a corrected image (Pr) .

11. System according to claim 10, characterised in that it includes calculating said optimal correction distribution (Nⁿ*Art^k, element by element) through an iterative comparison with a preset value (525) of a deviation calculated among the values (Ml, M2, M3) associated to neighborhood s (IM) of said points (wl, w2, w3) selected in a preliminary manner and corrected by applying iteratively a distribution that is the multiplication of a brightness distribution (N) acquired in a calibration step (210) by a preset brightness distribution (Art) raised (335) to respective indices n and k, said indices n and k being iteratively increased.

12. System according to one or more of the above claims, characterised in that said pointing device (60) is configured to perform manual tracing of areas to be excluded within an artefact exclusion procedure (500) implemented in said processing module (50) .

13. System according to one or more of the above claims, characterised in that said module (20) for detecting (30) at least one image (P) of a support functioning as a bone matrix (10) includes a microscope.

14. System according to one or more of the above claims, characterised in that said digitising module (40) comprises a digital camera.

15. System according to claim 13, characterised in that said microscope (20) is an optical inverted light microscope illuminated according to a Kohler configuration.

16. System according to one or more of the above claims, characterised in that said first level (450) and said second level (440) for coding into two levels are selected from among two colours .

17. System according to claim 1, characterised in that said processing module (50) is configured for regulating through the action of pointing devices (60) said one or more threshold values (S(I), S (2), S (3)) and display the consequent coded image (Pe) in real time.

18. System according to claim 1, characterised in that said processing module (50) is configured for progressively updating the coded image (Pe) maintaining the digitised image as a background.

19. System according to one or more of the above claims, characterised in that it includes a mass storage memory module to store (540) in the form of ordered tables (640) the data corresponding to the processed images and/or a text document (635) containing historical information on each analysis .

20. System according to one or more of the above claims, characterised in that said processing module (50) is configured for displaying (535) a percentage of the eroded area (605) and/or a dimension-frequency histogram (620).

21. Procedure for analysing osteoclast activity that comprises the operations performed by the system according to one or more of the claims from 1 to 20.

22. Computer program product directly loadable into the memory of a digital computer comprising software code portions for performing the steps of the procedure according to claim 21 when said program product is run on a digital computer .