WO2000004499A1

WO2000004499A1 - Determining the shape of an internal structure within a physical structure

Info

Publication number: WO2000004499A1
Application number: PCT/NL1998/000414
Authority: WO
Inventors: Cornelis Leonardus Maria De Bakker; Lambertus Spaanenburg; Johannes Albertus Gerhardus Nijhuis; Johan Hendrik Stevens
Original assignee: The Engineering Company Qtecq B.V.
Priority date: 1998-07-17
Filing date: 1998-07-17
Publication date: 2000-01-27
Also published as: AU8466298A

Abstract

For measuring the shape of an internal structure within a physical structure, non penetrating measurements of an exterior surface shape of the physical structure are made in a region where the exterior surface shape is related to the shape of the internal structure underneath. A first set of data describing the exterior surface shape in that region is obtained thereby. Then, penetrating measurements of the shape of the internal structure are made and a second set of data relating to the shape of the internal structure is received. A third set of data describing the shape of the internal structure is obtained by combining at least selected portions of the first and second sets of data. The non-penetrating measurements form a simple and effective tool for enhancing the results of the penetrating measurements. A system for carrying out the proposed method is described as well.

Description

TITLE: Determining the shape of an internal structure within a physical structure

TECHNICAL FIELD

The invention relates to methods involving measurement of the shape of an internal structure within a physical structure and to systems for carrying out such methods.

BACKGROUND ART

In many applications it is desired to obtain information regarding the shape of an internal structure within a physical structure.

An important category of such applications are medical applications in which information about the shape of a internal structure within the body of a patient is desired. It is for example desirable to obtain information about the shape a bone structure to which an implant is to be fitted, of the shape of a spine of a scoliosis patient for whom a corrective brace is to be tailored, or of the jaws of a patient under orthodontic treatment for whom a line of treatment is to be devised and corrective braces are to be made and fitted accordingly.

In other areas information about the shape of structures in the interior of bodies is desirable as well. Knowledge about the shape of the bone structure of a horse can for instance be used to tailor a saddle to that horse. Information about the volume of a consumable portion within pieces of fruit would allow to determine the number of pieces required to obtain a predetermined composition of ingredients more accurately. Knowledge about geologic formations relatively closely under the earth surface can be useful in many instance such as the selection of building or agricultural sites.

In WO 96/32065 it is proposed to make use of two types of imaging techniques and to combine received sets of data to enhance the information about an internal tissue structure of which information is desired. The modalities from which data are correlated may include mammography and other radiological procedures, ultrasound (including pulsed and CW Doppler) imaging, MRI, MRI spectroscopy (MRIS) , fluoroscopy, angiography, computer tomography (CT) , ultrafast computer tomography (UFCT) , electrocardiography (EKG) , electroencephalography (EEG) positron emission tomography (PET), single positron emission tomography (SPECT) . However, each of these techniques entails its specific drawbacks, in particular if it is desired to make quick observations without expensive equipment and even more so if it is also desirable to make measurements using different techniques in a single measurement set up.

SUMMARY OF THE INVENTION

It is an object of the invention to solve the problem of accurately measuring the shape of an internal structure within a physical structure, in a simple quick manner and with little adverse side-effects.

According to the present invention, this problem is solved by providing a method for measuring the shape of a internal structure within a physical structure, in which non penetrating measurements of an exterior surface shape of the physical structure are taken in at least a region where the exterior surface shape is related to the shape of that internal structure, a first set of data describing the exterior surface shape in at least that region is received, penetrating measurements of the shape of that internal structure are taken, a second set of data relating to the shape of that internal structure is received, and a third set of data describing the shape of that internal structure is determined by combining at least selected portions of the first and second sets of data.

Non-penetrating measurements of portions of the physical structure surface of which the shape is at least related to the shape of internal structures underneath can be carried out very quickly and with relatively cheap systems. Furthermore, such measurements typically have no substantial known adverse side-effect on the organism or other structure from which the measurements are to be taken. More specifically many non-penetrating measurement methods are available of which no such adverse effects on the health of a human patient are known or suspected. In the method according to the invention, disturbances due to the presence of tissue between the internal structure to be measured and the outer surface of the physical structure are compensated by combining (e.g. comparing) the results of the non penetrating measurements with the results of the penetrating measurements. Thus, the number of penetrating measurements can be substantially reduced in comparison with a situation where no other measurements are taken.

According to another aspect of the invention, a method and a system for determining a pattern of loads to be exerted to the exterior of a body is provided, in which a measurement method as proposed above is used and in which at least the configuration of positions or areas where the loads are to be exerted are determined from the third set of data .

The invention further provides a method and a system for preparing a tailored device which in use exerts loads to the exterior of a body in which a measurement method as proposed above is used and in which the configuration of at least portions of the device which are adapted to be in load transferring contact with the exterior surface of the body are determined from the third set of data.

In both latter methods, advantageous use is made of the presence of information about the shape of the outer surface of the physical structure in combination with information about the internal structures enveloped by the outer surface, which information is coupled to the information about the shape of the outer surface of the physical structure. The positions, orientations and shapes of the load transfer surfaces can thus be determined very accurately.

The invention also provides a system for measuring the shape of a internal structure in a physical structure which is specifically adapted for carrying out at least the first proposed method according to the invention in that it includes: first^' measurement means for taking non penetrating measurements, processing means connected to the first measurement means for generating a first set of data describing an exterior surface shape of the physical structure from a non penetrating measurement result, second measurement means for taking penetrating measurements, and processing means connected to the second measurement means for generating a second set of data relating to the shape of the internal structure from a penetrating measurement result, the processing means being programmed for determining a third set of data describing the shape of the internal structure by combining at least selected portions of the first and second sets of data. It is observed that the penetrating measurements need not be invasive measurements.

Particularly advantageous modes of carrying out the invention are set forth in the dependent claims . Further features, advantages and details of the invention are set forth in the description in which reference is made to the drawings .

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a flow chart representation of an example of a method according to the invention,

Fig. 2 is a perspective image of a scoliotic spine obtained by an example of a method according to the invention, Figs. 3-5 are representations of the positions of four marking points, Figs . 6-8 are graphic representations of fuzzy descriptions,

Figs. 9 is a side elevational views of a general representation of a structure of a particular class, Figs. 10 and 11 are side elevational representations of measurement results taken from a structure of the class shown in Fig. 9,

Fig. 12 is an example of an partially obscured structure of which information is acquired in accordance with the present invention,

Fig. 13 is a flow chart of a second example of a method according to the invention,

Fig. 14 is a flow chart of an elaboration of a step in the method shown in Fig. 1, Fig. 15 is a schematic representation of an example of a system according to the invention,

Fig. 16 is a cut-off representation of a system and a central portion of a human being, and

Fig. 17 is a combined representation of an outer surface of a body and of a tissue structure within that body.

MODES FOR CARRYING OUT THE INVENTION

The method depicted in Fig. 1 starts with taking a first set of non-penetrating measurements #1 and a second set of penetrating measurements #2 (steps 1 and 2) . Furthermore, a set of previously collected data is provided, which data are generally applicable to the class of structures to which the structure from which data are to be acquired belongs (step 3) . The sets of data which have been obtained by the measurements are received in a data processor and may for example be normalized by combining corresponding data, such as data obtained by the first and the second set of measurements which relate to the distance between a particular pair of points of the physical structure from which the measurements have been taken. Then the data obtained from the sets of measurements are fuzzified into fuzzy descriptions #A, #B and #C (steps 4-6) . The fuzzy model into which the data are entered can for instance include data that are generally applicable to physical structures of the class to which the structure at issue belongs. The fuzzy model may for example be a fuzzy root-to-frontier automaton used to recognize particular parts of the physical structure at issue from the data obtained by the measurements. The use of fuzzy tree automata in conjunction with syntactic pattern recognition is, as such, known in the art and for instance described by Lee in "Fuzzy Tree Automata and Syntactic Pattern Recognition", IEEE Transactions on Pa t tern Analysis and Machine Intell igence, Vol. PAMI-4, No. 4, 1982. The fuzzy descriptions #A, #B and #C are each determined by the data obtained from the respective measurements and include clearness degrees of various measures represented by the data. These clearness degrees can for instance be based on typical measurement error ranges of the type of measurements by which the data have been obtained or on the clearness of an image from which the data have been collected. For instance, the vagueness of the position of a contour of a bony structure will typically depend on the quality of the image (for instance an X-ray or ultrasonic image) from which the data have been taken.

The fuzzy description #C based on generally applicable knowledge can for instance include relations between different parts of the structure. For instance, for the analysis of Scoliosis, generally applicable information about the (scoliotic) spine preferably includes fuzzy sets describing the following knowledge:

• the number of vertebrae;

• the approximate shape of each vertebra;

• the approximate relations between the dimensions of the vertebrae from Thoracic vertebra 1 to Lumbar vertebra 5; • the approximate relations between the positions and orientations of the vertebrae from Thoracic vertebra 1 to Lumbar vertebra 5;

• the approximate overall shape of the spine from Thoracic vertebra 1 to Lumbar vertebra 5; and

• the approximate characteristics of the shape of scoliotic deformations (almost always S-shaped; if C-shaped the outside of the bend almost always faces to the right; largest deformation usually at vertebra Thoracic 7; kyfosis and lordosis inversely correlated to Scoliosis) . The fuzzy set also includes associated clearness degrees of the generally applicable data.

Examples of fuzzy subsets characterizing generally applicable knowledge regarding geometrical characteristics of a physical structure are well known in the art and for instance summarized by Rosenfeld in "The Fuzzy Geometry of Image Subsets", Pa ttern Recognition Letters, vol. 2 pp. 311- 317, September 1984.

After the fuzzy descriptions have been established, the descriptions are merged into an enhanced fuzzy description 21 (step 7) .

The concept of generating an enhanced fuzzy description by combining two or more fuzzy descriptions that are to a certain extent vague and/or incomplete is illustrated by some examples.

From measurements, two fuzzy descriptions A and B have been obtained regarding the values of two variables. This is represented by Fig. 6. On the basis of earlier measurements taken from physical structures from the same class, a fuzzy relation R characterizing the relation between the values of the two variables has been generated.

This fuzzy relation R is used to improve the accuracy of the description B, since the fuzzy description B', which is formed by the combination of A and R, forms an additional description of value of the same variable. This is represented by Fig. 7. Both fuzzy descriptions B and B¹ apply to the same variable so that these can be combined to a fuzzy description B" using an AND operator. This is represented by Fig. 8. Thus a substantially improved accuracy of the information regarding the variable B can be achieved. Generally applicable information to a class of buildings (see Fig. 9) which are generally formed by a rectangle 8 and an equilateral triangle 9 of approximately the same width on top of the rectangle 8 can be expressed in a fuzzy set which forms a first structure of presumability gradients. Such a set may for instance be of the form:

mu_LooksLikeHouse (a, b, c, d, e) =

min (mu_LooksLikeEquilateralTriangle (a, b, c) , mu_LooksLike Rectangle (b, c,d,e) )

where mu_ are membership functions, a, b, c, d and e are the positions of corners (see Fig. 9) and min is the minimum operator defining the overall membership value as the minimum of the two lower level values. Further details and examples of such fuzzy descriptions are described by: D. Dubois and H. Prade in "Fuzzy Sets and Systems: Theory and applications", Academic Press, New York 1980, and by: T.J. Ross in "Fuzzy Logic with Engineering Applications", Mc- Graw-Hill, 1995.

Height and width ranges of the rectangle 8 and the triangle 9 can for instance be left out, if this does not contribute to narrowing down the vagueness of data information by measurements. This depends on the nature of the insufficiency of the data obtained by measurements that is generally experienced in the particular application.

The measurement data from which an image of the house is to be constructed can for instance be incomplete as is represented by Figs. 10 and 11, for instance because a traffic sign obscures the view of the building or because the person taking the measurements has taken the measurements using an inadequate imaging window (positioned too low or too small) .

By combining the measurement results as represented by Fig. 10 or 11 with the generally applicable fuzzy descriptions about the general configuration of buildings of the type which has been measured, it can for instance be established that presumably, the sloping line 10 in Fig. 10 forms one sloping side of the triangle 9 and the intersection between that line 10 and the vertical midplane 11 of the rectangle 8 will be the position of the top of the roof. On the basis of Fig. 11 it is presumable, that the intersection 14 of continuations of the two sloping lines 12, 13 forms a fuzzy description applying to the position of the top of the roof as well. Another example of obtaining a fuzzy description about information that is completely missing in measurement results is illustrated by Fig. 12. If it is presumable that within the field of measurement a sequence of objects is generally not interrupted, the image of the house hidden behind the tree 20 can be automatically filled in on the basis of a fuzzy expression to that effect in combination with fuzzy descriptions describing the shape and positions of the other houses 15, 16, 17.

It is noted that it is a particular advantage of combining data from various sources including measurements and more generally applicable information on a fuzzy level, that exceptions to the generally applicable rules are not excluded, which allows to define the generally applicable rules quite narrow and specific. This, in turn, makes automatically processable formulation of the relations easier and allows to make a very effective use of the generally applicable information. For instance the fact that only parts of two door posts 18, 19 of the house presumably hidden behind the tree 20 are included in the measurement does have an effect on the presumability of the solution that there is a house behind the tree 20. Preferably, the fuzzy description of the generally applicable rules also provides for automatically lowering the presumability of an identical house being located behind the tree 20, the more the shape of the houses 15-17 and the pitch between the positions of the houses 15-17 varies - i.e. the weaker the relation between the identified houses 15-17 is.

The fuzzy descriptions preferably have a tree structure, which can be evaluated using fuzzy root to frontier automata (FRTFA), which are as such known in the art, to assess the extent to which the enhanced description has been improved.

As is shown in Fig. 1, the enhanced fuzzy description 21 is converted (step 22) into a signal 23 representing the desired information in a cognizable form suitable for the respective application. In the example in which information about the shape of the spine of a scoliotic patient is acquired, the representation may for example be provided in the form of images of a spine using a viewer system to allow viewing the spine from different angles, so that a medic expert can use the representation to make a diagnosis and to devise a therapy for the patient. An example of an image generated by such a viewer is shown in Fig. 5. As can be seen from this image, according to the model, the vertebra 24 of the spine 25 are represented by 8-cornered volumes. The dimensions, the positions and the orientations of the vertebrae 24 are obtained from measurements from lateral views in X and Y direction.

The proposed method is particularly effective for acquiring information about the shape of a spine, because a spine is a physical structure of which a large number of properties are in a semi-robust mutual relation and the spine is a skeletal structure of which the shape is very closely related to the shape of the exterior surface of a patient's back. The properties of which information is to be acquired include for instance the position and orientations of the vertebrae of the spine of which the mutual relations are within vaguely predictable constraints. Another example of such an application is the prediction of the volume of pulp in pieces of fruit. The body penetrating measurements can for instance be employed to take samples of the thickness of the shell and the non- body penetrating measurements can be used to measure the total contents of the piece of fruit. From the thickness of the shell and the outer volume of the shell, the volume of pulp can be established by conventional calculation methods . Yet another example of such an application is the measurement of the cross-section in a top end portion 83 (see Fig. 16) of the femur 84 in the thigh 85 for the purpose of preselecting a fixing pin of suitable size for fixing a joint member of an artificial hip joint thereto. By combining a body penetrating measurement, for instance by X- ray imaging, with a non penetrating measurement such as computerized surface topography measurement (e.g. raster stereography) , it can be established more accurately in which position 86 of the bone 84 the cross-section has been imaged without taking additional body penetrating measurements. To achieve this, the position of the head 87 of the bone 84 of the thigh 85 is estimated from the shape of the outer surface of the body of the patient in the area of the bone-head 87. This can be carried out by a human expert or using pattern recognition techniques which are preferably, but not necessarily, based on fuzzy logic. The position of the cross-section where the image is taken by body penetrating measurements is determined in relation to the estimated position of the bone head 87, so that the implications of the observed cross-section to the required size of the fixing pin can be established more accurately. This portion of the measurement process can be carried with or without fuzzy logic's.

To determining the position 85 on the exterior surface of a body which position corresponds to the position of the bone-head 87, a system is provided, which includes

The system further includes a computerized surface topography unit 90 formed by two raster stereography projecting and image capturing units 91, 92 which are connected to a surface topography processor 93 and an X-ray imaging unit 94 for taking non-invasive body-penetrating measurements. The imaging unit 94 has an image capturing head 95 which is movable along a guide rail 96 and driven by a drive unit 97 under control of a processor 98.

The processor 98 is also connected to image capturing head 95 for generating a second set of data relating to the shape of the tissue structure from a non-invasive body- penetrating measurement result. The processors 93 and 98 are connected and programmed such that the position of the measurement head 95 at the moment the body penetrating measurements are taken is determined by the first set of data collected by the image capturing units 91, 92. Thus, the processor 98 for controlling the drive unit 97 also forms an indicator for indicating the position where measurements are to be taken.

The cognizable representation of the desired information regarding the shape may be of a fuzzy nature as well, for instance by displaying the ideal representation, which forms the most presumable representation based on the available foreknowledge and available measurement data, and ranges around that ideal representation with gradually or stepwise decreasing degrees of presumability . This is illustrated by Figs. 3-5 which show representations of the positions of 4 marking points which may for instance characterize the shape and the orientation of a vertebra in a lateral side view. Fig. 3 shows a fuzzy description of the positions of the marking points as determined on the basis of a first set of measurements. In the present example, it is assumed that from information obtained from measurements of adjacent vertebra and from general knowledge about the spine fuzzy descriptions of the horizontal distance between the upper two marking points are available. This allows to improve the fuzzy representation to what is shown in Fig. 4. If, in addition, also a fuzzy representation is available regarding the relative positions of the left two marking points, the fuzzy representation can be improved to what is shown in Fig. 5. It is noted that for the vertical position of marking points, typically the results of measurements in X and Y direction can be combined, to get a more accurate estimate of the vertical positions of marking points.

In Fig. 13 a method is shown in which a first acquisition at a moment t=l and a second acquisition at a moment t=2 are made of information about the shape of a tissue structure in different situations. The situations can for instance be subsequent moments in the course of a therapy such as the treatment of Scoliosis employing a correction brace.

Each of the acquisitions of information includes the step 26, 27 of taking a first set of non penetrating measurements for the respective acquisition of information. In the present example, which relates to the gathering of information regarding the shape of the back of a Scoliosis patient as well, this first set of measurements is carried out by computerized surface topography. As such, measurement by means of computerized surface topography is well known. Examples of its application to the field of the acquisition of information regarding spinal shapes are for instance described by: Millner et al . in "Familial Spinal Shape Analysis using Three-Dimensional Surface Co-ordinates and Multivariate Analysis" Journal of Bone Joint Surgery 75B Supp . Ill p264, 1993. Advantages of computerized surface topography - and more in general of optical measurement methods - in this application are that it can be carried out with a relatively cheap system, that it has no known adverse effect on the health of the patient and that the measurements can be taken very quickly, so that artefacts caused by body sway are avoided.

The first acquisition of information also includes the step 28 of taking a second set of measurements from the spinal structure. In the present example, this is carried out by making X-ray image and processing the images into electronically processable data. As such, this presently forms the most practised manner of obtaining information about the shape of the spine.

The measurement data from the first and second sets of measurements are received and are each fuzzified (steps 29, 30) into corresponding fuzzy descriptions #A and #B of the spinal structure^' as determined by the respective measurement data and including clearness range indications.

Since, in contrast to computerized surface topography, X-ray imaging is a body-penetrating imaging technique, data obtained by X-ray imaging can be used to substantially compensate for measurement errors due to the variations in the relation between what is detectable from the outside and the structural configuration of the spine inside the patient.

The combination of imaging techniques penetrating into the body of a patient and measurement techniques taking measurements of structures inside the patient on the basis of observations of the outer surface of the patient are particularly accurate where the outside shape clearly reflects the shape and position of the tissue structure at issue inside the patient. Tissue structures in a patient of which the shape is particularly well reflected by the shape of the outer surface of the patient are supporting and protecting tissue structures and more in particular skeletal tissue structures. The shape of the spine of a human or an animal is an example of a tissue structure of which the overall shape is very well reflected by the shape of the outer surface of the patient. The acquisition of information about the shape of the spine at time t=l is carried out in essentially the same manner as described with reference to Fig. 1. The fuzzy descriptions #A and #B are merged (step 31) into an enhanced fuzzy description 32. Finally, the enhanced fuzzy description is converted (step 33) into a signal for displaying the information in a human perceptible form 34. To obtain information about the shape of the spine of the same patient at a moment t=2, which may be days or weeks later after the patient has undergone treatment in the form of wearing a brace, a next first set of measurements is taken at that moment t=2. These repeated measurements are taken by computerized surface topography (step 27). This measurement technique is particularly advantageous for frequently repeated measurements, because it does not entail any known adverse effects on the patient and it can be carried out quickly and at relatively low cost.

The results of these measurements 27 are fuzzified as well (step 35) , but in conjunction with the data obtained from the measurements taken at the moment t =1, to obtain a fuzzy description Δ#A. These results are merged with the enhanced fuzzy description 32 of the shape in the situation at t=l (step 36) to an enhanced fuzzy description 37 of the shape of the spine at t = 2. Thus, indirectly, the second fuzzy description #B based on the X-ray imaging measurements at time t = 1 are used for the second acquisition of information about the shape of the spine at time t=2 as well, so that the error compensating effect of the data obtained from the X-ray imaging measurements at t=l can be used to compensate for essentially the same effects at time t=2 as well without having to repeat the taking of X-ray images and the associated exposure of the patient to radioactive radiation.

Subsequently, the enhanced fuzzy description 37 of the shape of the spine at t=2 is converted (step 38) into a human perceptible display 39 of the shape of the spine at t=2 and a representation of the difference in shape which has occurred between t=l and t=2. The latter information is helpful to devise corrections to the treatment of the patient . Preferably, the enhanced fuzzy description relating to the shape of the spine at t=2 will, in turn, be used to obtain a next enhanced fuzzy description applying to the situation at t=3 by merging that enhanced fuzzy description 37 with a next set of measurement data obtained by computerized surface topography at time t=3 and so forth. In view of the close relation between the measurements 26, 28 at t=l, it can also be advantageous to each time merge the enhanced fuzzy description 32 applying to t=l with each next set of measurements at t=l+n to obtain the desired compensation until a next set of measurements based on X-ray imaging is made.' In addition, it is also possible to provide a fourth set of data describing general rules applicable to the shape of a particular tissue structure in at least a group of bodies and to further improve the accuracy of the third set of data by combining the fourth set of data with at least one of said first, second and third sets of data.

In Fig. 14 an example of a system for acquiring information about a property of a physical structure is shown in the form of a system for acquiring information about the shape of the spine of a patient (possibly) suffering from Scoliosis.

The system includes an observation position represented by footprints 69 where a patient from which information is to be acquired is to stand. To reduce artefacts due to body sway and variations in body posture between measurements of successive acquisitions of information, a support frame 70 is provided against which a patient is to stand.

The system includes an X-ray sensor structure 68 for taking a set of X-ray measurements. The X-ray system has an X-ray transmitter 71 and an X-ray receiver 72 for imaging the spine in side elevational view and an X-ray transmitter 73 and an X-ray receiver 74 for imaging the spine in rear view. The transmitters 71, 73 and the receivers 72, 74 are connected by an X-ray processor 75 for controlling the transmitters 71, 73 and processing the signals received by the receivers 72, 74. The system further includes a computerized surface topography unit 76 formed by two raster stereography projecting and image capturing units 77, 78, which are connected to a surface topography processor 79. The X-ray processor 75 and the surface topography processor 79 are connected to a central data processor unit 80. The central data processor unit 80 preferably controls the X-ray processor 75 and the surface topography processor 79 to capture the lateral and dorsal X-ray images and the surface topography images simultaneously, or very shortly after each other, so that body sway and other movements of the patient create virtually no disturbance of the correspondence between the measurement results. The central data processor unit 80 includes a data storage structure 81 for receiving and storing data obtained by the sensor structures 68, 76. The central data processor unit 80 is programmed in accordance with algorithms shown in Figs. 1, 13 and 15 for processing the data received from the measurement structures 68, 76 into a signal representing the information about the shape of the spine of the patient from which the measurements have been taken. The system further includes an operating interface 82 for generating a cognizable signal representing the desired information about the shape of the spine that has been acquired. The signal can be formed by 3-D data converted into an image on a display which image can be changed to represent different views of the spine. The operating interface may also include a printer, so that the signal can in addition be formed by an image printed on a sheet and can be made readily available in absence of a suitable display (for instance at the patient's home or in the practice of a physiotherapist) as well.

A problem of merging fuzzy descriptions to structures of which objects are in a large number of neighbour-to- neighbour relations, such as the vertebrae of a spine, is how to apply the fuzzy neighbour-to-neighbour relations to obtain the most valid and most efficient reduction of vagueness . In Fig. 15 a particularly effective solution to solve this problem is shown. The shown process forms a subroutine of the merging step 7 in Fig. 1.

After the algorithm has been started (step 59) , the first operative step 60 is formed by determining the order of vagueness reduction achievable with the available neighbour-to-neighbour relations. It is also possible to include other generally applicable fuzzy rules in the considerations . Then the difference between the largest vagueness reduction and the next largest vagueness reduction is compared with a threshold value "r" (step 61) to check whether the difference is presumably significant enough to decide which fuzzy relation to use first (or next) to reduce the vagueness of the fuzzy descriptions obtained from the sets of measurement.

If the outcome of the comparison is affirmative, the fuzzy descriptions #A and #B, or in a later cycle the fuzzy description based thereon, are combined with the neighbour- to-neighbour relations providing the largest vagueness reduction (step 62) and the vagueness of the fuzzy description is accordingly reduced towards the most presumable values (step 63) . Then a next vagueness reduction cycle is started with step 60. If the outcome of the comparison 61 is "no", i.e. there is no sufficiently significant difference between the vagueness reducing effect of the neighbour-to-neighbour relations which provide the largest vagueness reductions or the largest vagueness reduction is smaller than "r", the next achievable vagueness reductions are calculated for the n (for instance best 5%) neighbour-to-neighbour relations that provide the largest vagueness reductions at the present stage (step 64 ) .

Then, the difference between the largest overall (two- step) vagueness reduction and the next largest overall vagueness reduction is compared with a threshold value "s" (step 65) to check whether the difference is presumably significant enough to decide which fuzzy relation to use first (or next) to reduce the vagueness of the fuzzy descriptions obtained from the sets of measurement.

If the outcome of the comparison is affirmative, the fuzzy descriptions #A and #B, or in a later cycle the fuzzy description based thereon, are combined with the neighbour- to-neighbour relations providing the largest overall vagueness reduction (step 62) and the algorithm continues as described above. If the outcome of the comparison 65 is "no", i.e. there is no sufficiently significant relative difference between the overall vagueness reducing effect of two successive neighbour-to-neighbour relations which provide the largest vagueness reductions, the largest vagueness reduction is compared with a predetermined minimum vagueness reduction level "v" (step 66). If the achievable vagueness reduction is lower than that level, the algorithm is stopped and a signal is generated that no, or no further, information is available that appears meaningful in view of the presupposed information about the physical structure. If the achievable vagueness reduction of the fuzzy descriptions #A and #B or of the fuzzy description based thereon is larger than or equal to "v", the vagueness thereof is increased (step 67) and a next attempt is made to determine a most vagueness reducing neighbour-to-neighbour relation (step 60) as described above.

The process defined by the algorithm is stopped if application of all neighbour-to-neighbour relations does not yield further improvement above a pre-set minimum level. It is noted that it can occur that during the process the vagueness level is first reduced to a given level, which level subsequently turns out too irreconcilable at the present vagueness level with remaining neighbour-to- neighbour relations. Accordingly, the vagueness level is then increased to accommodate for the differences between the presumed fuzzy description and the results of the measurements and the final result accordingly is more vague. Thus, on the one hand an effective increase in the completeness and the accuracy of data that are in accordance with the generally expected situation is obtained, while on the other hand substantial deviations from the generally expected situation can be validly reflected in the presented information as well.

Fig. 17 shows an example of a third set of data obtained from a first set of data obtained by non penetrating raster stereography measurements and a second set of data obtained by penetrating X-ray measurement. As appears from Fig. 17, the third set of data further describes the configuration of a combination of at least portions of the tissue structure formed by the spine 88 and the exterior surface shape, which is in this example formed by the dorsal side of the patients' upper body 89. 10. The optical image of the exterior surface shape that is obtained by raster stereography is a binary image, which facilitates further processing of the collected image data.

The combined knowledge of the shape of the exterior surface 89 of the body and of the tissue structure 88 to be treated within the body allows to automatically determine the configuration of positions or areas where external loads are to be exerted and the magnitudes of these loads from the third set of data. To this end, expert rules where and with what magnitude the loads are to be applied can be expressed in the form of fuzzy descriptions. After these results are applied to the obtained data, the resulting 3D fuzzy description of the locations and magnitude (for instance in terms of "hard", "medium", "soft" and "no" pressure) of the loads to be applied can be converted via membership functions into a crisp representation on the basis of which a correction brace can be tailored to the respective patient. In this instance, suitable defuzzification techniques are for instance the "Centroid method" or the "Max-membership principle" (see for instance Hellendoorn and Thomas; "Defuzzification in Fuzzy Controllers", In telligent and Fuzzy Controllers , vol. 1, pp 109-123). The results of loads exerted on spines of patients under treatment can in turn be used to improve the fuzzy set of rules determining the loads to be exerted to counteract different scoliotic deformations . The ease of measurement according to the invention further provides the possibility to compare the shapes of spines with and without wearing a brace, preferably by taking non body-penetrating measurements only. The effect of the brace on the spine of an actual patient can thus be used to improve the fine adjustment of the brace. In the meantime, the results obtained thereby can be used to improve the fuzzy description of the design of braces for different scoliotic deformations .

Furthermore, if geometrical data on the shape of the spine are incomplete, it is possible to improve the clearness of the information about the shape of the spine by deriving information about the forces applied to the spine from the incomplete geometrical data. The assumptions regarding these forces can in turn be used to improve the clearness of the geometrical description. This cycle can be repeated until no meaningful further improvement of the clearness is achieved. The description of the external forces acting on the spine which is obtained in this manner can also be used for designing a brace. From the data about the shape and the positions of the load transfer area, the shape of portions of the corrective brace which are adapted to be in load transferring contact with the exterior surface of the body can be determined automatically as well, by inverting the measured shapes in selected areas where loads are to be transferred.

Claims

1. A method for measuring the shape of an internal structure within a physical structure, including the steps of: taking non penetrating measurements of an exterior surface shape of the physical structure in at least a region where said exterior surface shape is related to the shape of said internal structure; receiving a first set of data describing said exterior surface shape in at least said region; taking penetrating measurements of the shape of said internal structure; receiving a second set of data relating to the shape of said internal structure; and determining a third set of data describing the shape of said internal structure by combining at least selected portions of said first and second sets of data.

2. A method according to claim 1, wherein said third set of data further describes the configuration of a combination of at least portions of said internal structure and of said exterior surface shape.

3. A method according to claim 1 or 2, wherein at least portions of said non penetrating measurements and said penetrating measurements of corresponding portions of said exterior surface shape and of said internal structure are taken during a single measurement session.

4. A method according to claim 3, wherein at least portions of said non penetrating measurements and said penetrating measurements of corresponding portions of said exterior surface shape and of said internal structure are taken simultaneously.

5. A method according to any one of the preceding claims, wherein said internal structure includes a supporting or protecting tissue structure.

6. A method according to claim 5, wherein said tissue structure includes a skeletal tissue structure.

7. A method according to claim 6, wherein said tissue structure is at least a portion of a spine.

8. A method according to any one of the preceding claims, wherein at least some of said non penetrating measurements are carried out at least two times, said internal structure being in different conditions during said first and second measurements .

9. A method according to any one of the preceding claims, wherein said non penetrating measurements of an exterior surface shape of the physical structure include obtaining at least one optical image of said exterior surface shape.

10. A method according to claim 9, wherein said optical image of said exterior surface shape is a binary image .

11. A method according to any one of the preceding claims, wherein a fourth set of data describing general rules applicable to the shape of said internal structure in at least a group of bodies is provided and wherein said third set of data is further determined by combining said fourth set of data with at least one of said first, second and third sets of data.

12. A method for determining a pattern of loads to be exerted to the exterior of a body, including the steps of: taking non body-penetrating measurements of an exterior surface shape of the body in at least a region where said exterior surface shape is related to the shape of an internal tissue structure within the body; receiving a first set of data describing said exterior surface shape in at least said region; taking non invasive body-penetrating measurements of the shape of said tissue structure; receiving a second set of data relating to the shape of said tissue structure; determining a third set of data describing the shape of said tissue structure and the configuration or shape of at least portions of said tissue structure and said exterior surface shape by combining at least selected portions of said first and second sets of data; and determining at least the configuration of positions or areas where said^' loads are to be exerted from said third set of data.

13. A method for preparing a tailored device which in use exerts loads to the exterior of a body, including the steps of: taking non body-penetrating measurements of an exterior surface shape of the body in at least a region where said exterior surface shape is related to the shape of an internal tissue structure within the body; receiving a first set of data describing said exterior surface shape in at least said region; taking non-invasive body-penetrating measurements of the shape of said tissue structure; receiving a second set of data relating to the shape of said tissue structure; determining a third set of data describing the shape of said tissue structure and the configuration or shape of at least portions of said tissue structure and said exterior surface shape by combining at least selected portions of said first and second sets of data; and determining the configuration of at least portions of said device, which portions are adapted to be in load transferring contact with said exterior surface, from said third set of data.

14. A system for measuring the shape of an internal structure in a physical structure, including: first measurement means for taking non penetrating measurements ; processing means connected to said first measurement means for generating a first set of data describing an exterior surface shape of the physical structure from a non penetrating measurement result; second measurement means for taking penetrating measurements; and processing means connected to said second measurement means for generating a second set of data relating to the shape of said internal structure from a penetrating measurement result; said processing means being programmed for determining a third set of data describing the shape of said internal structure by combining at least selected portions of said first and second sets of data.

15. A system according to claim 14, wherein said first and second measurement means are mounted for taking measurements in overlapping areas .

16. A system for determining the configuration of at least portions of a device which, in use, exerts loads onto the exterior of a body, including: first measurement means for taking non body- penetrating measurements; processing means connected to said first measurement means for generating a first set of data describing an exterior surface shape of the body from a non body- penetrating measurement result; second measurement means for taking body-penetrating measurements; and processing means connected to said second measurement means for generating a second set of data relating to the shape of an internal tissue structure from a body- penetrating measurement result; said processing means being programmed for determining a third set of data describing the configuration of at least portions of said device by combining at least selected portions of said first and second sets of data.

17. A system for determining at least one position on the exterior surface of a body which position corresponds to a position on an internal tissue structure within said body, including : first measurement means for taking non body- penetrating measurements; processing means connected to said first measurement means for generating a first set of data describing an exterior surface shape of the body from a non body- penetrating measurement result; second measurement means for taking body-penetrating measurements; and processing means connected to said second measurement means for generating a second set of data relating to the shape of said tissue structure from a body-penetrating measurement result; said processing means being programmed for determining a third set of data representing said position and said second set of data.

18. A system according to claim 17, wherein said first measurement means are operative in an area and further including at least one indicator for indicating said position in said area.

19. A system for determining a pattern of loads to be exerted to the exterior of a body, including: first measurement means for taking non body- penetrating measurements; processing means connected to said first measurement means for generating a first set of data describing an exterior surface shape of the body from a non body- penetrating measurement result; second measurement means for taking body-penetrating measurements; and processing means connected to said second measurement means for generating a second set of data relating to the shape of an internal tissue structure from a body- penetrating measurement result; said processing means being programmed for determining a third set of data describing said loads and positions or area ' s of said exterior surface where said loads are to be exerted by combining at least selected portions of said first and second sets of data.