Patent Abstract:
A method for transforming solid, three-dimensional image data into three-dimensional surface data first smoothes the surface of the solid image by removing extraneous voxels both attached to main surface of the solid image as well as extraneous voxels about the main image before the surface transformation. Once the solid image is transformed into a surface image, the method again cleans the surface by removing any significantly spiked structures that appear out of place when considered with respect to the surrounding topography. The amount of data required to render the surface image is reduced by removing surface facets that fall within a planar threshold of the surrounding topography. The topography is also compressed in local areas to bring the topography toward a median level. The method may further cycle to attempt to reduce additional surface facets that may fall now within the threshold limit.

Full Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to methods for transforming solid graphical image files into data that represents merely the surface of the solid image. 
     2. Background Art 
     In conventional x-ray systems, a two dimensional shadow image is created based upon the different x-ray absorption characteristics of bone and soft tissues. A great improvement on the conventional x-ray system as a diagnostic tool was provided by the development of computed tomography (CT) or computerized axial tomography (CAT) systems. These CT or CAT systems are x-ray based and initially were used to produce single two dimensional views depicting transverse slices of a body, object, or patient being investigated. Three-dimensional information was thereafter assembled from CT scan data by generating data for a number of contiguous slices and using the inferential abilities of the radiologist to suggest a three dimensional representation for the various internal organs. Shaded and contoured three dimensional images can be generated by interpolation between slices from the three dimensional array of data generated by a sequence of such contiguous CT scans. In the same way, the newer magnetic resonance imaging (MRI) technology is also capable of generating three-dimensional arrays of data representing physical properties of interior bodily organs. MRI systems offer an advantage over CT systems by providing the capability to better discriminate between various tissue types, not just bone and soft tissue. MRI imaging systems are also capable of generating physiological data rather than just image data. Again, as in CT systems, MRI data is available only as a sequence of slices and interpolation between the slices is necessary to render a three dimensional image. 
     In recent years CT and MRI images of a patient&#39;s heart have been used to aid cardiologists and other clinicians in performing electrophysiology studies or cardiac ablation treatments to diagnose and treat arrhythmias. The three-dimensional images of the heart help the clinician visualize the location of a catheter electrode within the heart to map and treat a patient&#39;s condition. Generally, however, only a rendering of the surface of cavities within the heart is necessary or desirable. Therefore, methodologies have been developed to transform the solid, three-dimensional information from MRI or CT image data into surface data only. By manipulating only surface data, much less processing power is required and greater rendering speeds are achieved. 
     Several options for transforming solid, three-dimensional image data into mere surface data have previously been developed, each with benefits and drawbacks. Such positive and negative aspects generally manifest themselves in relative processing speeds, accuracy of the surface rendering, and ability to identify and discard extraneous information. 
     The information included in this Background section of the specification, including any references cited herein and any description or discussion thereof, is included for technical reference purposes only and is not to be regarded subject matter by which the scope of the invention is to be bound. 
     BRIEF SUMMARY OF THE INVENTION 
     The present invention provides a new methodology for transforming solid, three-dimensional image data into three-dimensional surface data. As part of the transformation, the method smoothes the surface of the solid image by removing extraneous voxels, including both voxels attached to the main surface of the solid image as well as extraneous voxels about the main image before the surface transformation. Once the solid image is transformed into a surface image, the method again cleans the surface by removing any severely spiked, peak-like structures that appear out of place when considered with respect to the surrounding topography. The method further reduces the amount of data required to render the surface image by removing surface facets that fall within a planar threshold of the surrounding topography. The method additionally compresses the topography in local areas to bring the topography toward a median level. After compressing topographical areas, the method may cycle to attempt to reduce additional surface facets that may fall now within the threshold limit. Once a target level of data reduction is reached the three-dimensional surface image is considered complete and saved for use. An exemplary application for this method is in transforming MRI or CT image data into three-dimensional surface-only data for use in conjunction with electrophysiology studies and ablation procedures. However, the present invention can be used to transform any solid, three-dimensional solid image data into three-dimensional surface data. 
     Other features, details, utilities, and advantages of the present invention will be apparent from the following more particular written description of various embodiments of the invention as further illustrated in the accompanying drawings and defined in the appended claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flow diagram of a process for transforming a solid, three-dimensional image into a three-dimensional surface image according to the present invention. 
         FIG. 2  is a flow diagram detailing the process of the erosion/dilation step of  FIG. 1 . 
         FIGS. 3A-3D  are schematic diagrams representing the actions of the erosion/dilation process of  FIG. 2  on variously-sized voxel groupings. 
         FIG. 4  is a flow diagram detailing the process of the clutter removal step of  FIG. 1 . 
         FIGS. 5A-5C  are schematic diagrams representing the actions of the clutter removal process of  FIG. 4  on variously sized-voxel groupings. 
         FIG. 6  is a flow diagram detailing the process of the high-density tiling step of  FIG. 1 . 
         FIGS. 7A-7B  are schematic diagrams representing the effect of the high-density tiling process of  FIG. 6  in transforming voxels into planar triangular plates to form a three-dimensional surface image. 
         FIG. 8  is a flow diagram detailing the process of the surface cleaning step of  FIG. 1 . 
         FIGS. 9A-9B  are schematic diagrams representing the effect of the surface cleaning process of  FIG. 8  on the three-dimensional surface image. 
         FIG. 10  is a flow diagram detailing the process of the decimation step of  FIG. 1 . 
         FIGS. 11A-11B  are schematic diagrams representing the effect of the decimation process of  FIG. 10  on the three-dimensional surface image. 
         FIG. 12  is a flow diagram detailing the process of the surface smoothing step of  FIG. 1 . 
         FIGS. 13A-13C  are schematic diagrams representing the actions and effect of the smoothing process of  FIG. 12  on the three-dimensional surface image. 
         FIG. 14  is a flow diagram detailing the process of the facet normal computation step of  FIG. 1 . 
         FIGS. 15A-15B  are schematic diagrams representing the effect of the facet normal computation process of  FIG. 14  on the three-dimensional surface image. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention is directed to a method for transforming a solid, three-dimensional graphic image model into a surface rendering that closely approximates the outer surface of the three-dimensional graphic model. Such a transformation may be highly desirable when working with an image model that is a three-dimensional solid model, but only the information related to the exterior surface of the model is of interest to the user. In such cases, attempting to manipulate all of the three-dimensional information is a waste of computer processing power and further takes additional and unnecessary processing time to render the image. For example, in medical imaging processes such as MRI or CT, a solid image model is rendered by stacking the sliced images resulting from such scans. This solid, three-dimensional information generally comprises a large amount of grayscale voxel data indicating changes in tissue density or type to render a diagnostic medical image. When the only information desired is a rendering of the surface of a particular organ or tissue structure, for example, a heart chamber, the voxel data internal and external to the surface region of interest is superfluous. 
       FIG. 1  depicts a high-level flow chart  100  indicating a series of steps undertaken according to the present invention to transform a solid three-dimensional graphic model into a surface rendering that closely approximates the outer surface of the three-dimensional graphic model. In step  105 , the three-dimensional graphic model, which may be a segmented model of an anatomical scan file, for example, an MRI image or a CT image, is accessed and opened. A segmented model indicates that a subregion of a three-dimensional image has been digitally separated from a larger three-dimensional image, e.g., an image of the right atrium separated from the rest of the heart. Exemplary segmentation applications include ANALYZE (Mayo, Minneapolis, Minn.), Verismo (St. Jude Medical, Inc., St. Paul, Minn.), and CardEP (General Electric Medical Systems, Milwaukee, Wis.). Generally such image files are composed of three-dimensional image point information. Each image point is commonly referred to as a voxel, which is a three-dimensional version of a pixel, the common unit of graphic information in a two-dimensional display environment. Each voxel therefore comprises information necessary to render the three-dimensional graphic image, in particular, positional information, color information, and shading information. 
     Once the three-dimensional graphic file is accessed, the voxels on the surface of the solid image are subject to an erosion and dilation process, step  110 , which is described in greater detail with respect to FIGS.  2  and  3 A- 3 D. The erosion and dilation process  200  of  FIG. 2  is performed to simplify the three-dimensional model. The erosion and dilation process  200  is targeted to remove bridges or other narrow voxel structures that may be extraneous to the desired three-dimensional image or that otherwise unnecessarily complicate the three-dimensional image. In step  210 , one or more layers of voxels on the surface of the solid model are eroded or removed. The number of layers to be removed is set as a threshold variable and can be changed by the user depending upon the desired result. Operating from the presumption that a narrow string of voxels is likely not part of the desired surface image, setting the threshold to remove groups of voxels of less than a certain number in width will remove or erode such groups or chains. 
       FIGS. 3A-3C  schematically depict the effect of the erosion step  210 . As shown in  FIG. 3A , a grouping of voxels labeled A-L is subject to the erosion process. Note that while the group of voxels A-L in  FIG. 3A  are represented in a two-dimensional form for ease of presentation, in actuality the process is operating on three-dimensional voxels on the surface of a solid graphic model. For example, with the threshold erosion value set to one, the erosion process  210  will remove all voxels, except D and E. The voxels A, B, C, F, G, H, I, J, K, L subject to erosion are crossed out in  FIG. 3B . This should be apparent because only voxels D and E are surrounded on all sides by adjacent voxels. 
     Once the surface of the solid graphic model has been subject to erosion, step  210 , the process next performs a dilation or rebuilding step  220 . In this step, one or more layers of voxels are added back to the eroded surface. As shown in  FIG. 3C , only voxels D and E are left of the original image surface. However once the dilation step  220  is performed, voxels D and E are surrounded by new voxels A′, B′, C′, F′, I′, and J′. The circles in these voxels A′, B′, C′, F′, I′, and J′ indicate that they are newly formed as a result of the dilation process. As is apparent from  FIG. 3D , the thin bridge of voxels G and H is no longer part of the solid graphic surface. Similarly, original voxels K and L have been removed and thus the resulting surface is “smoother.” Once the dilation step  220  has been performed, the dilated solid image graphic is saved, step  230 , for additional processing according to further steps of this invention. 
     Returning to  FIG. 1 , after the erosion and dilation processes are performed, step  110 , the solid graphic image is next subject to clutter removal, step  115 . The process of clutter removal  400  is depicted in greater detail in FIGS.  4  and  5 A- 5 C. The purpose of the clutter removal process  400  is to remove any small or detached voxel clusters that do not meet the size limits of the threshold values set by the user. If the number of voxels in an independent group of voxels is below the threshold, that group will be deleted as “clutter.” The first step, therefore, is to determine the minimum threshold for numbers of voxels in a cluster or grouping that will be maintained as part of the image, step  410 . The next step is to identify each individual island or cluster of connected voxels, step  420 . For example, in  FIG. 5A , four separate clusters of voxels  500 ,  502 ,  504 ,  506  are identified. The first cluster  500  is depicted as having six voxels; the second cluster  502  is depicted as a single voxel; the third cluster  504  is depicted as having two voxels; and the fourth cluster  506  is depicted as a grouping of five voxels. 
     The clutter removal process  400  continues by counting the number of voxels in each individual island or cluster, step  430 . A first island or cluster is selected for examination, step  440 . A determination is then made whether the number of voxels in that particular island or cluster is below the threshold value previously set, step  450 . If a particular cluster of voxels numbers above the threshold value, such clusters will remain part of the solid image file. If a number of voxels is below the threshold value, that particular island or cluster of voxels will be discarded from the three-dimensional graphic image, step  460 . For example, as shown in  FIG. 5B , when the threshold is set at two voxels, the second cluster  502  and third cluster  504  are subject to deletion from the overall image file. After a cluster is removed (step  460 ) or if the number of voxels in the cluster exceeds the threshold value (step  450 ), the process  400  continues to determine whether additional islands or clusters need to be examined, step  470 . If additional voxel clusters need to be examined, the process  400  selects a new cluster on which to operate, step  480 . If additional clusters of voxels require examination, the process  400  returns to step  450  to compare the voxels in each cluster to the threshold. Thus, as shown in  FIG. 5C , while the second cluster  502  and third cluster  504  have been removed, the first cluster  500  and the fourth cluster  506 , each with more voxels than the threshold amount, remain as part of the graphic image file. The clutter removal process ends by saving the solid image with the remaining voxel clusters that exceed the threshold, step  490 . 
     Following the step of clutter removal  115 , as indicated in  FIG. 1 , a high-density tiling procedure  120  may next be implemented. The primary purpose of the high-density tiling step  120  is to transform the solid graphic image into a rendering of merely the outer surface of the solid image. By creating a surface rendering, a significant amount of data and information can be discarded and thus faster processing and manipulation of the image is possible. An exemplary high density-tiling process is depicted in greater detail in  FIG. 6 . This exemplary process is described in great detail in U.S. Pat. No. 4,710,876, which is hereby incorporated herein by reference in its entirety. 
     For the purposes of the present discussion, a high-level review of the high-density tiling process  600  is presented in conjunction with FIGS.  6  and  7 A- 7 B. Initially, the surface voxels of the solid image are identified, step  610 . Next with respect to each surface voxel, each exposed vertex is further identified, step  620 . For example, in  FIG. 7A , a single voxel is depicted with eight vertices labeled V 1 -V 8 . For the purposes of the present discussion, vertex V 1  is identified as a sole exterior vertex. In practice this means that each of the other vertices is adjacent to a vertex on another voxel. Each voxel can further be viewed as having twelve edges that extend between each of the vertices V 1 -V 8 . In  FIG. 7A , each of the edges is labeled E 1 -E 12 . Excluding the dashed lines connecting vertices V 1  and V 4 , V 3  and V 4 , and V 4  and V 8 , the dashed lines in  FIG. 7A  connect the midpoints of each of the edges and generally divide the voxel into four quadrants. 
     With this background, the next step of high-density tiling process  600  is to construct triangular plates normal to the exterior or exposed vertices, step  630 . This concept is depicted in  FIG. 7B . As shown in  FIG. 7B , a triangular plate is constructed between the midpoints of each of the edges E 1 , E 4 , and E 5 , common to the exterior vertex V 1 . As can be seen in  FIG. 7B , this triangular plate is normal to the vertex V 1 . When more than one vertex of a voxel is an exterior vertex, this concept of triangular plate construction is extrapolated to construct a collection of triangular plates that are normal to each of the exterior vertices. Once the triangular plates have been constructed for each of the exterior vertices of the surface voxels, the vertices of each of the triangular plates identified by respective edge midpoints of the voxels are translated into binary surface image information, step  640 . Once the triangular plates are represented in binary form, the three-dimensional voxel information for both the surface voxels as well as the interior voxels of the solid image are discarded, step  650 . The binary surface information of the collection of triangular plates corresponding to the surface of the original solid image is then saved, step  660 . 
     Once a solid graphic image has been transformed into a surface model via the high-density tiling step  120  as indicated in  FIG. 1 , the next step in the process is to clean the surface, step  125 . The purpose of cleaning the surface is to search for and remove any objects that are not substantially co-planar with the surrounding surface. The process of cleaning the surface is described in greater detail with respect to FIGS.  8  and  9 A- 9 B. The surface image formed of the triangular plates may be visualized as a surface “mesh” with areas of relative planarity populated by polyhedron structures that extend above adjacent areas. 
     The surface cleaning process  800  begins by determining an average plane of the base vertices of any polyhedron with a peak vertex above and connected along edges to the base vertices, step  810 . The distance from each peak vertex to the average plane previously determined in step  810  is then calculated, step  820 . A determination is then made as to whether the distance of the peak vertex from the average base is greater than a threshold value, step  830 . Generally, this threshold value will be chosen by the user to identify substantial aberrations protruding from areas of otherwise smooth surface configurations. If the separation distance of the peak vertex extends above the threshold value, the peak vertex and any edges connecting the peak vertex to the base vertices are removed from the surface mesh, step  840 . Once the peak vertex is removed, any polygonal base areas remaining after removal of the peak vertex are converted to triangular sections, step  850 . If the distance from the peak vertex to the local plane is below the threshold, then the surface cleaning process  800  skips from step  830  to step  860 . The process  800  queries whether all vertices have been examined, step  860 . If not, then the process  800  selects a new vertex for examination, step  870  and returns to step  810  to compute the average plane around the new vertex and complete the other steps in the process  800 . After the final base area is triangulated, the clean surface model is then saved for use in further processing steps, step  880 . 
       FIGS. 9A and 9B  are exemplary depictions of the surface cleaning process  800 . In  FIG. 9A , a sharp polyhedron  902  with a base  904  extends above an otherwise generally planar surface area  900 . Pursuant to the surface cleaning process  800 , the peak vertex V of the polyhedron  902  is removed as are the edges connecting the peak vertex V to the vertices of the base  904 . In  FIG. 9B , the peak vertex V and the edges connecting it with the base  904  have been removed. Only the base  904  remains as a hole or open area on the planar area  900  in  FIG. 9B . However, the base  904  is a non-triangular polygon and does not conform to the binary dataset desired for representing the three-dimensional surface. Therefore, the base  904  of the former polyhedron  902  is be appropriately re-triangulated into two triangular sections  906 ,  908  as shown in  FIG. 9B . 
     Returning to  FIG. 1 , after the surface has been cleaned in step  125 , the polygonal surface model is decimated, step  130 . The major purpose of decimation is to reduce the amount of information used to describe the three-dimensional surface. The high-density tiling algorithm of step  120  generally creates surfaces composed of 100,000 or more triangles. The data associated with locating and orienting such a large number of surface triangles creates high demand on processing power of a computer system manipulating the surface model. Therefore, it is desirable to attempt to remove a significant number of the triangles forming the surface of the image. Typically, a reduction to around 15,000 triangles representing the image surface reduces the corresponding data to a manageable amount. 
       FIG. 10  depicts in greater detail the steps involved in a decimation process  1000  of a polygonal model.  FIGS. 11A and 11B  provide graphic representations of what effect such a process has on the surface image. The decimation procedure  1000  is actually similar in some respects to the surface cleaning procedure  800  previously described with respect to  FIG. 8 . As before, the local average plane around a polyhedron peak vertex is determined, step  1010 . A distance of this peak vertex from the local plane is then measured, step  1020 . The process then queries whether the distance of the peak vertex from the local plane is below a threshold distance, step  1030 . If the peak vertex is not below the threshold distance, the process continues to select a new peak vertex to undertake similar calculations, step  1080 . If the peak vertex separation distance is below the threshold distance, the process further queries whether the dihedral angles of the polygon formed by the peak vertex are greater than a threshold value. The larger the dihedral angles, the flatter the polyhedron formed by the peak vertex will be. This indicates that the polyhedron formed by the peak vertex is very low to the surrounding local planar surface and thus the polyhedron is a good candidate for decimation. If the dihedral angles are not above the threshold value, the process continues to select a new peak vertex to undertake similar calculations, step  1080 . 
     In the event that the conditions in steps  1030  and  1040  are met, the peak vertex and the attached edges are removed from the surface model, step  1050 . As before in the decimation process  800 , the remaining polygonal base is re-triangulated, step  1060 , in order to transform any odd polygonal-shaped area into a collection of triangles. The decimation process  1000  then queries whether all potential peak vertices on the surface model have been examined, step  1070 . If not, the decimation process  1000  continues to repeat to examine all peak vertices and determine whether they meet criteria for decimation. Once the decimation process  1000  has examined all the peak vertices on the surface model, the decimated surface model is saved for further processing, step  1090 . 
     The results of the decimation process  1000  are seen through comparison of  FIGS. 11A and 11B . In  FIG. 11A , a polyhedron with a peak vertex V is composed of five facets labeled A-E. In this instance, the height d of the peak vertex falls under the threshold limit, the polyhedral angles Øexceed the threshold limit, and therefore the polyhedron is subject to decimation. In  FIG. 11B , the peak vertex V and adjacent edges have been removed and the remaining polygonal foot print has been re-triangulated to create a surface composed of three triangles A′, B′, and C′ instead of the prior polyhedral surface composed of five triangles A-E. Thus, it becomes apparent that by pursuing the decimation process  1000 , the amount of data necessary to describe the surface model is reduced. 
     Again returning to  FIG. 1 , after decimation step has been performed, step  130 , the surface image model may be smoothed via an associated smoothing process, step  135 . An exemplary smoothing process  1200  is presented in greater detail in  FIG. 12 . The smoothing process  1200  begins in a similar manner to the decimation process  1000 , The local plane around each triangles&#39; vertices are determined, step  1210 , and the distance of the vertices on the surface model from respective local planes are calculated, step  1220 . However, in the smoothing process  1200 , an entirely different methodology is performed. In the smoothing process  1200 , the distances of the vertices from their respective local planes are translated into a waveform, step  1230 . An exemplary wave form is depicted in  FIG. 13A . The peaks in the wave form in  FIG. 13A  represent the relative distance of adjacent peak vertices from surrounding base vertices. 
     In the next step of the smoothing process  1200 , a Fourier transform is performed on the waveform, step  1240 , whereby the domain of the waveform is changed to an amplitude domain. The amplitude domain is depicted as a curve in  FIG. 13B  which represents the number of peak vertices plotted against their respective height or distance from the local plane. For example in  FIG. 13A , most of the peak vertices may have a separation distance that falls within a medium height. In the next step of the smoothing process  1200 , a filter is applied to the transform to remove low and high values based upon a range or percentage threshold of amplitude values to retain set by the user, step  1250 . This filtering is represented in  FIG. 13B  by the vertical lines that separate the middle values from the values marked high and low. Once the filter has been applied in step  1250 , a reverse Fourier transform is performed on the attenuated values in the amplitude domain waveform, step  1260 . The separation distance of the peak vertices from the local plane is thereby reduced due to attenuation of high height vertices, step  1270 . Further, the attenuation also affects any low lying vertices and thereby raises the local plane in those areas. The relative raising and lowing of vertices is represented by the arrows in  FIG. 13A . A final surface resulting from the smoothing process  1200  is represented in  FIG. 13C , wherein the result is a shorter separation distance between peak vertices and the local plane. Common vertices between  FIGS. 13A and 13C  are indicated by the exemplary dashed lines spanning between  FIGS. 13A and 13B  for several of such vertices. The smoothing process  1200  terminates by saving the smooth surface model, step  1280 . 
     The next step of the surface rendering process is again presented in  FIG. 1 . Recalling that the goal is to reduce the amount of data needed to represent the surface model, the process queries whether the triangle budget has been met, step  140 , via the decimation step  130  and the smoothing step  135 . As previously noted, an exemplary goal for the reduction of triangle facets from the surface model may be to reduce over 100,000 triangles to under 15,000 triangles to represent the surface image model. If the desired triangle reduction level has not been met, the process returns from step  140  to perform the decimation process in step  130  and further the smoothing process in step  135  until the triangle budget of step  140  is ultimately met. It should be noted that by performing the smoothing process of step  135 , the separation distances between any peak vertices of polyhedrons protruding from the surface model and their local plane are regularly reduced. By reducing this separation distance, greater numbers of peak vertices become eligible for decimation pursuant to step  1030  of  FIG. 10  as the separation distances of more peak vertices now fall below the threshold distance. Therefore, by repeatedly performing the decimation step  130  and the smoothing step  135 , the triangle budget of step  135  can ultimately be met. It should be further noted that the order of performing the decimation step  130  and the smoothing step  135  is not important and can be reversed. 
     Once the triangle budget of step  140  is achieved, the process computes facet normals (i.e., a unit vector indicating the direction perpendicular to the face of a triangle plate) for each of the remaining triangles forming the surface model, step  145 . Steps involved in computing facet normals are indicated in greater detail in  FIG. 14 . The facet normal computing process  1400  is a relatively standard process in creating surface image renderings. Facet normal data are used by graphic rendering programs to indicate appropriate shading for a particular facet based upon the relative location of an artificial light source. The facet normals are used to further compute vertex normals for each vertex of each triangular plate forming the three-dimensional surface model. The vertex normal information is saved with the surface image information for later graphic rendering. First, the normal to each facet or triangular plate in the surface image is computed, step  1410 . Next, the facet normals for facets surrounding a particular vertex are averaged together, step  1420 . The average of the facet normals surrounding the particular vertex is assigned to or identified with the particular vertex, step  1430  and saved with the surface model information, step  1440 . 
     For the purposes of the process  100 , the facet normal process  1400  is complete at step  1440 . However, a discussion of some of the general steps performed later by an exemplary graphic rendering program is helpful to understanding the rational for creating and storing vertex normal information. When determining shading for the application of grayscale or color to the surface image, many graphic rendering programs seek vertex normal information. The average of the normal values for the three vertices surrounding a facet is computed for each triangular facet of the surface image, step  1450 , and then this average value for the vertex normals is reassigned to a respective facet as the facet normal, step  1460 . The boxes for these steps are shown in phantom in  FIG. 14  because technically, they are not part of the surface image rendering process  100 . By averaging the normal vector information twice, a significantly more uniform surface image, especially with respect to grayscale or color shadings applied to the surface under a point light source, is achievable. 
     The benefit of this process is illustrated in  FIGS. 15A and 15B . In  FIG. 15A , a polyhedron with a peak vertex has several facets that would reflect light from a light source in a graphic surface rendering program in completely different directions as indicated by the arrows. Because of this difference in reflection direction, the gray scale applied to the facets of the polyhedron will be non-uniform and will further likely reflect the light source in completely different directions than a surrounding smoother surface area. However, by computing the vertex normals (see  FIG. 15B ), and then averaging the vertex normals for each triangular facet, the reflection direction for any gray scale application will be more coherent across the surfaces of the polyhedron. Once the facet normals and vertex normals are computed, the process outlined in  FIG. 1  terminates by saving the completed three-dimensional surface model in a format for later use, step  150 . 
     Although various embodiments of this invention have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of this invention. All directional references (e.g., proximal, distal, upper, lower, upward, downward, left, right, lateral, front, back, top, bottom, above, below, vertical, horizontal, clockwise, and counterclockwise) are only used for identification purposes to aid the reader&#39;s understanding of the present invention, and do not create limitations, particularly as to the position, orientation, or use of the invention. Connection references (e.g., attached, coupled, connected, and joined) are to be construed broadly and may include intermediate members between a collection of elements and relative movement between elements unless otherwise indicated. As such, connection references do not necessarily infer that two elements are directly connected and in fixed relation to each other. It is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative only and not limiting. Changes in detail or structure may be made without departing from the basic elements of the invention as defined in the following claims.

Technology Classification (CPC): 6