as used herein and in the appended claims , the singular forms “ a ,” “ an ,” and “ the ” include plural references unless the context clearly dictates otherwise . thus , for example , reference to “ an array ” can include a plurality of arrays and a reference to “ the centroid ” can include reference to one or more centroids , and so forth . unless defined otherwise , all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this invention belongs . although any method , device and material similar or equivalent to those described herein can be used in the practice of the invention , the preferred methods , devices and materials are now described . the apparatus and method described herein , for assigning coordinates to samples of an array , work from an initial list of centroids . a centroid represents the center of mass of a sample or a putative sample ; therefore , in the analysis , a centroid corresponds to a sample of the array . in practicing the invention , the apparatus and method associate to each centroid a set of coordinates , for example , a row and a column in the array , or identify the centroid as a suspicious or ambiguous point that should be excluded from the regular array . the inventive apparatus and method for labeling rows and columns in arrays of samples can accommodate the irregularities of the array described above . the apparatus and method proceed via a sequence of deductions concerning the lattice structure of the array . the apparatus and method begin from a region that conforms well to a local lattice structure and spreads out from this initial seed . fig7 illustrates one embodiment of the method for labeling rows and columns in an array of samples . a list of centroids is created from an array of samples 100 . the method searches for a starting region , defined as a region of centroids that best matches a local lattice structure 110 . the local lattice may be , for example , a three - by - three grid of points representing a center centroid and its eight nearest neighboring centroids . the size of the grid of the local lattice structure may be of any size and is only limited in that it have a center point , thus another example is a five - by - five grid . the method selects a best local lattice structure for the centroids and calculates a measure of the quality of fitness of the lattice to the centroids 130 . the calculated values are then sorted by their quality of fitness to determine the centroid having the best quality of fitness value and defining that centroid as the starting point 140 . the measure of the quality of fitness of a lattice structure for a given centroid is computed as follows . the lattice points relative to a center centroid ( x c , y c ) are given by the equation ( x grid , y grid )=( x c , y c )+ k · v a + l · v b ( 2 ) where k and l are integers and v a and v b are the two lattice vectors that describe the local lattice and that remain to be determined as part of the fit . in the case of a local lattice having an three - by three grid , the eight closest centroids n8 ={( x k , y k ), k = 1 , . . . , 8 }, are each associated with a node , or point , on the model grid by minimizing the equation ( x k − x grid ) 2 +( y k − y grid ) 2 ( 3 ) over the values of k and l for any given values of v a and v b , where ( x grid , y grid ) are as defined in equation ( 2 ). the grid parameters v a and v b are then adjusted to minimize the sum of the squared distances from the actual positions of the eight centroids closest to ( x c , y c ) and their nearest lattice points . specifically , the quality of fitness , fit ( x c , y c ), for a given centroid , ( x c , y c ) is determined by the equation fit ⁡ ( x c , y c ) = min v a , v b ⁡ ( ∑ k = 1 n ⁢ ( ( x k - x grid ⁡ ( k ) ) 2 + ( y k - y grid ⁡ ( k ) ) 2 ) ) ( 4 ) where n is the number of neighboring centroids , in this case eight . the sum of these eight squared distances is the measure of the quality of fitness of a region &# 39 ; s local lattice match . given reasonably close starting values of v a and v b , a local optimization can be carried out analytically . the choice of k and l for a given center centroid ( x c , y c ), its neighboring centroids ( x k , y k ), and lattice vectors v a and v b is achieved by expressing the displacement ( x k − x c , y k − y c ) as a linear combination of v a and v b , and rounding the coefficients to the nearest integers . using these integers k k and l k for the ( x grid ( k ) , y grid ( k ) ) positions , the resulting objective function is quadratic in the four unknowns v a =( v ax , v ay ) and v b ,=( v bx , v by ). setting the four partial derivatives of the objective function in equation ( 4 ) equal to zero , gives four linear equations which may be solved for the optimal values of v a and v b . the solution determines the horizontal and vertical vectors that generate the best model grid for the region . the starting values of v a and v b are v a =( x max / n cols , 0 ) and v b =( 0 , y max / n rows ). once a region with the best quality of fitness value is found , its v a and v b are used as initial values for the next optimization . the previous paragraphs describe how each centroid is assigned an associated pair of lattice vectors v a and v b and a quality of fitness value . the starting point of the method is the centroid with the best quality of fitness value . this starting centroid is assigned relative row and column coordinates ( i , j )=( 0 , 0 ) and its lattice vectors are denoted by v a * and v b *. referring to the embodiment shown in fig7 , once the starting centroid is identified , the diffusion step can proceed 160 . however , before beginning the step of assigning coordinates to the remaining centroids by way of the outwardly diffusion process , “ suspicious points ” in the list of centriods are identified 150 . because these suspicious points question whether this centroid is a real sample or merely a mistake , these suspicious points will not be a center centroid during the diffusion process . suspicious points can be identified by either ( a ) having a quality of fitness value as determined by equation 4 above some tolerance level ε 1 , or ( b ) having a value when the local lattice vector associated with the centroid in question is compared to the local lattice vectors associated with the starting centroid ( which has been determined to have the best quality of fitness value ) above some tolerance level ε 2 . the tolerance levels can be user specified . in one embodiment , the tolerance levels can specify limiting values of certain quantities calculated from the ( x , y ) coordinates of the centroids and , as such , may be highly dependent on the scale used in digitizing the image . to eliminate this dependence , the values of these tolerance levels , ε 1 and ε 2 , are calculated from user - specified tolerances δ i , i = 1 and 2 which are measured in units of a lattice spacing . to rescale from units of a lattice spacing to the units used in the digitized grid , it is convenient to define a parameter m , which represents the mean lattice spacing , as m = 1 2 ⁢ (  v a *  +  v b *  ) where v a * and v b * are the lattice vectors of the starting centroid , having been determined that the centroid had the best quality of fitness value . it is also possible to define m by using the scales of δx and δy . where n is the number of neighboring centroids , which would be eight for a three - by - three grid . for a centroid to be identified as suspicious , its quality of fitness is greater than the tolerance level ε 1 and fit ( x c , y c ) is a sum of n squared deviations and δ 1 represents the average deviation between a lattice point and a neighboring centroid in units of m . the user can also specify that if the quality of fitness is equal to ε 1 , the centroid should also be labeled as suspicious . the second tolerance level , ε 2 , is used to identify suspicious points on the grounds that their lattice vectors deviate too much from the ideal . in the method , this can be measured by ɛ 2 & gt ;   v i   v i *  - 1  ; i = a , b . this makes δ 2 a fractional threshold comparing the size of the locally optimized lattice of the centroid to the best lattice of the starting centroid . this test condition serves to eliminate harmonics . if a set of points is well described by the lattice vectors v a , v b , then they are also well described by v a / 2 and v b / 2 . these locally optimal solutions are called harmonics . if the optimized lattice is too small , then the lattice vectors correspond to higher harmonics . similarly , if the lattice is too large it is called lower harmonics . once all the centroids have been evaluated in order to identify suspicious centroids , the diffusion step proceeds 160 . alternatively or additionally , the method may provide for re - evaluating the centroids periodically to note any changes in the tolerance values . in other words , as the diffusion step proceeds , a centroid may be a neighboring centroid in several lattice structures . as more information is gathered , the tolerance measurements for a centroid may be re - calculated to see if additional information has changed the tolerance measures below the applicable tolerance threshold levels , ε 1 and ε 2 , to justify removal of the suspicious label . this “ re - evaluation ” of a suspicious point can be done after the assignment of a coordinate , or before the selection of a new center centroid . it can also be done periodically during the diffusion process . in the diffusion process 160 , the method provides for selecting a centroid ( x k , y k ) to be used as the next center of a new local lattice and then assigning row and column coordinates to all the points lying sufficiently near the lattice point , such as those found in a five - by - five grid having a center at ( x k , y k ) 170 . the new center must fulfill four criteria : ( a ) it has not been labeled suspicious as described in the previous paragraph ; ( c ) it has not been previously used as a center in the method ; and ( d ) it has been assigned relative row and column coordinates ( i , j ). among the centroids that fulfill these four conditions , the one which lies closest to the starting center ( i , j )=( 0 , 0 ) can be used as the next center centroid . fig8 a - 8c depict the diffusion pattern the method of the invention generates as it looks for neighbors to label . note that condition ( d ) implies that initially only the starting point can be chosen as a center . the coordinates of the next center centroid is denoted by ( x k , y k ) and its assigned relative row and column coordinates by ( i k , j k ). the diffusion step proceeds to generate the lattice points in the lattice structure . for example , in a lattice structure having a five - by - five grid , 25 lattice points are generated ; for each k and l value between − 2 and 2 , the centroid ( x , y ) closest to the lattice point is calculated by ( x grid , y grid )=( x k , y k )+ kv a k + lv b k these tentatively assigned coordinates are actually assigned to this centroid only if no previous coordinates have been assigned to this point and the distance √{ square root over (( x − x grid ) 2 +( y − y grid ) 2 )} is less than a third tolerance level ε 3 . if coordinates have previously been assigned and they do not match the newly calculated coordinates , then the centroid is identified as “ ambiguous ” and , thus , will not be used as a center . a user specified tolerance level δ 3 is used in the equation ε 3 & gt ;√{ square root over (( x − x grid ) 2 +( y − y grid ) 2 )} to decide whether the neighboring centroid ( x , y ) of a center centroid should be labeled based on its proximity to the nearest grid point emanating from the center centroid . therefore , if the third tolerance value of the neighboring centroid is less than ε 3 and no previous coordinates have been assigned it , it will be assigned a coordinate . defining δ 3 by the diffusion process continues until all eligible centroids have been used as the center centroid 180 . the relative row and column coordinates ( i , j ) can then be translated to absolute row and column coordinates by the following equation where i min and j min represent the smallest assigned i and j values 190 . besides the row and column assignments , the output of the present method can include lists of suspicious , ambiguous , and unlabeled centroids 200 . the various techniques , methods , and aspects of the invention described above can be implemented in part or in whole using computer - based systems and methods . additionally , computer - based systems and methods can be used to augment or enhance the functionality described above , increase the speed at which the functions can be performed , and provide additional features and aspects as a part of or in addition to those of the invention described elsewhere in this document . various computer - based systems , methods and implementations in accordance with the above - described technology are presented below . an apparatus of the invention may include the use of the methods of the invention in conjunction with any of the computer - based systems described herein . the computer - based system may include the means for inputting data either manually or automatically . the system can include devices for scanning the array , or otherwise inputting data about the array , digitizing the image , storing data , processing the data according to the methods described herein , outputting data , or any one of these functions . any or all of these functions may be automated . the processor - based system can include a main memory , preferably random access memory ( ram ), and can also include a secondary memory . the secondary memory can include , for example , a hard disk drive and / or a removable storage drive , representing a floppy disk drive , a magnetic tape drive , an optical disk drive , etc . the removable storage drive reads from and / or writes to a removable storage medium . removable storage media represents a floppy disk magnetic tape , optical disk , etc ., which is read by and written to by removable storage drive . as will be appreciated , the removable storage media includes a computer usable storage medium having stored therein computer software and / or data . in alternative embodiments , secondary memory may include other similar means for allowing computer programs or other instructions to be loaded into a computer system . such means can include , for example , a removable storage unit and an interface . examples of such can include a program cartridge and cartridge interface ( such as the found in video game devices ), a movable memory chip ( such as an eprom , or prom ) and associated socket , and other removable storage units and interfaces which allow software and data to be transferred from the removable storage unit to the computer system . the computer system can also include a communications interface . communications interfaces allow software and data to be transferred between computer system and external devices . examples of communications interfaces can include a modem , a network interface ( such as , for example , an ethernet card ), a communications port , a pcmcia slot and card , etc . software and data transferred via a communications interface are in the form of signals which can be electronic , electromagnetic , optical or other signals capable of being received by a communications interface . these signals are provided to communications interface via a channel capable of carrying signals and can be implemented using a wireless medium , wire or cable , fiber optics or other communications medium . some examples of a channel can include a phone line , a cellular phone link , an rf link , a network interface , and other communications channels . in this document , the terms “ computer program medium ” and “ computer usable medium ” are used to generally refer to media such as a removable storage device , a disk capable of installation in a disk drive , and signals on a channel . these computer program products are means for providing software or program instructions to a computer system . computer programs ( also called computer control logic ) are stored in main memory and / or secondary memory . computer programs can also be received via a communications interface . such computer programs , when executed , enable the computer system to perform the features of the present invention as discussed herein . in particular , the computer programs , when executed , enable the processor to perform the features of the present invention . accordingly , such computer programs represent controllers of the computer system . in an embodiment where the elements are implemented using software , the software may be stored in , or transmitted via , a computer program product and loaded into a computer system using a removable storage drive , hard drive or communications interface . the control logic ( software ), when executed by the processor , causes the processor to perform the functions of the invention as described herein . in another embodiment , the elements are implemented primarily in hardware using , for example , hardware components such as pals , application specific integrated circuits ( asics ) or other hardware components . implementation of a hardware state machine so as to perform the functions described herein will be apparent to person skilled in the relevant art ( s ). in yet another embodiment , elements are implanted using a combination of both hardware and software . in another embodiment , the computer - based methods can be accessed or implemented over the world wide web by providing access via a web page to the methods of the present invention . fig9 and 10 show a perspective view and a block diagram , respectively , of one embodiment of the apparatus of the invention . the apparatus for automatically processing an array is generally indicated by reference numeral 10 . the apparatus comprises a scanning subsystem . the scanning subsystem can include a microscope subsystem 32 housed in a housing 12 . the housing 12 includes an array carrier input hopper 16 and an array carrier output hopper 18 . a door 14 in the housing 12 secures the microscope subsystem from the external environment . a computer subsystem comprises a computer 22 having at least one system processor 23 , and a communications modem 29 . the computer subsystem further includes a computer monitor 26 and other external peripherals including storage device 21 , a pointing device , such as a track ball device 30 , a user input device , such as a touch screen , keyboard , or voice recognition unit 28 and color printer 35 . the apparatus can also include a separate image monitor 27 . an external power supply 24 is also shown for power outage protection . the apparatus 10 further includes an optical sensing device 42 , such as a camera , preferably a ccd camera , for acquiring images . microscope movements are under the control of system processor 23 . an automatic array feed mechanism in conjunction with x - y stage 38 provide automatic slide handling in the apparatus 10 . an illumination light source 48 can project light onto the x - y stage 38 where an array is placed . the array is subsequently imaged through the microscope subsystem 32 and acquired through the optical sensing device 42 for processing by the system processor 23 . in one embodiment , the x - y stage is moved to scan the image of the array . alternatively or additionally , it is the microscope subsystem 32 and / or optical sensing device 42 that is moved to scan the array . the apparatus may also include a z stage or focus stage 46 under control of the system processor 23 to provide displacement of the microscope subsystem in the z plane for focusing . the microscope subsystem 32 can further include a motorized objective turret 44 for selection of objectives to view the array . in other embodiments , the array is scanned in a manner similar to that of electronic scanners , where an “ eye ” picks up images and digitalizes it as the “ eye ” is moved along , directly or indirectly , to the surface of the array . one purpose of the apparatus 10 is for the unattended automatic scanning of prepared arrays for the detection of samples in the array . the apparatus 10 can then automatically locate and assign coordinates to the samples in the array . a number of stains can be used , if desired , to distinguish the samples from the background . thus , in one aspect , the samples can be prepared with a reagent to obtain a colored insoluble precipitate . the apparatus of the present invention being used to detect this precipitate as a sample . other means of distinguishing samples from background include , but are not limited to texture and density analysis . methods and apparatus of automated image analysis and detection of objects of interest , e . g ., samples on slides , are provided in u . s . patent application ser . no . 09 / 344 , 308 , filed jun . 24 , 1999 ; u . s . patent application ser . no . 09 / 495 , 461 , filed feb . 1 , 2000 ; and u . s . patent application ser . no . 09 / 616 , 817 , filed jul . 12 , 2002 , all of which are explicitly incorporated by reference in their entirety . during operation of the apparatus 10 , a user can mount the prepared arrays onto the input hopper 16 . the hopper 16 may utilize an automatic feed mechanism 37 . the user can then specify the size , shape and location of the area to be scanned or , alternatively , the system can automatically scan the array . the operator can command the apparatus to begin automated scanning of the array through a graphical user interface . a bar code label can be affixed to the array or array carrier and read by a bar code reader 33 during this loading operation . the apparatus stores an image of the array for later analysis or review by a user . all results and images can be stored to a storage device 21 such as a removable hard drive , dat tape , local hard drive , optical disk , or transmitted to a remote site for review or storage . the stored images for each slide can be viewed in a mosaic of images for further review . the following examples are provided to illustrate the practice of the instant invention and in no way limit the scope of the invention . in evaluating the experimental values for δs , table 1 below shows the range of parameter values that can produce satisfactory performance using a method of the invention . the table also summarizes the meaning and use of each of the parameters . tolerances liberal conservative meaning use δ 1 0 . 20 0 . 15 mean deviation of decide whether neighboring points to use centroid from the local as center lattice δ 2 0 . 30 0 . 10 fractional deviation decide whether of local lattice to use centroid vectors from best as center lattice vectors δ 3 0 . 25 0 . 15 deviation of one decide whether neighbor to nearby to assign label grid point to neighbor the method described above performed satisfactorily on all datasets considered . the arrays of fig1 and 4 show the centroids in the troublesome regions of each figure , i . e ., fig2 , 5 , and 6 , received labels that depended strongly on the parameters used and , therefore , served to define the range of reliable values shown in table 1 . in evaluating the method of the invention , it is useful to introduce three complementary ways of visualizing the results obtained from practicing the method of the invention . the first is the finished version of the lattices generated during the diffusion phase . fig1 a and 11c show this view of the labeled dataset from the array of fig1 . fig1 a is generated using conservative values of the tuning parameters δ while fig1 c uses liberal values . fig1 b and 11d show labeled close - ups of the bottom left corner corresponding to fig3 of the array of fig1 . for legibility , only the labels suspicious ( susp ), ambiguous ( ambg ) and unlabeled ( unlb ) are included in the figures . using conservative values for the parameters tend to label fewer points and result in one unlabeled centroid , while using liberal values tend to overlabel and result in six ambiguously labeled points . the lattice visualization shows each edge ( the line segment connecting two adjacent points in a row or column ) as represented in six different local lattices . these lattices are well aligned if the six line segments appear as a single line segment . small misalignments cause the line segment to appear thicker , and large misalignments produce shadows or completely separate traces . as a result , this way of displaying the data can explain any difficulties encountered by the algorithm . the “ ambiguous ” labels are due to the very slanted local lattice clearly visible in the liberal labeling program . this lattice does not show up in the conservative labeling program since its center is labeled suspicious and , therefore , is not used to label nearby points according to its local lattice . the second visualization of the results obtained from practicing the method of the invention is shown in fig1 a - 12d . fig1 a and 12c show the results of the conservative and the liberal labeling for the dataset of fig1 . this view shows the grid generated by connecting each centroid to the adjacent centroids in its assigned row and column . this visualization displays the global structure resulting from the assignment of coordinates to centroids . the rows and columns can be easily discerned and deviations from a regular grid shape stand out . irregularities in the grid pattern identify centroids with questionable assignments . for example , fig1 b shows the centroid left unlabeled when using conservative parameter values . the centroid is seen to lie at a “ fault line .” when using liberal values , fig1 d shows a shift by about half a grid spacing for the grid to the left relative to the grid line on the right . in this situation , it is better for the method to focus the attention of the user to this region than to come up with purported labels . both extremes of parameter values fulfill this goal of pointing out problem areas . the third visualization for this same dataset is shown in fig1 a - 13d . this graphical representation displays the lines that are the least squares fit to the centroids in each row and in each column . these lines summarize the global structure that has been assembled by the methods of the invention . any nonlinear trends or deviations within a row or column are clearly visible within this representation and show the variation of local structures relative to the global structure . the lone pair of centroids in the 6 th row from the bottom are very easy to spot in this view . in retrospect , it is also easy to spot these two points in the grid representation of fig1 a - 12d and represent another reason to call in a human operator . the “ fault line ” of fig1 a - 12d is also evident here in fig1 a - 13d . the least squares lines in this representation can be used to extend the global structure into sparse regions and enable the assignment of row and column indices to centroids that were left unlabeled by the local structure method . the grid can also be extended across empty regions to connect isolated groups of centroids . the closeness of a centroid to the nearest grid intersection can be used as a fourth measure of the confidence in its row and column assignment . the row and column assignments of centroids that are further from an intersection of the grid may be questionable . using this measure would enable the method to perform further consistency checks thereby resolving some ambiguities and identifying others . all three visual representations make it easy to spot the missing rows and columns . the grid and the lattice representations make clearly visible any misalignment between two intentionally separated regions . fig1 a - 11d show how a region boundary projects the local structure of its region into the missing row or column . a misalignment between the regions will appear as a series of misalignments between the local lattices centered on opposite sides of the missing row or column . thus , the local structure for the boundary centroids spans the missing row or column and resolves the offset between the regions . the sensitivity to parameter values is further illustrated by considering the labeling for the dataset from fig4 shown in fig1 a - 14f ( local lattice view ), fig1 a - 15f ( grid view ) and fig1 a - 16f ( lines view ). both the conservative and the liberal values for the parameters focus the operator attention on the problem area in the bottom left corner . conservative parameter settings leave many points unlabeled , while liberal parameter settings label many points ambiguous . the best results are obtained by taking a conservative approach to allowing centers by keeping the conservative setting δ 1 = 0 . 15 , while allowing liberal labeling of neighbors from approved centers δ 3 = 0 . 25 . the third labeling in fig1 a - 14f , fig1 a - 15f and fig1 a - 16f show the results of the mixed setting δ 1 = 0 . 15 , δ 2 = 0 . 30 , and δ 3 = 0 . 25 . this labeling shows that the culprit is the point in the second column which lies a little too far above the fourth row from the bottom .