QUANTITATIVE DENTAL CARIES DETECTION SYSTEM AND METHOD

Abstract: A caries detection system (20) includes a method for quantifying a probability of lesions existing in tissues. Digital x-ray images (25) are segmented in consideration of segmentation parameters for different digital image (28) sources. The digital images are then processed to determine inputs for a neural network.

Description: QUANTITATIVE DENTAL CARIES DETECTION SYSTEM AND METHOD CROSS-REFERENCE TO RELATED APPLICATION This application is a continuation-in-part of U.S. patent application serial no. 08/513,472, filed August 10, 1995, entitled QUANTITATIVE DENTAL CARIES DETECTION SYSTEM AND METHOD, by Douglas C. Yoon, et al. BACKGROUND OF THE INVENTION Field of the Invention The present invention relates to a system and method for the digital detection of tissue lesions and, more particularly, pertains to a system and method for quantifying a probability of lesions existing in tissues. Description of the Related Art According to the Journal of the American Dental Association , Volume 108, May 1984, page 755, dentists fail to detect carious lesions in teeth a significant fraction of the time (up to 40%) . Healthy teeth are misdiagnosed a significant fraction of the time as well (up to 20%) . In part, this problem is due to the fact that dentists are unable to directly view carious lesions on proximal surfaces, i.e., between the teeth. The human eye is an imperfect tool for visually analyzing dental x-rays because of its tendency to smooth out intensity gradients. Furthermore, substantial variations in dental x-ray images are attributable to variations in film type, exposure level, tooth structure and shape, and location and extent of lesions. Accordingly, an object of the present invention is to provide a tool for clinicians which reliably quantifies the probability of a lesion being present in tissues, an "electronic second opinion." Another object is to provide a digital imaging method for quantifying a probability of lesions existing in tissues. Another object is to provide a quantitative method which employs segmentation techniques to identify boundaries between adjacent tissues under analysis. Another object is to provide a quantitative method which extracts and derives statistics from digital imagery in consideration of segmentation boundaries. Another object is to provide a quantitative method wherein neural network techniques are applied to analyze feature statistics derived from digital images of tissues to detect and classify lesions within the tissues. Another object is to develop a data base of lesions in relevant tissues and to optimally employ such a data base for training a neural network. SUMMARY OF THE INVENTION In accordance with a specific illustrative embodiment of the present invention, a method for quantifying a probability of lesions existing in tissues includes the steps of: processing an image of at least two adjacent tissues to establish segments of the image; processing portions of the image within each segment to identify features within the tissues; processing the image to generate values for parameters (hereinafter "feature statistics") pertaining to the features; and processing the feature statistics to determine probabilities of the identified features being lesions. In a broader aspect of the present invention, a method for quantifying a probability of lesions existing in tissues includes the steps of: providing feature information for image segments corresponding to at least two adjacent tissues; and processing the feature information to generate feature statistics for subsequent processing by a neural network. In a further aspect of the present invention, a system for the digital detection of dental lesions includes a source of digitized x-ray images of teeth, a processor, and a display. The images have variable intensities which are analyzed to identify outer surfaces and interfaces of the imaged teeth. The processor employs the identified outer surface and interface to identify significant changes in intensity along successive space lines extending generally parallel to the tooth surface and generally parallel to the interface between the enamel and the dentine. The processor is additionally programmed to compare the variations in intensity with stored data relating to known dental lesions or caries and determined by such comparison the probability of such lesions being present. The display provideε visible indicia corresponding to the locations of probable lesions. DESCRIPTION OF THE DRAWINGS Other objects, features and advantages of the invention will become readily apparent upon reference to the following detailed description when considered in conjunction with the accompanying drawings, in which like reference numerals designate like parts throughout the figures thereof, and wherein: FIG. 1 is a perspective view of the quantitative dental caries detection system of the present invention; FIG. 2 is a photographic board of an enlarged front view of the display of FIG. 1; FIG. 3A is a flowchart of an automatic image segmentation aspect of a method of the present invention; FIG. 3B is a flowchart of a feature extraction aspect of a method of the present invention; FIG. 3C is a flowchart of a feature statistics aspect of a method of the present invention; FIG. 3D is a flowchart of a classifier aspect of a method of the present invention; FIG. 4 illustrates digital image processing employing a gradient technique according to the present invention; FIG. 5 compares the performance of the quantitative dental caries detection system for finding dentinal lesions in molars using film images with the performance of dentists and additionally illustrates by the shaded region plus and minus one standard deviation about the mean of the dentists' responses; FIG. 6 compares the performance of the quantitative dental caries detection system for finding dentinal lesions in molars using sharpened direct digital images with the performance of dentists and additionally illustrates plus and minus one standard deviation about the mean of the dentists' responses; FIG. 7 is an enlarged view of a portion of the plot shown in FIG. 5 and provides a quantitative comparison between the detection system of the present invention and the mean response of dentists; FIG. 8 is an enlarged view of a portion of the plot shown in FIG. 6 and provides a quantitative comparison between the detection system of the present invention and the mean response of dentists. FIG. 9 compares the performance of the quantitative dental caries detection system for finding dentinal lesions in premolars with the performance of dentists and additionally illustrates plus and minus one standard deviation about the mean of the dentists' responses; FIG. 10 compares the performance of the quantitative dental caries detection system for finding dentinal lesions in canines with the performance of dentists and additionally illustrates plus and minus one standard deviation about the mean of the dentists' responses; and FIG. 11 compares the performance of the quantitative dental caries detection system for finding dentinal lesions in incisors with the performance of dentists and additionally illustrates plus and minus one standard deviation about the mean of the dentists' responses. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS FIG. 1 is a perspective view of a quantitative dental caries detection system 20 according to the present invention. An x-ray source 22 and an x-ray sensor 24 are employed to produce two dimensional x-ray images of teeth 26. The x-ray sensor 24 may comprise a charge coupled device (CCD) x-ray sensor. A digitizer 28 receives the analog output of the CCD and converts it to a digital format for receipt by a computer 32. The sensor 24 should provide at least 8-10 line pairs/mm resolution. The detection system 20 additionally includes a computer 32, a monitor 34 and a printer 36. Digitized x-ray image information is provided from the digitizer 28 to the computer 32, for example, via an interface cable 38. Alternately, an x-ray film image may be input to the computer 32 by scanning and digitizing a film radiograph 25 with a commercially available film scanner 27 such as the Nikon Coolscan system. The computer 32 includes memory devices as well as a disk drive 40. A storage device such as diskette 42 provides executable software to the computer 32. Many aspects of the quantitative method of the present invention may be embodied in the form of software which is stored on a memory device such as the diskette 42 and executed by a processor such as the computer 32. The detection system 20 additionally includes a monitor interface cable 44 which electrically connects the computer 32 and the monitor 34. A printer interface cable 46 similarly interconnects the computer 32 with the printer 36. The monitor 34 includes a display 48 and the printer 36 generates a corresponding printout 50. FIG. 2 is an enlarged front view of the display 48 shown in FIG. 1. The display 48 is preferably partitioned into a plurality of display portions including a digitized image portion 52, an intensity variation plot 54, a probability graph 56, a patient information portion 58 and an analysis notes portion 60. Generally, the quantitative dental caries detection system 20 processes acquired digital images and identifies particular segments of these images by determining outer boundaries of teeth and the dentinoenamel junction (DEJ) 62. The detection system 20 processes the digitized images in consideration of the foregoing segmentation and employs trained network weights to quantify probabilities of lesions existing in the various segments of the radiographed teeth. As one of the outputs of the process, FIG. 2 shows a first lesion boundary 64 and a second lesion boundary 66 overlayed on the digitized image portion 52. The first lesion boundary 64 and the second lesion boundary 66 both extend, from left to right, from the outer boundary of the tooth's enamel through the dentinoenamel junction 62 and into the dentine of the tooth. The intensity variation plot 54 and the probability graph 56 will be explained in greater detail with reference to FIG. 4. FIG. 3A is a flowchart of an automatic image segmentation aspect of the method of the present invention. Software resident within the computer 32 receives and processes the digitized image at 70. If the input image is a scanned film radiograph, such as one produced with a Nikon Coolscan film scanner, execution of the image segmentation algorithm is directed toward block 72. If the input image is a direct digital radiograph (as produced, for example, by equipment from Schick Technologies, Inc.) execution of the image segmentation algorithm is directed toward executable block 74. Image segmentation is automatically implemented using a gradient method to identify the outer boundaries of the teeth and their dentinoenamel junctions. A key feature of the invention is the employment of different sets of segmentation parameters in the segmentation algorithm which take into account the varying degrees of image sharpness or clarity as between scanned film radiographs, unenhanced direct digital radiographs, and enhanced direct digital videographs. Tissue boundaries and junctions between adjacent tissues are preferably determined by image processing based on polar coordinates, exploiting the substantially radially symmetric structure of the tooth, as is best illustrated in FIG. 4. The gradient method of the present invention employs a "sliding" two dimensional matrix of pixel intensities comprising £ x 2*ά elements. Generally, the tissue boundaries and interfaces between tissues are found by identifying the maximum gradients along radial vectors The segmentation parameters mentioned above include: £: The length of the gradient matrix of pixels intensities, d: Half of the width of the gradient matrix of pixels intensities. α: An angle defined by a vector along the length of the gradient matrix of pixel intensities and by the radial vector (a.k.a., matrix orientation angle) . Δθ: The angular difference between successive radial vectors. Further with regard to FIG. 3A, decisional diamond 76 directs execution of the image segmentation algorithm depending upon whether a direct digital radiograph is to be image enhanced. If yes, a Fast Fourier Transform (FFT) based deconvolution using a laboratory measured point spread function is implemented at executable block 78. The foregoing deconvolution sharpens the direct digital radiograph and has been found to improve performance of the image segmentation algorithm. As discussed above, the segmentation parameters vary depending upon the source of the digital image. Film image segmentation parameters are selected at executable block 80 if the source of the digital image is a scanned film radiograph. Unenhanced digital image segmentation parameters are selected at executable block 82 if the source of the digital image is an unenhanced direct SUBSTITUTESHEET(RULE23) digital radiograph. Enhanced digital image segmentation parameters are selected at executable block 84 if the source of the digital image is an enhanced direct digital radiograph. The segmentation parameters are varied to optimize the image segmentation algorithm for different digital image sources which are provided in varying degrees of image sharpness or clarity. At executable block 86, an automatic image segmentation algorithm using a gradient method processes the digital image in consideration of the segmentation parameters selected for that particular image. The following equation provides for the calculation of the gradient: d d gradient « A j ^ ± _ B± ω where A, and B, are the sums of the pixel intensities along the i rt strips (FIG. 4) and ω, is a weight factor which, for example, may vary linearly from 1 to 0 as i varies from 1 to d. The location of the relevant tissue boundary is where the highest gradient iε found. This εearch iε performed over a user defined range of angleε θ,. about a tooth centroid 98 (FIG. 4) . In turn, for each θ, a εearch iε performed over a range of gradient matrix orientation angles α, and in turn for each α, a search is performed along the corresponding radial vector r to find Z mΑX , which iε the range along vector r at which the gradient iε maximized. ^m ^ andθ^. are the coordinates of points along the tissue boundary. Further with regard to the segmentation parameters, the strip length (£) , strip thickness (d) and matrix orientation angles (α,) are varied as necessary to accommodate differences in εharpneεε or clarity aεεociated with the variouε εourceε of digital imageε. The matrix orientation angle (α,) is preferably varied between 60° and 120° at 10° increments or finer and the angle (Δθ) is preferably εet at approximately 1°. The εubject matter of the preεent invention additionally contemplates an image segmentation algorithm which automatically or adaptively adjustε the foregoing parameterε during execution. FIG. 3B iε a flowchart of a feature extraction aεpect of the method of the preεent invention. Generally, the method of the preεent invention next employε the segmentation resultε to aεεiεt in the identification of candidate leεionε within the εubject tissue. In the case of teeth aε the subject tisεueε, if the boundaries identified by the image segmentation algorithm are found at decisional diamond 90 to be acceptable to the dentist, the feature extraction algorithm advances to executable block 92. Otherwiεe, the dentiεt manually traceε the outer boundarieε of the tooth and the dentinoenamel junction at block 94. Generally, the εyεtem and method of the preεent invention addreεε the problem of deriving coherent denεity featureε from intenεity variation data. A preferred method partitionε the radiograph (alεo commonly referred to aε an "x-ray image") into portionε depending upon the reεults of the image segmentation calculations. According to a preferred method and as illustrated in FIG. 4, the outer boundary 96 and the dentinoenamel junction 62 are then used to further partition the resulting enamel and dentine segmentε into substantially parallel stripε or traces at succeεεively deeper levels into the tooth which are later used for the feature extraction aspect of the present invention as discuεεed infra. In operation, a uεer εelectε a portion of the tooth by, for example, moving a εcreen curεor to a deεired centroid 98 and clicking a uεer input device such aε mouse. An angular range from r to ∑ is similarly selected by movement of the mouse from the centroid 98 toward the tooth surfaces of interest. Further with reference to FIG. 3B, pixel intensitieε I (x) , where x represents the distance along the trace relative to the beginning of the trace, are calculated for a series of points along each trace at executable block 100. The feature extraction algorithm next calculates intensity variations V(x) along each trace baεed upon high pass filtering techniques. Such techniqueε correct for the effect of tooth geometry on the x-ray image and enhance the ability to identify local variationε in intensity. A preferred digital high pasε filter embodieε the function where L > H and where L and H repreεent diεtanceε in pixel unitε from x t along the traceε. A broad length average iε provided by calculating an intensity average for points between x ( -L and x t +L along a trace. A narrow length average is provided by calculating an intenεity average between x t -H and x t +H along a trace. The high paεε filter function V (x t ) repreεentε the difference between the broad length average in intenεity and the narrow length average in intenεity at x t . Appropriate εcale factorε may be employed in the foregoing equation to relate trace length unitε (which vary from trace to trace) to pixel unitε. It should additionally be underεtood that the foregoing equation iε an approximation of a mathematical integral and that other filtering techniqueε may be employed. Aε εhown in FIG. 2, intenεity variationε along each trace may be plotted with dark featureε appearing aε bumpε in the plotε. After the high paεε filtering technique at executable block 102 iε completed, all dark featureε (candidate leεionε) along each trace are located and their lengthε and magnitudeε are determined at executable block 104. More generally, the segments are divided into families of traces within the outer boundary of the tooth. The families of traceε are not neceεεarily oriented εubεtantially parallel to the DEJ; appropriate coordinate tranεformation algorithmε may be employed to compensate for particular orientations of the traces. It should be noted that the present invention additionally contemplates the application of alternative two-dimenεional high paεε filtering techniqueε, εuch as unsharp maεking, wherein the εegments are not necesεarily divided into εtrips or traces. Although more computationally demanding, εuch an alternative two dimenεional technique would be leεε εenεitive to εegmentation errorε and, more εpecifically, to errorε in the DEJ calculation. FIG. 3C iε a flowchart of a feature εtatistics aspect of the method of the present invention. The feature statistics algorithm generally functions to identify the best linear alignments of dark featureε in both the enamel and the dentine and to calculate the values of a limited number of parameters or inputε for εubεequent proceεεing, preferably by a neural network. The traces shown in FIG. 4 are again employed to εearch for the beεt linear alignment of dark featureε. The εearch is preferably based upon a linear regresεion using the size and location of featureε in each trace. The alignment and depth of featureε are evaluated by two metricε, ε,(N) and A,(N) . ε,(N) repreεentε the εcaled alignment error and A,(N) repreεents the scaled mean feature magnitude. ε s (N) and A„(N) are associated with a candidate set of features from the firεt N traceε, where one feature iε εelected from each trace. Aε will be deεcribed later, ε s (N) is uεed to find the beεt linear alignment of featureε found in the trace and A S (N) iε uεed to determine the depth of penetration into the tooth of the featureε. ε (N) and A g (N) are calculated by firεt computing the beεt fit line chrough the candidate set of N features through standard linear regresεion. Alεo, the magnitude of the εelected feature in each trace is computed (e.g., as the area under the bumpε in the intenεity variation plot 54 of FIG. 2) . From theεe valueε the mean feature magnitude, A(N) , iε computed. Next, the RMS deviation aεεociated with the poεition of the featureε ( featurc ) anc the beεt fit line (X fit )through the featureε iε computed aε Finally the εcaled error which takeε into account the mean magnitude of the featureε, is given by ε s (N) = ε(N) (l-exp(-A(N))) and the scaled magnitude is given by The present invention additionally contemplates other techniques for εcaling £ g (N) andj^ / j j) in conεideration of the mutual biaεing effect of theεe metricε upon each other. The area term iε uεed to determine the end of a lesion. The error term £ f (N) is used to determine the beεt alignment. Feature characterization iε dependent upon the scaling of £,(N) and A β (N) . For example, weighting the area term by the error term helpε prevent the selection of large but randomly positioned dark features aε might occur in an image with large- εcale mottling. Similarly, weighting the error by the mean area helpε prevent the εelection of εmall but aligned featureε aε might occur by chance in a noiεy image. The following diεcussion illuεtrates how the metrics £ s (N)andA s (N) are used to find the location and depth of a candidate lesion. As shown in FIG. 4, the enamel εegment includes a total of ten traces between and including the outer boundary 96 and the DEJ 62. The deeper the leεion penetrateε into the tooth, the more proceεsing iε required to determine the beεt linear alignment by linear regreεεion techniques. For example, if two candidate dark features are found in each of the outermost three traceε, eight different calculationε (2 3 ) are required for an exhaustive search to find the beεt fit within thoεe three traceε. Aε εuch, a key feature of the present invention is the development of a procedure for automatically stopping the calculationε in the enamel εegment after a determination is made that the lesion has ended. In a preferred embodiment of the feature statistics algorithm, a coherent feature is searched for in the first three traces starting at the outer boundary 96. When the best fit exhibiting the greateεt degree of colinearity is identified by selecting the combination of features with the lowest εcaled error, ε,(N) , thiε value and the aεεociated εcaled feature magnitude, A S (N) , are εtored. Other information aεεociated with thiε combination of features is alεo εtored. Aε shown in FIG. 3C, executable block 106 is again implemented but now proceεεes the intensity variation information for the first four traceε. Similarly, the loweεt εcaled error, the scaled feature magnitude, and information asεociated with the combination of featureε are εtored. With the addition of each trace to the calculation of executable block 106, a new scaled feature magnitude is calculated and compared against the immediately preceding calculation. When a decreaεe in εucceεεive εcaled magnitudeε exceedε a predetermined threshold value, the feature statiεtics algorithm at decisional diamond 108 redirects execution of the algorithm to executable block 110. When the threshold iε exceeded, a εufficiently large decreaεe in the scaled magnitude (as compared to the previous scaled magnitude or, preferably, to a running average of all previous scaled magnitudes) has occurred indicating an end in the feature. If the threεhold iε not exceeded by the time n=N, thiε indicateε that the feature haε reached the DEJ and the algorithm proceeds to executable block 110. A search for the best linear alignment of dark features in the dentine is preferably a simplified calculation comprising a single iteration wherein the intensitieε along the firεt five traces of the dentine are procesεed. Additionally, it haε been found that there iε little value to proceεεing image traceε greater than halfway through the dentine. In a preferred embodiment of the preεent invention, only εix parameterε or feature εtatiεticε are provided as inputs to a neural network. FIG. 3C showε that the feature εtatiεtics algorithm asεignε an enamel penetration depth at executable block 112. Additionally, a εcaled mean enamel feature magnitude is calculated at executable block 114 in the traces between the outer boundary 96 and the trace corresponding to the enamel penetration depth aε eεtabliεhed at executable block 112. The εcaled enamel feature alignment error aε diεcuεεed above iε alεo εent at executable block 116 to the neural network as an input. Statisticε pertaining to the dentine are alεo provided to the neural network. At executable block 118, the εcaled mean dentine feature magnitude iε calculated and provided to the neural network. A εcaled dentinal feature alignment error from the foregoing regreεεion analyεiε iε alεo εent at executable block 120 to the neural network. Additionally, a degree of colinearity between enamel featureε and dentinal featureε iε calculated and provided to the neural network at executable block 122. The degree of colinearity between enamel and dentinal featureε iε conceptionally illuεtrated in the intenεity variation plot 54 of FIG. 2 by the enamel linear regression line 124 and the dentine linear regression line 126. The degree of colinearity between enamel and dentinal features iε calculated as the average distance, along each dentinal trace between line 126 and the projection of line 124 into the dentine. FIG. 3D iε a flowchart of a classifier aspect of the method of the present invention. In a preferred embodiment, a data base of laboratory exam inputs is aεεembled at block 130. Radiographic parameterε include expoεure levelε, film speeds, digital senεor characteriεticε and tooth overlap geometrieε. An extenεive data εet iε developed by croεε sectional analysiε of teeth uεed to determine the abεolute extent of leεions. For example, 320 tooth surfaces are provided by four tooth types. (incisor, canine, premolar, molar), four lesion depths (clean, >% enamel, full enamel, > dentine) and 20 examples of each type and depth based on statistical analysiε. A simplified clasεification or scoring syεtem iε εet forth below: 0: clean tooth 1: leεions penetrating lesε than halfway into the enamel 2: leεionε penetrating more than halfway into the enamel 3: leεionε penetrating into dentine As shown in FIG. 3D, training of the neural network by back-propagation is effected at executable block 132. The resulting trained network weights at block 134 and the inputs from the feature statiεtics algorithm are provided to a neural network classifier 136. In a preferred embodiment, the neural network clasεifier 136 compriεeε a fully connected, three-layer, feed forward, network uεing trained network weightε to recognize carious lesions. If the probability that any lesion exiεts exceeds a predetermined detection threshold, the system 20 indicates that a lesion is present in the tooth as shown in the probability graph 56 of FIG. 2. If deciεional diamond " 138 reεults in a negative determination, the syεtem indicates that no suspicious features were found. The neural net is trained to generate the probability of a lesion being present in the selected region on the tooth. This probability (p) could be either the probability of any lesion or the probability of a lesion penetrating the dentine (distinct neural nets provide each probability) . The probability (p) is calculated as shown below: where : Xi — 1 9 th input to neural net; i - 1, 2 ... N; N = number of features; ** j' *ji are the network weights; V" , V- are the network threεholdε; and σ (y) = — ■—r- is the neural node tranεfer 1 + ex (- ) function where (y) iε the εummation term within the bracketε in the above probability equation. The weightε and threεholdε are eεtabliεhed by training on a database which is preferably extensive. The claεεifier algorithmε are εtructured aε εhown in FIG. 3D because it is vitally important for a dentist to treat a tooth which has lesions in its dentine. The detection threεhold of decisional diamond 140 may be varied appropriately in view of the foregoing. FIG. 5 compares the performance of the quantitative dental caries detection syεtem 20 on film imageε of molarε with the performance of dentiεtε. The performance compariεon waε baεed on a εet of radiographic imageε taken with standard film radiography (Kodak D film) and was determined by standard receiver operator characteristic (ROC) analysiε. An ROC analyεiε measures the tradeoff between the false positive identification rate (in thiε caεe, the percentage of teeth free of carieε in the dentine which are incorrectly claεεified aε having a lesion in the dentine) versus the true positive identification rate (in this caεe, the percentage of teeth with a leεion in the dentine which are correctly identified) . An ROC curve is generated by meaεuring the falεe poεitive and true poεitive rateε for a range of detection confidence levelε. The greater the area under the ROC curve, the better the overall performance. Obεerved data shows that the Logicon Dental Detection System (LDDS) exemplifying the preεent invention outperformed a team of eleven trained dentiεtε in detecting interproximal leεionε which have penetrated into the dentine in a test for four different categories of lesions on 80 surfaces. FIG. 5 additionally illustrates plus and minus one standard deviation about mean of the dentists' responεeε. The εhaded region repreεents the range encompasεing approximately 68% (pluε and minuε one εtandard deviation about the mean) of the dentiεtε' individual reεponεeε. The larger, hollow dotε correεpond to five deciεion threεholdε along an average reεponεe trace for the teεted dentists. In comparison, the LDDS outperforms the dentists' mean reεponεe aε the majority of the individual reεponεeε for the dentiεtε are below the LDDS curve. FIG. 6 compares the performance of the quantitative dental caries detection syεtem 20 on sharpened direct digital images of molars with the performance of dentistε and, aε in FIG. 5, additionally illuεtrates plus and minus one standard deviation about the mean of the dentistε' responses. FIG. 7 is an enlarged view of a portion of the plot shown in FIG. 5 and provideε a quantitative compariεon between the detection εyεtem 20 and the mean reεponεe of dentists in the more clinically relevant lower end of the ROC. The 30-50% reduction in the falεe poεitive identification rate at the lower end of the ROC demonεtrates the diagnostic advantage provided by the detection system 20 because it is in this area of the ROC curve that difficult decisions are presented to the dentist. Notwithstanding the scattered individual responses of the dentistε (FIG. 5) , the quantitative dental carieε detection system 20 outperformε the average reεponεe of the tested dentists in the majority of caseε and iε conεiεtent in itε evaluation, unlike dentiεtε' responses. In fact, the detection system 20 generates a εingle, conεiεtent response when run in "automatic mode" (i.e., all tisεue boundaries determined by the detection system 20) . Consiεtent responses are provided by the detection syεtem 20 when uεed correctly, keying in on the correct anatomical tissue landmarks. FIG. 8 is an enlarged view of a portion of the plot εhown in FIG. 6 and provideε a quantitative compariεon between the detection εyεtem 20 and the mean reεponεe of dentiεtε. Aε with film, the detection εyεtem 20 reduceε the falεe poεitive identification rate for εharpened direct digital imageε of molarε at the critical lower end of the ROC thereby reducing the unneceεεary treatment of teeth. More εpecifically, FIG. 8 illuεtrateε that dentiεts identify approximately 62% of all dentinal lesionε at a falεe poεitive rate of approximately 16%. An LDDS interpretation of the εame enhanced direct digital radiographε identifieε the εame percentage of leεionε while reducing the falεe poεitive rate to approximately 7% (a 50% reduction) . Alternately, an LDDS interpretation of the εame enhanced direct digital image maintainε the εame falεe poεitive rate aε dentists while increasing the rate of lesion identification to 80% (a 33% increase).; In a fashion similar to FIG. 6, FIGS. 9, ^10 and 11 compare the performance of the quantitative dental caries detection syεtem 20 on εharpened direct digital imageε of premolars, canines and incisorε reεpectively, with the performance of dentiεts, additionally illuεtrating pluε and minuε one εtandard deviation about the mean of the dentiεt'ε reεponεeε. As with molars, LDDS equalε or exceeds the performance of dentistε on direct digital images of premolars, canineε and inciεorε. In concluεion, it iε to be underεtood that the foregoing detailed deεcription and the accompanying drawings illustrate the principles of the invention. However, various changes and modifications may be employed without departing from the εpirit and εcope of the invention. Thuε, by way of example and not of limitation, the εyεtem and method of the preεent invention may be employed to detect leεionε in other typeε of tiεεueε beεides teeth. For example, mammograms, chest x-rays, tiεεue characterization and cell characterization and other applicationε are contemplated. Accordingly, the preεent invention iε not limited to the εpecific form εhown in the drawingε and described in detail hereinabove.

Claims: WHAT IS CLAIMED IS: 1. A system for the digital detection of dental lesionε or carieε compriεing: a εource of digitized x-ray images of teeth, said imageε having variable image intenεitieε; means for identifying the outer surface of a tooth in a predetermined area, and for identifying the interface between the enamel and the dentine; meanε for identifying significant changes in intensity along successive spaced lines extending generally parallel to the tooth surface and generally parallel to the interface between the enamel and the dentine within the enamel, and within the dentine adjacent to and parallel to the interface; means for comparing the variations in intensity with stored data relating to known dental lesions or caries, and for determining by such comparison whether dental lesionε are preεent in the dentine, and the probable extent of εuch leεionε; and means for printing out both the variations in intensity along said succeεεive εpaced lines, showing the presence or absence of dental lesions in the enamel and/or dentine, and the extent of such lesions if they are present. 2. A syεtem for the digital detection of dental leεionε or caries comprising: a source of digitized x-ray images of teeth, said imageε having variable image intenεitieε; meanε for identifying the outer εurface of a tooth in a predetermined area, and for identifying the interface between the enamel and the dentine; meanε for identifying εignificant changes in intensity along successive spaced lines extending generally parallel to the tooth surface and generally parallel to the interface between the enamel and the dentine within the enamel, and within the dentine adjacent to and parallel to the interface; meanε for comparing the variations in intensity with stored data relating to known dental lesions or caries, and for determining by such comparison whether dental lesionε are present in the dentine, and the probable extent of εuch lesions; and means for displaying both the variations in intensity along said εucceεsive spaced lines, showing the presence or absence of dental lesionε in the enamel and/or dentine, and the extent of εuch lesions if they are present. 3. A method for the digital detection of dental lesions or caries comprising: obtaining digitized x-ray images of teeth, said images having variable image intensities; identifying the outer surface of a tooth in a predetermined area, in said digitized images and for identifying the interface between the enamel and the dentine; identifying significant changes in intensity along successive spaced lines extending generally parallel to the tooth surface and generally parallel for the interface between the enamel and the dentine within the enamel, and within the dentine adjacent to and parallel to the interface; comparing the variations in intensity with stored data relating to known dental lesions or caries, and for determining by such comparison whether dental lesions are present in the dentine, and the probable extent of εuch lesions; and displaying both the variations in intensity along said successive spaced lines, showing the presence or absence of dental lesions in the enamel and/or dentine, and the extent of such leεions if they are present. 4. A method for quantifying a probability of lesions existing in tissues, comprising the steps of: (a) processing an image of at least two adjacent tisεueε to determine a junction between εaid tiεεueε and an outer boundary of at leaεt one of εaid tiεεues, thereby establiεhing εegments of said image; (b) procesεing portionε of said image within each said segment to identify features within said tisεues; (c) procesεing εaid image to generate εtatiεticε pertaining to εaid featureε; and (d) proceεεing εaid feature εtatiεtics to determine probabilities of said features being lesions. 5. The method for quantifying a probability of lesions existing in tissues of claim 4 wherein said segmentation processing step comprises: employing a gradient method search to determine said outer boundary, said εegmentation processing step (a) being controlled by εegmentation parameters which are establiεhed in conεideration of an optimization of εaid gradient method εearch. 6. The method for quantifying a probability of leεionε exiεting in tiεsues of claim 4 wherein: said image has a degree of sharpneεs; and said εegmentation processing step (a) is controlled by segmentation parameterε which are established in consideration of said degree of sharpness of said image being proceεεed. 7. The method for quantifying a probability of leεionε exiεting in tiεεueε of claim 4 wherein: said image is a digital image with a degree of clarity; and further comprising a step; (e) of procesεing εaid image to improve εaid degree of clarity prior to performing εaid segmentation processing step (a) . 8. The method for quantifying a probability of lesions existing in tissues of claim 7 wherein said image clarity improvement processing step (e) compriseε: performing a FFT-baεed deconvolution on said image using a meaεured point spread function. 9. The method for quantifying a probability of lesions existing in tissues of claim 4 wherein: said portionε of εaid image of εaid feature extraction proceεsing εtep (b) are determined in conεideration of εaid junction and said outer boundary. 10. The method for quantifying a probability of lesions existing in tissues of claim 4 wherein said portions of said image of εaid feature extraction proceεsing step (b) comprise families of traces within εaid outer boundary. 11. The method for quantifying a probability of leεions existing in tissues of claim 4 wherein said portions of said image of said feature extraction procesεing εtep (b) comprise traces substantially parallel to said junction and εaid outer boundary. 12. The method for quantifying a probability of leεions existing in tissues of claim 11 wherein said feature extraction proceεsing step (b) further comprises the εteps of: determining pixel intensities along said traces; determining variations of said intensities along said traces; and proceεεing εaid intenεity variations to identify locations and magnitudes of said features along εaid traceε. 13. A method for quantifying a probability of leεions existing in tissues, comprising the steps of: (a) processing an image of at least two adjacent tisεues to establish segments of said image; (b) proceεεing portionε of εaid image within each εaid εegment to identify featureε within εaid tiεεueε, εaid feature extraction proceεεing εtep (b) further compriεing: determining pixel intenεitieε within εaid portionε; determining variations of said intensities within said portions; and procesεing εaid intenεity variationε to identify locationε and magnitudes of said features within said portions; (c) procesεing εaid image to generate εtatiεtics pertaining to said features, said feature statiεticε including best linear alignments of said features within each said segment; and (d) procesεing said feature statistics to determine probabilities of εaid features being lesionε. 14. The method for quantifying a probability of leεions existing in tissues of claim 13 wherein said feature statistics processing step (c) compriseε: performing linear regressions on said portions of said image to generate leaεt squares errors which are weighted by said magnitudes of said features. 15. The method for quantifying a probability of lesions existing in tissues of claim 13 wherein said best linear alignments of said feature εtatiεtics procesεing εtep (c) are determined differently as between said segment . 16. The method for quantifying a probability of leεionε existing in tissues of claim 13 wherein said feature statisticε further include a measure of colinearity between features. 17. A method for quantifying a probability of lesions existing in tissues, comprising the stepε of: (a) processing an image of at least two adjacent tiεεues to establiεh εegments of said image; (b) procesεing portions of said image within each said segment to identify features within said tissues; (c) procesεing εaid image to generate statistics pertaining to said features; and (d) processing said statistics with a neural network clasεifier to determine probabilities of said featureε being leεions. 18. The method for quantifying a probability of lesions existing in tissues of claim 17 wherein said neural network classifier comprises a fully connected, three layer, feed-forward network employing trained network weights to determine said probabilities of said features being lesions. 19. The method for quantifying a probability of lesions existing in tissues of claim 17 wherein said probabilities of said feature statistics processing step (d) compriεe: a probability of εaid features being lesions in all of εaid segments. 20. The method for quantifying a probability of lesions existing in tiεεueε of claim 17 wherein εaid probabilities of said feature statistics processing step (d) comprise: a probability of said features being lesions in a selected one of said segments. 21. A method for quantifying a probability of lesions existing in tissues, comprising the steps of: (a) procesεing an image of at least two adjacent tissues to establish segments of said image; (b) processing portions of said image within each said segment to identify features within said tissues; (c) processing said image to generate statistics pertaining to said features; and (d) procesεing said feature statiεticε to determine probabilitieε of said features being lesions. 22. A method for quantifying a probability of lesionε exiεting in tiεεueε, compriεing the steps of: (a) providing features of image segments corresponding to at leaεt two adjacent tiεεues; and (b) procesεing said features to generate feature statiεtics for εubεequent proceεεing by a neural network. 23. A εyεtem for quantifying a probability of lesions exiεting in tiεεueε, compriεing the εteps of: a memory device embodying a computer executable program for: (a) procesεing an image of at leaεt two adjacent tiεεues to establish segments of said image; (b) processing portions of said image within each εaid εegment to identify features within said tiεεueε; (c) proceεεing εaid image to generate statistics pertaining to said features; and (d) procesεing said feature statistics to determine probabilitieε of εaid features being lesionε. 24. The εyεtem for quantifying a probability of leεions existing in tissues of claim 23, further comprising: a printer for printing visible indicia of said probabilitieε.

Inventors: GAKENHEIMER DAVID C, MANUKIAN NARBIK, NEUHAUS JOSEPH A, WILENSKY GREGG D, YOON DOUGLAS C, GAKENHEIMER, DAVID, C., MANUKIAN, NARBIK, NEUHAUS, JOSEPH, A., WILENSKY, GREGG, D., YOON, DOUGLAS, C.
Applicants: LOGICON INC, LOGICON, INC.
Application year: 1996
Main classification: G06K9
IPC codes: A61B6, G06F19, G06T1, G06T7