Abstract:
A digital image processing method for a centralized multi-mode medical tongue image inspection system, comprising the steps of: forming at least one service unit ( 300 ) for performing tongue record ( 220 ) construction; forming a processing unit ( 320 ) for tongue record inspection ( 308 ); and providing communication links ( 330 ) between the service units and the processing unit.

Description:
FIELD OF THE INVENTION 
     The present invention relates generally to a medical imaging system and, in particular, to a longitudinal tongue image inspection system. 
     BACKGROUND OF THE INVENTION 
     One of the principal concepts of Chinese medicine is that there are correspondences among various parts of the body. In pulse diagnosis the pulse on the radial artery can be felt in three sections reflecting the energetic states of the upper, middle and lower parts of the body. In facial diagnosis the face is believed to reflect the condition of different organs. In tongue diagnosis the same general principal is applied. Certain parts of the tongue reflect the health of the other parts of the body, or certain internal organs. The beauty of tongue diagnosis lies in its simplicity and immediacy: whenever there is a complex disorder full of contradictions, examination of the tongue instantly clarifies the main pathological process (see “Tongue Diagnosis in Chinese Medicine,” by Giovanni Maciocia, Eastland Press, Seattle, 1996). 
     Tongue diagnosis is a vital instrument used in traditional Chinese medicine (TCM) both for assessing the current health of a patient and providing a basis for prognosis. It also informs the practitioner about the underlying strength or weakness of the patient&#39;s constitution. 
     For thousands of years, tongue diagnosis has played an indispensable role in the practice of traditional Chinese medicine. TCM practitioners rely on tongue diagnosis to differentiate one syndrome from another, and use variations in the tongue&#39;s color, texture, shape, and coating to evaluate a patient&#39;s condition. 
     In the practice of TCM, tongue diagnosis is accomplished by visual inspections. Visual inspection of the tongue offers many advantages: it is a non-invasive diagnosis method, is simple, and inexpensive. However, the current practice in TCM is mainly experience based or subjective. The quality of the visual inspection varies between individual practitioners. And although there are a few experts successfully diagnosing cancers based on inspection of the tongue, their skills are not easily transferable to other medical professionals. Their expertise is limited at qualitative descriptions, not to quantitative or mathematical formulations. 
     Furthermore, traditional visual inspection of the tongue does not provide traceable fidelity information on patients&#39; tongue appearance such as color, coating and texture, except subjective descriptions in writing. 
     Therefore, there is a need for creating a tongue diagnosis system that facilitates performing functionalities such as tongue image acquisition, recording, retrieval, analysis, detection, tele-inspection, remote access, and so on. 
     These and other aspects, objects, features and advantages of the present invention will be more clearly understood and appreciated from a review of the following detailed description of the preferred embodiments and appended claims, and by reference to the accompanying drawings. 
     SUMMARY OF THE INVENTION 
     Briefly, according to one aspect of the present invention a method for diagnosing diseases by inspecting images of an individual&#39;s tongue comprises forming a first image of the tongue and a portion of the individual&#39;s face. A second image of the tongue and the portion of the individual&#39;s face is formed. A color comparison of the portion of the individuals&#39; face and the second image of the individual&#39;s face in the first image is performed. The colors in the second image are matched to the colors in the first image based on the color comparison. At least one disease is diagnosed based on changes in the first tongue image and the second tongue image. 
     In another embodiment, a digital image processing method for a centralized multi-mode medical tongue image inspection system comprises forming at least one service unit for performing tongue record construction; forming a processing unit for tongue record inspection; and providing communication links between the service units and the processing unit. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the U.S. Patent and Trademark Office upon request and payment of the necessary fee. 
         FIG. 1  (Prior Art) is a graph illustration of a tongue image acquisition setup; 
         FIG. 2  is an illustration of the concept of a tongue record of the present invention; 
         FIG. 3A  is a flowchart illustrating information flow of the tongue inspection method of the present invention; 
         FIG. 3B  is an illustration of a centralized tongue image inspection system of the present invention; 
         FIG. 4  is a schematic diagram of a tongue record processing hardware system useful in practicing the present invention; 
         FIG. 5  is an illustration showing tongue image color calibration of the present invention; 
         FIGS. 6A and 6B  are illustrations of two graphs related to color distributions; 
         FIG. 7  is a flowchart illustrating a two-way communication system; 
         FIG. 8  is a flowchart illustrating two-way communication steps; 
         FIG. 9  is a graph illustrating tongue image inspection user interface of the present invention; and 
         FIG. 10  is an illustration showing a tongue. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, various aspects of the present invention will be described. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the present invention. However, it will also be apparent to one skilled in the art that the present invention may be practiced without the specific details presented herein. Furthermore, well-known features may be omitted or simplified in order not to obscure the present invention. 
     During a typical examination of a patient in TCM, the patient is asked to let the physician inspect the patient&#39;s tongue&#39;s physical properties. Equipped with an imaging device, the physician takes an image of the patient&#39;s tongue. The captured image data and some contextual information available prior to or after the image collection process constitute an image packet  206 , shown in  FIG. 2 . The contextual information will be referred to as metadata. Metadata is analogous to the image header data that accompanies many digital image files. 
     The image packet  206  comprises two sections: the pixel data  208  of an image that has been captured by the imaging device, and image specific metadata  210 . The image specific metadata  210  can be further refined into image specific collection data  212 , and inferred image specific data  216 . Image specific collection data  212  contains information such as the image index number, image capture rate, image capture time, image exposure level, and acquisition mode (with or without Munsell ColorChecker, see below). Inferred image specific data  216  includes location and description of identified abnormalities within the image, and any pathologies that have been diagnosed. This data can be obtained either from a physician or by automated methods. 
     The general metadata  204  contains such information as the date of the examination, the patient identification, the name or identification of the referring physician, the purpose of the examination, suspected abnormalities and/or detection, and any information pertinent to the image packet  206 . It can also include general image information such as image storage format (e.g., TIFF or JPEG), number of lines, and number of pixels per line. 
     Referring to  FIG. 2 , the image packet  206  and the general metadata  204  are combined to form a tongue record  220  suitable for diagnosis. 
     It will be understood and appreciated that the order and specific contents of the general metadata or image specific metadata may vary without changing the functionality of the examination bundle. 
     Referring now to  FIGS. 3A and 3B , an embodiment of the tongue image inspection system of the present invention will be described.  FIG. 3A  is a flowchart illustrating the tongue image inspection method of the present invention. In  FIG. 3B , a service unit  300  consists of an image acquisition step  302 , a record formation step  304 , and a communication step  305 . A tongue image  208  is captured in a tongue image acquisition step  302 . In a step of tongue record formation  304 , the image  208  is combined with image specific data  210  to form an image packet  206 . The image packet  206  is further combined with general metadata  204  and compressed to become a tongue record  220 . 
     The service unit  300  can be of a modular structure including hardware and software physically residing in a TMC practitioner&#39;s office, a family doctor&#39;s office, a nursing house, a patient&#39;s home, a remote site clinic, a medical kiosk, an examination room in a hospital, or in any locations that are able to accommodate such a modular and provide a certain type of data communication link  330  that is to be discussed later. 
     It is understood by the people skilled in the art that the service unit  300  can be of any forms of combination of an image capturing device and a stand-alone data-editing device. An image capture device can be an off-the-shelf consumer camera, a professional camera, a specially designed camera with data-editing capabilities, or a medical image scanner. A stand-alone data-editing device can be a personal computer, a hand-held computing machine, or a networked computing facility. The image capture device can be physically attached to the stand-alone data-editing through a USB link, an Ethernet cable, a serial communication cable, or a parallel communication cable. The image capture device can also be wirelessly connected to the stand-alone data-editing through. RF communication channel, infra-red, or microwave communication links. 
     Multiple image acquisition modes are employed in step  302  for the tongue image acquisition method of the present invention. One exemplary mode is to acquire tongue images together with a Munsell ColorChecker (see “A Color Rendition Chart”, by C. S. McCamy et al, Journal of Applied Photographic Engineering, Vol. 2, No. 3, pp. 95-99, 1976) embedded in the image. Since the color value of the test cells of the ColorChecker is known, the color of the tongue image can be calibrated computationally (see “A Novel Imaging System for Tongue Inspection”, by Yang Cai, IEEE Instrumentation and Measurement Technology Conference, Anchorage, Ak., USA, pp. 21-23, May 2002). 
     A second exemplary mode of image acquisition is to acquire a tongue image together with a Munsell ColorChecker embedded in the image only once (preferably the first time) for a patient. Subsequent acquisitions of tongue images for the same patient can be made without the use of the Munsell ColorChecker. 
     A third exemplary mode of image acquisition is to acquire tongue images without the use of the Munsell ColorChecker (calibration could be done by using a statistically satisfied skin color value for a particular population of patients). 
     For the first and second acquisition mode, an exemplary set up of a prior art (Cai&#39;s paper) is shown in  FIG. 1 . A ColorChecker plate  102  is placed near the patient&#39;s tongue  104 . The standard color values of all the colors (24 of them) are given for the ColorChecker  102 . Cai&#39;s method for calibrating the tongue image  104  is by manually clicking the four corners of the ColorChecker  102  in image  101  to get the measured values of those four colors in the captured image  101 . By using the given standard color values and the measured color values for the four corners, a transformation function can be derived (see Cai&#39;s paper). The derived transformation function is then applied to all the pixels in the image  101  so that the tongue image  102  can be color calibrated for proper visual inspection. The need of color calibration is mainly for color appearance consistency (consistent rendering) of tongue images taken at different times and under various illumination conditions. 
     As stated previously, in the second acquisition mode, for a patient, the ColorChecker is required when acquiring some images (e.g. a first tongue image). All the subsequent tongue images are taken without a ColorChecker. With the help of the ColorChecker, the first tongue image of this patient can be calibrated in the same manner as the tongue images acquired using the first acquisition mode. Subsequent images can be color calibrated using the first image that is calibrated already with a method called consistent color rendering to be discussed next. 
     Removing the requirement of acquiring images with a ColorChecker (in the second and third modes) allows the patient to take tongue pictures anytime and anywhere under a normal lighting condition. To still meet the requirement of appearance consistency for tongue images taken in the second and third modes, an operation of consistent rendering is applied to all the tongue images taken without a ColorChecker. In the second acquisition mode, the calibrated first tongue image is then used as a color reference image for subsequently acquired tongue images for the same patient. In the third acquisition mode, any one of the tongue images of a patient can be used a color reference image for the other tongue images for the same patient. 
     The operation of consistent color rendering for tongue images acquired with the second and third modes can be explained using an exemplary algorithm discussed next with the help of  FIG. 5 . 
     In  FIG. 5 , there are shown three tongue images of a person. Images  501  and  503  are the original images acquired at different times with different lighting conditions. It is noticeable that the color appearance is quite different for these two images, which may not be acceptable for diagnosing longitudinal changes. Color calibration (consistent color rendering) is thus needed to bring color appearances of these two images to close to each other. The idea is to use color information of a common area (such as patches  507  and  509 ) from both images to find the color difference. Then use the color difference information to calibrate either one of the images in the color space to make the color appearance closer to the other one. Denote an image by I t ,t∈[0,1, . . . T]. The subscript t signifies the time index for images. The parameter T is the number of images involved in calibration operation. For the current example, T=2. Similarly, denote a patch (such as patches  507  or  509 ) by p t ,t∈[0,1, . . . T] for I t . A reference image needs to be selected in order to calibrate other images to have similar color appearance close to the reference image. The reference image is selected arbitrarily or purposely and denoted by I r ,r∈[0,1, . . . T] . Accordingly, the patch associated with the reference image is denoted by P r . Since color images are to be dealt with, to further differentiate color channels (red, green and blue channels), a superscript is introduced to the defined notations. Therefore, a color image is expressed as I t   i ,t∈[0,1, . . . T],i∈[1,2,3]. An exemplary association of a superscript and a color channel could be I 1  for a red channel, I 2  for a green channel and I 3  for a blue channel. With the notations defined above, the calibration operation can be readily presented by a function ƒ(s,u,v,w):
 
 Ĩ   t   i =ƒ( I   t   i   ,p   r   i   ,p   t   i   ,c   i ), ∀ t≠r;t∈[ 1,2 , . . . T];r∈[ 1,2 , . . . T]   (1)
 
where Ĩ is a color calibrated image. An exemplary function ƒ( ) could be
 
ƒ( s,u,v,w )= s−w ·Φ( u,v )  (2)
 
where · is a scalar multiplication operator. Noted that w is a coefficient, 0≧w≧1 and it varies for different channels, that is, an adjustable coefficient by the user. Also noted that in Equation (2) s represents an image. The correct interpretation of Equation (2) is that every pixel value in image s will be offset by a quantity of w·Φ(u, v). It now boils down to determine the function Φ( ). As stated previously, the purpose of color calibration is to match the color appearance of one image to that of the other. Therefore, function Φ( ) should provide information of the difference between the images to Equation (2). A variety of ways can be used to construct a function Φ( ). Below is an exemplary Φ( ) function that uses small patches of a common area of the two images:
 
Φ( u,v )=ζ( u )−ζ( v )  (3)
 
where ζ( ) is a nonlinear function in this exemplary algorithm. A common area such as a portion ( 507  or  509 ) of the cheek is a good candidate to extract color difference features between two images. Noted that in practice, it is not necessary for patches  507  and  509  to have the same size or shape. The use of these patches is to provide the algorithm with color (red, green and blue) statistics for the images under processing. There are shown two graphs in  FIGS. 6A and 6B . Graph  601  displays three intensity distributions, for red, green and blue respectively, for all the pixels within patch  507  of image  501 . Graph  603  displays three intensity distributions, for red, green and blue respectively, for all the pixels within patch  509  of image  503 . The color intensity distributions of patch  507  are different from that of patch  509  due to the different lighting conditions for the two images. These color intensity distributions can be used in computing function ζ( ). For example, ζ( ) could be a function that finds the value that divides the area under the intensity distribution curve into two equal parts. ζ( ) also could be a function that finds a trimmed median value of a distribution. The reference patch u could be from any one of the images that acquired in mode  3 , or from the first image in mode  2 . The color calibration process may also be applied to images acquired in mode  1 . Using the algorithm described by Equation (3), the calibrated image of image  503  is obtained and shown in  FIG. 5  as image  505  that has a color appearance closer to that of image  501 . The skilled in the art should be able to use other previously published color calibration functions than Equation (2) for ƒ( ).
 
     Returning back to  FIG. 3A , steps related to data formation and communication will be explained. In the record formation step  304 , the acquisition mode is included in the image specific collection data  212 . The tongue record is uploaded from the image and data formation unit  300  through the communication step  306  to a processing unit  320  through a link  330 . The link  330  could be a wired link or a wireless link. The processing unit  320  could be part of a tongue record processor  402  as shown in  FIG. 4 . The image and data formation unit  300  also could be part of the tongue record processor  402 , in which case, the link  330  is virtually eliminated. In other cases, the tongue record processor  420  can be physically residing in a place proximity to the service unit  300 , or in a remote place separated from the unit  300 . In either situation, using a wired link is to connect service unit  300  through networks (LANs and/or WANs). 
     For a wireless link, the wireless communication protocol IEEE-802.11, or one of its successors, is implemented as a choice for this application. This is the standard wireless communications protocol and is the preferred one here. However, while this is preferred, it will be appreciated that this is not a requirement for the present invention, only a preferred operating situation. In wireless connection, service unit  300  may serve as nodes of a LAN or a WAN. 
     One embodiment of the tongue image inspection system structure of the present invention is a centralized inspection system and illustrated in  FIG. 3A  in which there are shown four exemplary tongue image and data formation units  300  and one processing unit  320 . These tongue image and data formation units  300  could be in places with minimum medical staffing and scattering over a large geographical area. This exemplary structure provides two-way remote messaging and networked communication channels  330 . In two-way communications, unit  300  sends tongue records  220  to unit  320  and receives diagnostic information and instructions from unit  320 . 
     An exemplary two-way communication system is shown in  FIG. 7 . In  FIG. 7 , service units I ( 710 ), through service unit W ( 712 ) concurrently capturing and transmitting images. These service units are represented by service unit  300 . There are two types of communication links in this exemplary two-way communication system. One of the types is a wireless communication link  700  that has a receiving end and a transmitting end. The other type is a network link  750  through which messages (tongue record, notification message, acknowledgement, or diagnostic message) can be exchanged. Noted that in  FIG. 3A , the data communication step  305  performs both receiving and transmitting data tasks, the data input step  306  performs receiving data task, and the data output step  309  performs transmitting data task. 
     For a wireless communication link,  FIG. 8  shows a communication path from the transmitting end to the receiving end (such as  720 - 728 ,  730 - 722 ,  724 - 728 , and  730 - 726 ).  FIG. 8  represents the steps that take place when a message  800  is transmitted. The message  800  could be the tongue record  220 , a notification message from medical staff members at the sites of unit  300 , a diagnostic message or an acknowledgment from process unit  320 . The communication path receives a message  800  in a step  802  of receiving end receives message from a sender. The message  800  is transmitted in a step  804  of transmitting end transmits message to receiving end. The receiving end receives the transmitted message in step  806  and routes the message to a user (service unit  300 ) in step  808 . 
     The transmitting and receiving message from the transmitting network (including  802  and  804 ) to the receiving network (including  806  and  808 ) is governed by a software platform, which runs on processing unit  740  shown in  FIG. 7 , to simplify the process of delivering messages to a variety of devices including any mobile computing devices. The software platform service can route and escalate notifications intelligently based on rules set up by the user to ensure “closed loop” communication. Routing rules determine who needs access to information, escalation rules set where the message needs to be directed if the initial contact does not respond, and device priority rules let users prioritize their preferred communication devices. The platform could be designed to use a web-based interface to make using the two-way communication easy. The hosted service uses secure socket layer (SSQ) technology for logins. The software could be designed to run on any operating system and is based on XML (markup language), Voice XML and J2EE (Java 2 Platform, Enterprise Edition). For voice-only device, the software platform can use text-to-voice conversion technology. The message can be received and responded to on any mobile or wireline phone using any carrier or multiple carriers. An exemplary software platform is a commercially available service INIogicNOW developed by MIR3, Inc. 
     In an exemplary scenario, the remote site receives a notification from the patient site for diagnostic support. The remote site staff launches a software with the patient communication identities such as the IP address. The software then, in tern, launches remote access application using the corresponding IP address through a network link  750  (also  330 ). After launching the application, a window appears showing exactly what&#39;s on the screen of the computer system at the patient side. The health care staff at the remote site can access the patient&#39;s computer to open folders and documents residing on the computer, edit them, print them, install or run inspection programs for diagnosis, view images, copy files between the remote site computer and the patient&#39;s computer, restart the patient&#39;s computer, and so on, exactly as though the health care staff seated in front of patient&#39;s computer. The connection is encrypted. With that, the remote site health care staff can perform relevant tasks remotely on service unit  300 . 
     An exemplary realization of direct access network link is by using a commercially available service GoToMyPc from www.gotomypc.com. There is no dedicated compute hardware system needed. Any computer capable of performing image/message processing and accessing the network could be used. That means that the remote site is itself location unconstrained. 
     Referring to  FIG. 3A , the remaining steps of the operation of the tongue inspection system of the present invention are to be further discussed. The step of case retrieval  307 , by parsing the tongue record  220  to find the patient&#39;s ID, retrieves the patient&#39;s previously saved tongue history records, if any, from a data storage server. The retrieved image data is used for the TCM doctor to perform a variety of tasks in the inspect step  308 . The data storage server could be a readable storage medium  407  shown in  FIG. 4 . One of the tasks that the step of inspection  308  performs is the color calibration that is discussed previously. 
     In step  308 , visualization tools are employed for TCM medical professional to examine concerned regions of the object (regions of interest in the images) for better diagnosis. One embodiment of such visualization facility and user interface is illustrated in  FIG. 9 . 
     There is shown in  FIG. 9  a computer monitor screen  900  (also  404  in  FIG. 4 ) hooked up to an image processor ( 402 ) that executes previously described steps. On screen  900 , two representative image  501  and  503  are shown on the left. Two patches  908  and  918  are selected by the user in order to perform the color appearance calibration. The user is also provided with three bars  922 ,  924  and  926  to choose coefficient values of w for red, green and blue channels as shown in Equation (2). The calibrated image  505  is shown in the middle of screen  900 . It should be pointed out that the color calibration process can run automatically without the user interference. Note that  FIG. 9  only illustrates an exemplary usage of the current design. One mode of inspection that step  308  performs is manual diagnosis by a TCM professional. In practice, the TCM professionals are able to retrieve other reference tongue images from the data storage server  407  and display them on the screen as diagnostic guide. An exemplary case discussed in Giovanni Maciocia&#39;s book is shown in  FIG. 10 . The deep red color shows that there is severe heat. The sides are redder and slightly swollen, it is clear that the heat is in the liver. This patient suffered from chronic asthma. 
     Another mode of inspection that step  308  performs is automatic disease diagnosis by using a classification algorithm that classifies tongue images to normal and abnormal groups. Tongue image features such as color, texture, shape and coating can be used to train a classifier and differentiate normal tongue images from abnormal ones. An exemplary color feature based classification algorithm is described below. 
     It is known to people skilled in the art that to reduce the brightness influence of brightness component of an image on classification tasks, a color transformation operation is usually applied to a color image before extracting color features. Similar to the color transformation performed in Cai&#39;s paper, the current method utilizes a color transformation that converts a color image from the regular RGB space to a generalized RGB space using the formula for every pixel of an image I: 
                           ⁢           I   _     j     =       I   j         ∑   i     ⁢     I   i           ;     i   ∈     [     1   ,   2   ,   3     ]       ;     j   ∈     [     1   ,   2   ,   3     ]                 (   4   )               
where Ī x  is a color plane (channel) of the resultant generalized RGB image and I x  is a color plane (channel) of an RGB color image as previously defined. For every pixel of Ī, the resultant three new elements are linearly dependent, that is,
 
                   ∑   j     ⁢       I   _     j       =   1     ,         
so that only two elements are needed to effectively form a new space that is collapsed from three dimensions to two dimensions. In most cases, Ī 1  and Ī 2 , that is, generalized R and G, are used. Noted that for simplicity, in the above discussion, an image and a pixel of that image share a single notation.
 
     In order to train a classifier, a plurality of tongue images (from both healthy and unhealthy people) must be collected and the tongue regions (see example of tongue region  502  in  FIG. 5 ) are segmented from all the collected tongue images. Methods such as reported by Bo Pang (see “On Automated Tongue Image Segmentation in Chinese Medicine,” Bo Pang, et al., Proc. ICPR, 2002) can be used to effectively extract tongue regions from the tongue images. Pixels of the extracted tongue regions are then converted from the RGB space to the generalized RGB space as described above. Denote the components of the converted color pixel by if;  q   j ;j=[1,2,3]. Denote {circumflex over (θ)} n =[  q   n   1 ,  q   n   2 ] as the color feature vector for a pixel from the tongue region of a healthy people. Denote  θ   a =[  q   a   1 ,  q   a   2 ] as the color feature vector for a pixel from the tongue region of an unhealthy people. Color feature vectors {circumflex over (θ)} n  and {circumflex over (θ)} a  will be used in a supervised learning process to train a classifier. 
     A supervised learning is defined as a learning process in which the exemplar set consists of pairs of inputs and desired outputs. In this tongue image classification case, the exemplar inputs are {circumflex over (θ)} n  and {circumflex over (θ)} a , the exemplar desired outputs are indicators O n  and O a  for normal and abnormal respectively. Suppose that there are M color feature vectors {circumflex over (θ)} among which, M n  vectors (denoted by {circumflex over (θ)} n   i ,i=1. . . M n ) represent normal condition with indicator O n , and M a  vectors (denoted by {circumflex over (θ)} n   i ,i=1. . . M a ) represent abnormal condition with indicator O a . These learned vectors {circumflex over (θ)} n   i  and {circumflex over (θ)}hd a i  are used to train a classifier that in turn is used to classify a tongue image in a detection or diagnosis process. 
     For the learning and training purpose, construct the training data set
 
{ p   j τ j   },j= 1 . . . l,τ   j ={−1,1 },p   j ∈             d   (5)
 
wherein τ j  are the class labels.

     For example, if the condition is normal, τ j =1, otherwise, τ j =−1. The vector p j =[{circumflex over (θ)}] is traditionally called feature vector in computer vision literature. The notion              d  represents a domain, d is the domain dimension. For this exemplary case, d=2. Persons skilled in the art understand that the data vector p j  can be constructed in a different manner and augmented with other physical (texture, coating, etc.) numerical elements.
     There are known types of classifiers that can be used to accomplish the task of differentiating normal tongue images from abnormal ones with the use of color feature vectors. An exemplary classifier is an SVM (support vector machine) (refer to “A Tutorial on Support Vector Machines for Pattern Recognition,” by C. Burges,  Data Mining and Knowledge Discovery,  2, 121-167, 1998, Kluwer Academic Publisher, Boston). 
     An example case of an SVM classifier would be training and classification of data representing two classes that are separable by a hyper-plane. A hyper-plane that separates the data satisfies
 
 W·p+σ= 0
 
where · is a dot product.
 
     The goal of training the SVM is to determine the free parameter W and σ. A scaling can always be applied to the scale of W and σ such that all the data obey the paired inequalities:
 
τ j ( W·p   j +σ)−1≦0 , ∀j   (6)
 
Equation (6) can be solved by minimizing a Lagrangian function
 
                     L   ⁡     (     W   ,   ξ     )       =         1   2     ⁢          W        2       -       ∑     j   =   1     l     ⁢       ξ   j     ⁡     (       τ   j     ⁡     (       W   ·     p   j       +   σ     )       )                   (   7   )               
with respect to the parameter W, and maximize it with respect to the undetermined multipliers ξ j ≦0.
 
     After the optimization problem has been solved, the expression for W in Equation (6) can be rewritten in terms of the support vectors with non-zero coefficients, and plugged into the equation for the classifying hyper-plane to give the SVM decision function: 
                     Ψ   ⁡     (     p   new     )       =       (       W   ·     p   new       +   σ     )     =         ∑     j   =   1       I   s       ⁢       τ   j     ⁢     ξ   j     ⁢       p   j     ·     p   new           +   σ               (   8   )               
wherein l s  is the number of support vectors. Classification of a new vector p new  into one of the two classes (normal and abnormal) is based on the sign of the decision function. Persons skilled in the art will recognize that in non-separable case, non-linear SVMs can be used.
 
     Also recognized is that other sophisticated tongue image classification methods such as Bayesian Network based algorithms are available in the public domain (see “Computerized Tongue Diagnosis Based on Bayesian Networks,” by Bo Pang, David Zhang, Naimin Li, and Kuanquan Wang,  IEEE Transactions on Biomedical Engineering , No. 10, Vol. 51, October 2004). 
     Noted that the tongue image classification can be accomplished automatically with no user interference. The classification process can also be accomplished with the help from the user with a user interface such as the one shown in  FIG. 9  for image segmentation. In either case, the task can be performed using the tongue record processor  402  in  FIG. 4 . The classification results will be incorporated into tongue record  220  in a step of tongue record updating  308 . 
     For completeness,  FIG. 4  shows an exemplary of a tongue image inspection hardware system useful in practicing the present invention including a service unit  300 . The images from the service unit  300  is provided to a tongue record processor  402 , such as a personal computer, or work station such as a Sun Sparc workstation. The tongue record processor  402  preferably is connected to a CRT display  404 , an operator interface such as a keyboard  406  and a mouse  408 . The tongue record processor  402  is also connected to computer readable storage medium  407 . The tongue record uploading step  312  shown in  FIG. 3A  can update  310  the patient history that is stored in medium  407 . The tongue record processor  402  transmits processed digital images and metadata to an output device  409 . Output device  409  can comprise a hard copy printer, a long-term image storage device, and a connection to another processor. The tongue record processor  402  is also linked to a communication link  414  or a telecommunication device connected, for example, to a broadband network. 
     The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention. 
     PARTS LIST 
     
         
           101  image 
           102  ColorChecker plate 
           104  tongue 
           204  general metadata 
           206  image packet 
           208  pixel data 
           210  image specific metadata 
           212  image specific collection data 
           216  inferred image specific data 
           220  tongue record 
           300  service unit 
           302  tongue image acquisition 
           304  tongue record formation 
           305  data communication 
           306  data input 
           307  case retrieval 
           308  inspection 
           309  data output 
           310  tongue recorder updating 
           312  tongue record uploading 
           320  process unit 
           330  communication link 
           402  tongue record processor 
           404  image display 
           406  data and command entry device 
           407  computer readable storage medium 
           408  data and command control device 
           409  output device 
           414  communication link 
           501  image 
           502  tongue 
           503  image 
           505  image 
           507  patch 
           509  patch 
           601  graph 
           603  graph 
           700  two way notification system 
           710  service unit I 
           712  service unit W 
           720  transmitting end I 
           722  receiving end I 
           724  transmitting end W 
           726  receiving end W 
           728  receiving end 
           730  transmitting end 
           740  processing unit 
           750  network link 
           752  network link 
           800  message 
           802  step 
           804  step 
           806  step 
           808  step 
           900  screen 
           908  patch 
           918  patch 
           922  bar 
           924  bar 
           926  bar