Abstract:
The invention relates to a system ( 100 ) for classifying image data on the basis of a model for adapting to an object in the image data, the system comprising a segmentation unit ( 110 ) for segmenting the image data by adapting the model to the object in the image data and a classification unit ( 120 ) for assigning a class to the image data on the basis of the model adapted to the object in the image data, thereby classifying the image data, wherein the classification unit ( 120 ) comprises an attribute unit ( 122 ) for computing a value of an attribute of the model on the basis of the model adapted to the object in the image data, and wherein the assigned class is based on the computed value of the attribute. Thus, the system ( 100 ) of the invention is capable of classifying the image data without any user input. All inputs required for classifying the image data  10  constitute a model for adapting to an object in the image data. A person skilled in the art will understand however that in some embodiments of the system ( 100 ), a limited number of user inputs may be enabled to let the user influence and control the system and the classification process.

Description:
FIELD OF THE INVENTION 
       [0001]    The invention relates to classifying image data and, more particularly, to classifying image data on the basis of a model for adapting to an object in the image data. 
       BACKGROUND OF THE INVENTION 
       [0002]    Radiologists are faced with ever increasing workloads resulting from the ever increasing number of images to be analyzed, classified and described. Classifying image data may be useful, for example, for image data retrieval. Nowadays, a class of image data is typically based on the acquisition modality, e.g. CT, part of the anatomy represented by the image data, e.g. chest, gender and age group of the patient, e.g. male, young adult, and objects described by the image data. Description of the anatomy represented by the image data is particularly time consuming and often requires studying many images rendered based on acquired image data. The rendered images are viewed and described by radiologists. In order to assist a radiologist in his tasks, software implemented image analysis systems are available. Many software packages provide interactive tools for measuring objects in the image. For example, the user may select two points on the wall of a blood vessel for computing a distance between the two points, yielding the diameter of the vessel. Other systems include image segmentation systems for delineating features such as edges and surfaces in images and measuring tools for measuring objects in the image data on the basis of the image segmentation. For example, WO 2003/023717 entitled Automated Measurement of Geometrical Properties describes a method of measuring a geometric parameter of a three-dimensional structure contained in an object, using model-based image segmentation. First, a first model is adapted to an object in the image data. Then, a second model is fitted to the adapted first model by adjusting the value of the geometric parameter of the second model. For example, the second model may be a sphere and the geometric parameter may be the sphere diameter. The first model may be a triangular mesh for adapting to a femur bone depicted in the image data. The sphere may be fitted to the femur head. After obtaining necessary parameter values, the radiologist is required to describe the findings and/or classify the image data based on the findings. Typically this is done by dictating a description and using speech recognition techniques for converting speech to text. 
       SUMMARY OF THE INVENTION 
       [0003]    It would be advantageous to provide means for classifying image data which would require fewer inputs from a radiologist. 
         [0004]    Thus, in an aspect, the invention provides a system for classifying image data on the basis of a model for adapting to an object in the image data, the system comprising 
         [0005]    a segmentation unit for segmenting the image data by adapting the model to the object in the image data; and 
         [0006]    a classification unit for assigning a class to the image data on the basis of the model adapted to the object in the image data, thereby classifying the image data, wherein the classification unit comprises an attribute unit for computing a value of an attribute of the model on the basis of the model adapted to the object in the image data, and wherein the assigned class is based on the computed value of the attribute. 
         [0007]    Thus, the system of the invention is capable of classifying the image data without any user input. All inputs required for classifying the image data constitute a model for adapting to an object in the image data. A person skilled in the art will understand, however, that in some embodiments, a limited number of user inputs, e.g., an input for selecting a model for adapting to an object in the image data, may be enabled to let the user influence and control the system and the classification process. 
         [0008]    In an embodiment of the system, the attribute of the model is defined based on the model or based on a user attribute input. The classification unit of the system is arranged to employ the attribute unit for computing the value of the attribute. The attribute whose value is to be computed may be defined based on the model. For example, if the model comprises a mesh for adapting to the object in the image, the model may further specify two vertices. The two vertices may define an attribute of the mesh—the distance between said vertices. The attribute unit may be arranged for computing the value of the distance between the specified vertices of the adapted mesh. Such an attribute is determined based on the model. Alternatively, it may be useful to let the user provide a user attribute input, e.g., for indicating two vertices of the model mesh. The two vertices may define an attribute of the mesh—the distance between said vertices. The attribute unit may be arranged for computing the value of the distance between the indicated vertices of the adapted mesh. Such an attribute is determined based on the user attribute input. 
         [0009]    Those skilled in the art will understand that it is possible that certain attributes do not require to be defined either by the model or by a user attribute input. For example, the system may comprise an attribute unit for computing the value of the distance between every two vertices of the mesh. The attribute unit may be further arranged for selecting the largest value. Such an attribute—the diameter of the smallest sphere containing all vertices of the model mesh—can be computed for every mesh and does not require to be defined either by the model or by the user attribute input. The system may be arranged to routinely compute the value of such an attribute. 
         [0010]    In an embodiment of the system, the value of the attribute of the model is a text for classifying the image data. A text-valued attribute may be easier to understand and interpret for users. An example of a text-valued attribute is the type of a breast nodule detected in an X-ray image which may assume values “malignant” or “benign”. The value may be assigned based on the brightness of the segmented nodule after injection of a contrast agent. Since malignant tumors develop their own blood supply system, they appear brighter in an X-ray image than benign nodules. Nodules having a brightness above a threshold may be classified as malignant. 
         [0011]    In an embodiment of the system, the value of the attribute of the model is at least one number for classifying the image data. As discussed above, the attribute may be the distance between two vertices of the model mesh. 
         [0012]    In an embodiment of the system, the value of the attribute of the model is a range or a vector for classifying the image data. For example, a vector-valued attribute may describe the main principal axis of the inertia tensor of a structure, e.g., a vertebra. An exemplary range-valued attribute is a percent range of the stenosis of an artery by arterial plaque. 
         [0013]    In an embodiment, the system further comprises a description unit for creating a description based on the class assigned to the image data. The description may comprise both text and numerical data derived from the class assigned to the image. The description unit may be arranged to use a vocabulary and grammar rules for building syntactically correct sentences. The description may be used for creating reports, for example. 
         [0014]    In an embodiment of the system, the segmentation unit is further arranged for segmenting second image data by adapting the model to a second object in the second image data, the classification unit is further arranged for assigning a second class to the second image data on the basis of the model adapted to the second object in the second image data, thereby classifying the second image data, and the system further comprises a comparison unit for comparing the class assigned to the image data with the second class assigned to the second image data to determine a correspondence between the image data and the second image data. The correspondence may be based on a similarity of the image data and the second image data. Alternatively, the correspondence may be based on complementarity of the image data and the second image data. 
         [0015]    In an embodiment, the system further comprises a second comparison unit for comparing the class assigned to the image data with a data record to determine a correspondence between the image data and the data record. The data record may be, for example, an entry from a handbook or an encyclopedia. 
         [0016]    In an embodiment, the system further comprises a second classification unit for assigning a data record class to the data record, thereby classifying the data record, and wherein the second comparison unit is arranged for comparing the class assigned to the image data with the data record class assigned to the data record. 
         [0017]    In a further aspect of the invention, the system according to the invention is comprised in a database system. The database comprises items. Each data record is assigned a data record class. The query for retrieving a data record from the database is determined based on the class assigned by the system to the image data. The system is adapted for identifying the data record that is similar or complementary to the image by comparing the class assigned to the image with the class assigned to the data record. 
         [0018]    In a further aspect, the system according to the invention is comprised in an image acquisition apparatus. 
         [0019]    In a further aspect, the system according to the invention is comprised in a workstation. 
         [0020]    In a further aspect, the invention provides a method of classifying image data on the basis of a model for adapting to an object in the image data, the method comprising 
         [0021]    a segmentation step for segmenting the image data by adapting the model to the object in the image data; and 
         [0022]    a classification step for assigning a class to the image data on the basis of the model adapted to the object in the image data, thereby classifying the image data, wherein the classification step comprises an attribute step for computing a value of an attribute of the model on the basis of the model adapted to the object in the image data, and wherein the assigned class is based on the computed value of the attribute. 
         [0023]    In a further aspect, the invention provides a computer program product to be loaded by a computer arrangement, the computer program comprising instructions for classifying image data on the basis of a model for adapting to an object in the image data, the computer arrangement comprising a processing unit and a memory, the computer program product, after being loaded, providing said processing unit with the capability to carry out steps of the method. 
         [0024]    It will be appreciated by those skilled in the art that two or more of the above-mentioned embodiments, implementations, and/or aspects of the invention may be combined in any way deemed useful. 
         [0025]    Modifications and variations of the database system, of the image acquisition apparatus, of the workstation, of the method, and/or of the computer program product, which correspond to the described modifications and variations of the system, can be carried out by a person skilled in the art on the basis of the present description. 
         [0026]    A person skilled in the art will appreciate that the method may be applied to multidimensional image data, e.g., 2-dimensional (2-D), 3-dimensional (3-D) or 4-dimensional (4-D) image data, acquired by various acquisition modalities such as, but not limited to, X-ray Imaging, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT), and Nuclear Medicine (NM). 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0027]    These and other aspects of the invention will become apparent from and will be elucidated with respect to the implementations and embodiments described hereinafter and with reference to the accompanying drawings, wherein: 
           [0028]      FIG. 1  shows a block diagram of an exemplary embodiment of the system; 
           [0029]      FIG. 2  illustrates segmentation of the optic nerves; 
           [0030]      FIG. 3  shows the variation of the diameter of the left optic nerve model and the left sheath model along the left optic nerve; 
           [0031]      FIG. 4  shows the variation of the intensity determined on the basis of the left optic nerve model along the left optic nerve; 
           [0032]      FIG. 5  shows a flowchart of an exemplary implementation of the method; 
           [0033]      FIG. 6  schematically shows an exemplary embodiment of the database system; and 
           [0034]      FIG. 7  schematically shows an exemplary embodiment of the image acquisition apparatus; and 
           [0035]      FIG. 8  schematically shows an exemplary embodiment of the workstation. 
       
    
    
       [0036]    Identical reference numerals are used to denote similar parts throughout the Figures. 
       DETAILED DESCRIPTION OF EMBODIMENTS 
       [0037]      FIG. 1  schematically shows a block diagram of an exemplary embodiment of the system  100  for classifying image data on the basis of a model for adapting to an object in the image data, the system comprising: 
         [0038]    a segmentation unit  110  for segmenting the image data by adapting the model to the object in the image data; and 
         [0039]    a classification unit  120  for assigning a class to the image data on the basis of the model adapted to the object in the image data, thereby classifying the image data, wherein the classification unit comprises an attribute unit  122  for computing a value of an attribute of the model on the basis of the model adapted to the object in the image data, and wherein the assigned class is based on the computed value of the attribute. 
         [0040]    The exemplary embodiment of the system  100  further comprises the following optional units: 
         [0041]    a description unit  130  for creating a description based on the class assigned to the image data; 
         [0042]    a comparison unit  140  for comparing the class assigned to the image data with the second class assigned to the second image data to determine a correspondence between the image data and the second image data; 
         [0043]    a second classification unit  150  for assigning a data record class to the data record, thereby classifying the data record; 
         [0044]    a second comparison unit  155  for comparing the class assigned to the image data with a data record to determine a correspondence between the image data and the data record; 
         [0045]    a control unit  160  for controlling the work of the system  100 ; 
         [0046]    a user interface  165  for communication between the user and the system  100 ; and 
         [0047]    a memory unit  170  for storing data. 
         [0048]    In an embodiment of the system  100 , there are three input connectors  181 ,  182  and  183  for the incoming data. The first input connector  181  is arranged to receive data coming in from a data storage means such as, but not limited to, a hard disk, a magnetic tape, a flash memory, or an optical disk. The second input connector  182  is arranged to receive data coming in from a user input device such as, but not limited to, a mouse or a touch screen. The third input connector  183  is arranged to receive data coming in from a user input device such as a keyboard. The input connectors  181 ,  182  and  183  are connected to an input control unit  180 . 
         [0049]    In an embodiment of the system  100 , there are two output connectors  191  and  192  for the outgoing data. The first output connector  191  is arranged to output the data to a data storage means such as a hard disk, a magnetic tape, a flash memory, or an optical disk. The second output connector  192  is arranged to output the data to a display device. The output connectors  191  and  192  receive the respective data via an output control unit  190 . 
         [0050]    A person skilled in the art will understand that there are many ways to connect input devices to the input connectors  181 ,  182  and  183  and the output devices to the output connectors  191  and  192  of the system  100 . These ways comprise, but are not limited to, a wired and a wireless connection, a digital network such as, but not limited to, a Local Area Network (LAN) and a Wide Area Network (WAN), the Internet, a digital telephone network, and an analog telephone network. 
         [0051]    In an embodiment of the system  100 , the system  100  comprises a memory unit  170 . The system  100  is arranged to receive input data from external devices via any of the input connectors  181 ,  182 , and  183  and to store the received input data in the memory unit  170 . Loading the input data into the memory unit  170  allows quick access to relevant data portions by the units of the system  100 . The input data may comprise, for example, the image data and model data. The memory unit  170  may be implemented by devices such as, but not limited to, a Random Access Memory (RAM) chip, a Read Only Memory (ROM) chip, and/or a hard disk drive and a hard disk. The memory unit  170  may be further arranged to store the output data. The output data may comprise, for example, the class assigned to the image. Optionally, the output data may further comprise the model adapted to the object in the image data and/or the value of the attribute. The memory unit  170  may be also arranged to receive data from and/or deliver data to the units of the system  100  comprising the segmentation unit  110 , the classification unit  120 , the attribute unit  122 , the description unit  130 , the comparison unit  140 , the second classification unit  150 , the second comparison unit  155 , the control unit  160 , and the user interface  165 , via a memory bus  175 . The memory unit  170  is further arranged to make the output data available to external devices via any of the output connectors  191  and  192 . Storing data from the units of the system  100  in the memory unit  170  may advantageously improve performance of the units of the system  100  as well as the rate of transfer of the output data from the units of the system  100  to external devices. 
         [0052]    Alternatively, the system  100  may comprise no memory unit  170  and no memory bus  175 . The input data used by the system  100  may be supplied by at least one external device, such as an external memory or a processor, connected to the units of the system  100 . Similarly, the output data produced by the system  100  may be supplied to at least one external device, such as an external memory or a processor, connected to the units of the system  100 . The units of the system  100  may be arranged to receive the data from each other via internal connections or via a data bus. 
         [0053]    In an embodiment of the system  100 , the system  100  comprises a control unit  160  for controlling the system  100 . The control unit may be arranged to receive control data from and provide control data to the units of the system  100 . For example, after adaptation of the model to the image data, the segmentation unit  110  may be arranged to provide control data “the image data is segmented” to the control unit  160  and the control unit  160  may be arranged to provide control data “classify the image data” to the classification unit  120 . Alternatively, a control function may be implemented in another unit of the system  100 . 
         [0054]    In an embodiment of the system  100 , the system  100  comprises a user interface  165  for communication between a user and the system  100 . The user interface  165  may be arranged to receive a user input for selecting the model for adapting to the object in the image data. The user interface may further provide means for displaying a view of the mesh adapted to the object. Optionally, the user interface may receive a user input for selecting a mode of operation of the system such as, e.g., for defining the terms of the external or internal energy expression, or a pre-positioning method. A person skilled in the art will understand that more functions may be advantageously implemented in the user interface  165  of the system  100 . 
         [0055]    In an embodiment, the system of the invention is arranged for classifying image data describing the optic nerves.  FIG. 2  illustrates segmentation of the left and right optic nerve using a left N 1  and right N 2  optic nerve model. Each optic nerve comprises a bundle of fibers for transmitting electrical impulses from the retina to the brain. The left and right optic nerves leave the respective eyeballs, modeled by respective eyeball models E 1  and E 1 , via the optic canals and run towards the chiasm, modeled by a chiasm model C, where there is a partial crossing of fibers of both optic nerves. A segment of each optic nerve at each eyeball is protected by a sheath modeled by a left model S 1  and a right sheath model S 2 . The typical diameter of the optic nerve increases from about 1.6 mm inside the eyeball to 3.5 mm at the eyeball orbit and further to 4.5 mm within the cranial space. 
         [0056]    In an embodiment, the models N 1 , N 2 , E 1 , E 2 , and C are mesh surface models. A mesh model suitable for modeling an optic nerve is described in SPIE Medical Imaging, Conference 6914 Image Processing, Session 7, Segmentation of the heart and major vascular structures in cardiovascular CT images, Jochen Peters, Olivier Ecabert, Cristian Lorenz, Jens von Berg, Matthew J. Walker, Mani Vembar, Mark E. Olszewski, Jürgen Weese, San Diego 18 Feb. 2008, to appear in Proceedings SPIE Medical Imaging 2008: Image Processing, J. M. Reinhardt and J. P. Pluim, eds., hereinafter referred to as Ref. 1. Each optic nerve is modeled by a stack of consecutive rings, as described in more detail in section 2.3 of Ref 1. Each ring is defined by a fixed number of vertices. The vertices of two consecutive rings are connected with edges forming a segment mesh with triangular faces. The triangular mesh for modeling the optic nerve is placed in the image data space and adapted to the optic nerve in the imaged data. The placement may be based on detection of the nerve or of a reference structure in the image data using, for example, the generalized Hough transform. A method of placing a mesh using the generalized Hough transform is described in M. J. Walker, A. Chakrabarty, M. E. Olszewski, O. Ecabert, J. Peters, C. Lorenz, J. von Berg, M. Vembar, K. Subramanyan, and J. Weese, “Comparison of two initialization methods for automatic, whole-heart, model-based segmentation of multiphase cardiac MSCT images,” in Proc. 1st Annual Scientific Meeting Soc. Cardiovascular Computed Tomography, Int. Journal Cardiovascular Imaging, 22 (suppl. 1), p. 12, July 2006. The adaptation of the initialized mesh employs the method described, for example, in J. Peters, O. Ecabert, C. Meyer, H. Schramm, R. Kneser, A. Groth, and J. Weese, “Automatic whole heart segmentation in static magnetic resonance image volumes,” in Proc. MICCAI, N. Ayache, S. Ourselin, and A. Maeder, eds., LNCS 4792, pp. 402-410, Springer, 2007. Each sheath of the optic nerve is modeled using an approach similar to that used to model the optic nerve. 
         [0057]    In an embodiment, the models of the two optic nerve models N 1  and N 2  and of the two sheath models S 1  and S 2  are attached to the respective eyeball models E 1  and E 2  and the chiasm model C which is further attached to other structures of the central nervous system. Such a comprehensive model comprising multiple parts is adapted to the structures in the image data. Alternatively, the component models can be adapted one after another with models of reference structures, such as the two hemispheres and eyeballs being adapted before the adaptation of the optic nerve models N 1  and N 2  and the sheath models S 1  and S 2  attached to the eyeball models E 1  and E 2  and the chiasm model C. Those skilled in the art will understand that there are other models and methods of adapting such models to objects in the image data which may be used by the system according to the invention. The models and methods described above illustrate embodiments of the system and must not be construed as limiting the scope of the claims. 
         [0058]    In an embodiment, the image data is classified based on the state of the left optic nerve. In more detail, the image data is classified based on two attributes of the left optic nerve model N 1  adapted to the left optic nerve in the image data: the diameter of the left optic nerve model N 1  and the mean intensity of the left optic nerve model N 1 . The value of the diameter and the mean gray value are determined for each ring of the stack of rings used to model the left optic nerve. The diameter d i  of the i-th ring of the left optic nerve model N 1  is computed for each adapted ring, using the formula A i =π(d i /2) 2  where A i  is the area of the ring. The area of the ring is approximated by the area of the polygon defined by the vertices of the ring. The location of each ring is defined by the distance of the ring center from the surface of the left eyeball model measured along the centerline. This distance of each ring is approximated by the sum of distances of the centers of consecutive rings between the ring in question and the ring adjacent to the eyeball. The center of each ring is defined as the mass center of the vertices of the ring. The diameter of the sheath model S 1  may be computed in an analogous way.  FIG. 3  shows the variation of the diameter of the left optic nerve model N 1  and the left sheath model S 1  along the left optic nerve. 
         [0059]    The mean gray value of the i-th ring of the left optic nerve model N 1  is computed from the gray values of sampling points. The sampling points are points selected at equal distances on each semi-line originating at the ring center and extending towards a vertex of the ring. The histogram of the gray values at these sampling points shows two large maxima at gray values I 0,i  and J 0,i , where i is the ring index. The larger of the two gray values, denoted I 0,i , approximates the mean intensity of the optic nerve at the location corresponding to the location of the i-th ring. The smaller of the two gray values, denoted J 0,i , approximates the mean intensity of the sheath at the location corresponding to the location of the i-th ring.  FIG. 4  shows the variation of the intensity determined on the basis of the left optic nerve model N 1  along the left optic nerve. 
         [0060]    In an embodiment of the system, there are two attributes of the left optic nerve model used for defining the class of the image data. The first attribute is the graph of the diameter of the optic nerve and the second attribute is the graph of the mean intensity of the optic nerve model. It is worth pointing out that although the second attribute is defined based on the optic nerve model, the value of the second attribute is further based on the gray values of the image data. The two graphs may be represented by a table comprising locations along the optic nerve (i.e., coordinates of the centers of mass of the rings) and the corresponding values of the diameter of the optic nerve and the mean intensity of the optic nerve. The values of the diameter and the mean intensity may be quantized to limit the number of classes. In the present context, quantization means replacing each value from a range of values with one value, e.g. the smallest value or the largest value from the range of values or the mean of said smallest and largest value. Optionally, the values of the graph may be text values such as “high”, “low” or “medium”, based on the mean value of the diameter, for example. 
         [0061]    In an embodiment, the graph of the left optic nerve diameter is further processed and the image data is classified based on the results of the processing of the graph: 
         [0062]    a smoothing filter is applied to the sequence of diameter values d 0,i  to reduce artifacts of segmentation; for example a moving average filter can be used; the output of this step are smoothed diameter values d 1,i    
         [0063]    the maximum value M and minimum value m of the smoothed diameter values d 1,i  are computed and the diameter values are normalized, e.g. by subtracting the minimum m from each value and dividing the obtained value by the M−m; the output of this step are normalized diameter values d 2,i ; 
         [0064]    the first derivative of the normalized diameter values d 2,i  is computed; the output of this step are first derivative values D 2,i ; 
         [0065]    a positive threshold value t is computed on the basis of the first derivative values D 2,i ; this threshold is used to quantize the first derivative values D 2,i  as follows: 
         [0066]    if −t&lt;D 2,i )&lt;t, D 2,i  is replaced with 0; 
         [0067]    if D 2,i &gt;t, D 2,i  is replaced with 1; 
         [0068]    if D 2,i &lt;−t, D 2,i  is replaced with −1; 
         [0000]    the output of this step are quantized first derivative values D 3,i ;
 
a smoothing filter is applied to the quantized derivative values D 3,i ; for example, a moving average filter can be used; the output of this step are smoothed derivative values D 4,i ;
 
         [0069]    a second positive threshold value s is computed on the basis of the smoothed derivative values D 4,i ; this threshold is used to quantize the smoothed derivative values D 4,i  as follows: 
         [0070]    if −s&lt;D 4,i )&lt;s, D 4,i  is replaced with 0; 
         [0071]    if D 4,i &gt;s, D 4,i  is replaced with 1; 
         [0072]    if D 4,i &lt;−s, D 4,i  is replaced with −1; 
         [0000]    the output of this step are quantized smoothed derivative values D 5,i . 
         [0073]    The sequence −1 . . . 0 . . . 1 in the quantized smoothed derivative values D 5,i  indicates enlarged nerve, and thus this sequence is referred to as enlarged nerve sequence. The sequence 1 . . . 0 . . . −1 in the quantized smoothed derivative values D 5,i  indicates thinned nerve, and thus this sequence is referred to as thinned nerve sequence. Consequently, image data depicting an optic nerve comprising an enlarged sequence is classified as enlarged, and image data depicting an optic nerve comprising a thinned nerve sequence is classified as thinned. Image data depicting an optic nerve comprising no enlarged nerve sequence and no thinned nerve sequence is classified as normal. Image data depicting an optic nerve comprising both an enlarged nerve sequence and a thinned nerve sequence may be classified either as enlarged or thinned. Optionally, a classification comprising multiple values, e.g. enlarged, thinned, may be used. 
         [0074]    In an embodiment, the graph of the left optic nerve mean intensity is further processed and the image data is classified based on the results of the processing of the graph: 
         [0075]    a smoothing filter is applied to the sequence of mean intensity values I 0,i  to reduce artifacts of segmentation; for example a moving average filter can be used; the output of this step are smoothed intensity values I 1,i    
         [0076]    the maximum value M and minimum value m of the smoothed mean intensity values I 1,i  are computed and the gray values are normalized, e.g. by subtracting the minimum m from each value and dividing the obtained value by M−m; the output of this step are normalized intensity values I 2,i ; 
         [0077]    the first derivative of the normalized mean intensity values I 2,i  is computed; the output of this step are first derivative values D 2,i ; 
         [0078]    a positive threshold value t is computed on the basis of the first derivative values D 2,i ; this threshold is used to quantize the first derivative values D 2,i  as follows: 
         [0079]    if −t&lt;D 2,i )&lt;t, D 2,i  is replaced with 0; 
         [0080]    if D 2,i &gt;t, D 2,i  is replaced with 1; 
         [0081]    if D 2,i &lt;−t, D 2,i  is replaced with −1; 
         [0000]    the output of this step are quantized first derivative values D 3,i ; 
         [0082]    a smoothing filter is applied to the quantized derivative values D 3,i ; for example, a moving average filter can be used; the output of this step are smoothed derivative values D 4,i ; 
         [0083]    a second positive threshold value s is computed on the basis of the smoothed derivative values D 4,i ; this threshold is used to quantize the smoothed derivative values D 4,i  as follows: 
         [0084]    if −s&lt;D 4,i )&lt;s, D 4,i  is replaced with 0; 
         [0085]    if D 4,i &gt;s, D 4,i  is replaced with 1; 
         [0086]    if D 4,i &lt;−s, D 4,i  is replaced with −1; 
         [0000]    the output of this step are quantized smoothed derivative values D 5,i . 
         [0087]    The sequence −1 . . . 0 . . . 1 in the quantized smoothed derivative values D 5,i  indicates hypointensity, and thus this sequence is referred to as hypointensity sequence. The sequence 1 . . . 0 . . . −1 in the quantized smoothed derivative values D 5,i  indicates hyperintensity, and thus this sequence is referred to as hyperintensity sequence. Consequently, image data depicting an optic nerve comprising a hypointensity sequence is classified as hypointense, and image data depicting an optic nerve comprising a hyperintensity sequence is classified as hyperintense. Image data depicting an optic nerve comprising no hypointensity sequence and no hyperintensity sequence is classified as isointense. Image data depicting an optic nerve comprising both a hypointensity sequence and a hyperintensity sequence may be classified either as hyperintense or hypointense. Optionally, a classification comprising multiple values, e.g. hypointense, hyperintense, may be used. 
         [0088]    The mean intensity graph shown in  FIG. 4  reveals a hypointense image data comprising an optic nerve detected using the filters described above. It is worth pointing out that due to MR bias fields, the grey level of the optic nerve increases from the globe to the chiasm. The use of filters allows for a correct classification of the image data. 
         [0089]    Table 1 illustrates a classification scheme for classifying image data, based on the diameter and intensity attributes of the left optic nerve model adapted to the left optic nerve in the image data. 
         [0000]    
       
         
               
               
               
             
           
               
                   
                   
               
               
                   
                 Attribute 
                 Attribute value 
               
               
                   
                   
               
             
             
               
                   
                 diameter 
                 enlarged 
               
               
                   
                   
                 normal 
               
               
                   
                   
                 thinned 
               
               
                   
                 intensity 
                 hyperintense 
               
               
                   
                   
                 isointense 
               
               
                   
                   
                 Hypointense 
               
               
                   
                   
               
             
          
         
       
     
         [0090]    Classifying an image may be very useful. Image data class descriptors may be used for describing images, for writing medical reports on findings based on the image data, for constructing queries for searching other databases. When the image data class and a class of a data record in a database are identical or satisfy certain condition, the data record can be retrieved as a data record corresponding to the image data. 
         [0091]    In an embodiment of the system  100 , a statistical distribution of the diameter values and/or intensity values for each ring is used for classifying the image data. The statistical distribution of the diameter and intensity values can be learned from a training set of image data in a training phase. Optionally, the system  100  may be adapted for carrying out such training The classification unit may be arranged for comparing the computed diameter and intensity values with learned sequences indicating “abnormalities” and for computing the probability of occurrence of said abnormalities, or simply for classifying the image data as “normal” or “abnormal” based on probability thresholds. 
         [0092]    Those skilled in the art will appreciate that the classification unit  120  of the system  100  may comprise a plurality of attribute units  122 , each attribute unit  122  arranged for computing an attribute value based on the model adapted to the object in the image data. The plurality of computed attribute values defines a class of the image data. Optionally, the system  100  may further comprise a plurality of segmentation units  110  and a plurality of corresponding classification units  120 . Each segmentation unit may employ its own segmentation method based on its own model. The attribute unit  122  of the corresponding classification unit  120  may be arranged to compute attribute values of the model employed by the segmentation unit  110 . 
         [0093]    In an embodiment of the system  100 , the user may be enabled to select attributes to be used by the classification unit  120  to classify the image data. For example, if the user is interested in the diameter of the optic nerve, he may use the user interface  165  to indicate that the classification should be based exclusively on the diameter of the optic nerve model. In another situation, the user may be interested in classifying the image data based on both the diameter and the intensity of the optic nerve. Hence, he may instruct the system via the user interface to use both attributes of the optic nerve model for image data classification. 
         [0094]    In an embodiment of the system  100 , the segmentation unit  110  of the system  100  is further arranged for segmenting second image data by adapting the model to a second object in the second image data. The classification unit  120  is further arranged to assign a second class to the second image data on the basis of the model adapted to the second object in the second image data, thereby classifying the second image data. The image data, hereinafter referred to as the first image data, may be compared with the second image data on the basis of the class of the first image data, hereinafter referred to as the first class and the second class. The comparison is carried out by a comparison unit  140  to determine a correspondence between the first and second image data. The comparison unit  140  may be arranged for verifying that the first class and the second class satisfy a condition. If the condition is satisfied, the second image data is considered the corresponding image data. The condition may be the identity condition: the first image data corresponds to the second image data if the first class is identical with the second class. The condition may be a similarity condition: the first image data corresponds to the second image data if the first class is similar to the second class. The condition may be also a complementarity condition: the first image data corresponds to the second image data if the first class is complementary to the second class, like two jigsaw puzzle pieces. 
         [0095]    The second image data may be image data from a database of image data. The first image data may be query image data. Finding the second image data which is similar to the first image data may be a very valuable tool for a physician, useful for diagnostics and treatment planning. Such a tool enables the physician to retrieve reference images from the database of image data. 
         [0096]    In an embodiment, the system  100  further comprises a second comparison unit  155  for comparing the class assigned to the image data with a data record class assigned to a data record to determine a correspondence between the image data and the data record. The data record class may be already available in the database index. For example, the class of the image data may be defined by keywords such as “left optic nerve”, “enlarged”, and “hypointense”. The second comparison unit  155  may be arranged to perform a keyword search for the keywords “left optic nerve”, “enlarged”, and “hypointense” in entries from a handbook of guidelines for a neurologist. If the number of hits in an entry exceeds a threshold, this entry is determined to be corresponding to the image data. Alternatively or additionally, the system  100  may further comprise a second classification unit  150  for assigning a data record class to the data record, thereby classifying the data record, and the second comparison unit  155  is arranged for comparing the class assigned to the image data with the data record class assigned to the data record. The second classification unit  150  may be, for example, a unit for classifying a specification of a hip implant from a catalogue of hip implants or second image data acquired using a modality different from the modality used to acquire the image data. Optionally, the second classification unit  150  may comprise a second segmentation unit for segmenting image data acquired using the other modality. Alternatively, the second classification unit  150  may be a unit for classifying another data record, e.g., an entry in an encyclopedia or in some guidelines. 
         [0097]    A person skilled in the art will appreciate that the system  100  may be a valuable tool for assisting a physician in many aspects of her/his job. Further, although the embodiments of the system are illustrated using medical applications of the system, non-medical applications of the system are also contemplated. 
         [0098]    Those skilled in the art will further understand that other embodiments of the system  100  are also possible. It is possible, among other things, to redefine the units of the system and to redistribute their functions. Although the described embodiments apply to medical images, other applications of the system, not related to medical applications, are also possible. 
         [0099]    The units of the system  100  may be implemented using a processor. Normally, their functions are performed under the control of a software program product. During execution, the software program product is normally loaded into a memory, like a RAM, and executed from there. The program may be loaded from a background memory, such as a ROM, hard disk, or magnetic and/or optical storage, or may be loaded via a network like the Internet. Optionally, an application-specific integrated circuit may provide the described functionality. 
         [0100]      FIG. 5  shows a flowchart of an exemplary implementation of the method  500  of classifying image data on the basis of a model for adapting to an object in the image data. The method  500  begins with a segmentation step  510  for segmenting the image data by adapting the model to the object in the image data. After the segmentation step, the method  500  continues to a classification step  520  for assigning a class to the image data on the basis of the model adapted to the object in the image data, thereby classifying the image data, 
         [0000]    wherein the classification step  520  comprises an attribute step  522  for computing a value of an attribute of the model on the basis of the model adapted to the object in the image data, and wherein the assigned class is based on the computed value of the attribute. 
         [0101]    After the classification step  520 , the method  500  terminates. 
         [0102]    A person skilled in the art may change the order of some steps or perform some steps concurrently using threading models, multi-processor systems or multiple processes without departing from the concept as intended by the present invention. Optionally, two or more steps of the method of the current invention may be combined into one step. Optionally, a step of the method of the current invention may be split into a plurality of steps. Optionally, the method  500  may further comprise a description step, a comparison step, a second classification step, and/or a second comparison step corresponding to the respective units of the system  100 . 
         [0103]      FIG. 6  schematically shows an exemplary embodiment of the database system  600  employing the system  100  of the invention, said database system  600  comprising a database unit  610  connected via an internal connection to the system  100 , an external input connector  601 , and an external output connector  602 . This arrangement advantageously increases the capabilities of the database system  600 , providing said database system  600  with advantageous capabilities of the system  100 . 
         [0104]      FIG. 7  schematically shows an exemplary embodiment of the image acquisition apparatus  700  employing the system  100  of the invention, said image acquisition apparatus  700  comprising an image acquisition unit  710  connected via an internal connection with the system  100 , an input connector  701 , and an output connector  702 . This arrangement advantageously increases the capabilities of the image acquisition apparatus  700 , providing said image acquisition apparatus  700  with advantageous capabilities of the system  100 . 
         [0105]      FIG. 8  schematically shows an exemplary embodiment of the workstation  800 . The workstation comprises a system bus  801 . A processor  810 , a memory  820 , a disk input/output (I/O) adapter  830 , and a user interface (UI)  840  are operatively connected to the system bus  801 . A disk storage device  831  is operatively coupled to the disk I/O adapter  830 . A keyboard  841 , a mouse  842 , and a display  843  are operatively coupled to the UI  840 . The system  100  of the invention, implemented as a computer program, is stored in the disk storage device  831 . The workstation  800  is arranged to load the program and input data into memory  820  and execute the program on the processor  810 . The user can input information to the workstation  800 , using the keyboard  841  and/or the mouse  842 . The workstation is arranged to output information to the display device  843  and/or to the disk  831 . A person skilled in the art will understand that there are numerous other embodiments of the workstation  800  known in the art and that the present embodiment serves the purpose of illustrating the invention and must not be interpreted as limiting the invention to this particular embodiment. 
         [0106]    It should be noted that the above-mentioned embodiments illustrate rather than limit the invention and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” does not exclude the presence of elements or steps not listed in a claim or in the description. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements and by means of a programmed computer. In the system claims enumerating several units, several of these units can be embodied by one and the same record of hardware or software. The usage of the words first, second, third, etc., does not indicate any ordering. These words are to be interpreted as names.