Displaying image data using automatic presets

Abstract

A computer automated method that applies supervised pattern recognition to classify whether voxels in a medical image data set correspond to a tissue type of interest is described. The method comprises a user identifying examples of voxels which correspond to the tissue type of interest and examples of voxels which do not. Characterizing parameters, such as voxel value, local averages and local standard deviations of voxel value are then computed for the identified example voxels. From these characterizing parameters, one or more distinguishing parameters are identified. The distinguishing parameter are those parameters having values which depend on whether or not the voxel with which they are associated corresponds to the tissue type of interest. The distinguishing parameters are then computed for other voxels in the medical image data set, and these voxels are classified on the basis of the value of their distinguishing parameters. The approach allows tissue types which differ only slightly to be distinguished according to a user's wishes.

1 . A method of numerically processing a medical image data set comprising voxels, the method comprising: 
 (a) receiving user input to positively and negatively select voxels that are and are not of a tissue type of interest;    (b) determining a distinguishing function that discriminates between the positively and negatively selected voxels on the basis of one or more characterizing parameters of the voxels; and    (c) classifying further voxels in the medical image data set on the basis of the distinguishing function.    
   
   
       2 . The method according to  claim 1 , further comprising presenting an example image representing the medical image data set to a user, wherein the user positions a pointer at locations in the example image to select corresponding voxels.  
   
   
       3 . The method according to  claim 2 , wherein a selected voxel is taken to be a voxel whose coordinates in the data set map to the location of the pointer in the example image.  
   
   
       4 . The method according to  claim 2 , wherein selected voxels are taken to be voxels in a region surrounding a voxel whose coordinates in the data set map to the location of the pointer in the example image.  
   
   
       5 . The method of  claim 1 , further comprising rendering an image of the medical image data set, wherein the rendering takes account of the classification of voxels, and displaying the image to the user.  
   
   
       6 . The method of  claim 1 , further comprising rendering an image of the medical image data set, wherein the rendering is of a volume data set representing values of the distinguishing function.  
   
   
       7 . The method according to  claim 1 , wherein at least one of the one or more characterizing parameters is a function of surrounding voxels.  
   
   
       8 . The method of  claim 1 , further comprising further classifying voxels on the basis of the morphology of their respective classifications in the medical image data set.  
   
   
       9 . The method of  claim 1 , wherein the distinguishing function is determined by computing the characterizing parameters for the selected voxels and taking as the distinguishing function the value of at least one characterizing parameter whose value depends on whether its associated voxel has been positively or negatively selected.  
   
   
       10 . The method of  claim 1 , wherein voxels are classified as either corresponding to the tissue type of interest or not corresponding to the tissue type of interest.  
   
   
       11 . The method of  claim 10 , further comprising rendering an image of the medical image data set, wherein the rendering takes account of the classification of voxels, and displaying the image to a user.  
   
   
       12 . The method of  claim 11 , wherein voxels classified as not corresponding to the tissue type of interest are rendered as transparent.  
   
   
       13 . The method of  claim 11 , wherein voxels classified as corresponding to the tissue type of interest are rendered as transparent.  
   
   
       14 . The method of  claim 11 , wherein voxels classified as corresponding to the tissue type of interest are rendered in one range of displayable colors and voxels classified as not corresponding to the tissue type of interest are rendered in another range of displayable colors.  
   
   
       15 . The method of  claim 1 , wherein voxels are classified by associating with them a probability that they correspond to the tissue type of interest.  
   
   
       16 . The method of  claim 15 , further comprising rendering an image of the medical image data set, wherein the rendering takes account of the classification of voxels, and displaying the image to a user.  
   
   
       17 . The method of  claim 16 , wherein the rendering takes account of the classification by rendering a volume data set representing the probability that the voxels correspond to the tissue type of interest.  
   
   
       18 . The method of  claim 17 , wherein voxels having a probability of corresponding to the tissue type of interest of less than a threshold level are rendered as transparent.  
   
   
       19 . The method of  claim 18 , further comprising adjusting the threshold level and re-rendering the image.  
   
   
       20 . The method of  claim 17 , wherein a pre-determined fraction of the voxels having the lowest probabilities of corresponding to the tissue type of interest are rendered as transparent.  
   
   
       21 . The method of  claim 1 , wherein the user input includes clinical information regarding at least one of the positively and negatively selected voxels, and wherein the distinguishing function is determined from the characterizing parameters having regard to the clinical information.  
   
   
       22 . The method of  claim 21 , wherein the clinical information specifies tissue type.  
   
   
       23 . The method of  claim 1 , wherein the medical image data set comprises a data set representing the probability that the voxels correspond to a tissue type of interest determined in a previous iteration of the method.  
   
   
       24 . The method of  claim 1 , wherein the user input is prompted by displaying an image to a user from a 3D data set comprising a plurality of voxels, each with an associated signal value.  
   
   
       25 . The method of  claim 24 , wherein the displaying of the image to the user comprises: 
 selecting a volume of interest (VOI) within the 3D data set;    generating a histogram of signal values from voxels that are within the VOI;    applying a numerical analysis method to the histogram to determine a visualization threshold; and    setting at least one of a plurality of boundaries for a visualization parameter according to the visualization threshold.    
   
   
       26 . A computer program product bearing computer readable instructions for performing the method of  claim 1 .  
   
   
       27 . A computer apparatus loaded with computer readable instructions for performing the method of  claim 1 .  
   
   
       28 . Apparatus for numerically processing a medical image data set comprising voxels, the apparatus comprising: 
 (a) storage from which a medical image data set may be retrieved;    (b) a user input device configured to receive user input to positively and negatively select voxels that are and are not of a tissue type of interest; and    (c) a processor configured to determine a distinguishing function that discriminates between the positively and negatively selected voxels on the basis of one or more characterizing parameters of the voxels; and to classify further voxels in the medical image data set on the basis of the distinguishing function.