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.

Description

BACKGROUND OF THE INVENTION [0001] The invention relates to the setting of visualization parameter boundaries, such as color and opacity boundaries, for displaying images, in particular two-dimensional (2D) projections from three-dimensional (3D) data sets. [0002] When displaying an image, such as in medical imaging applications, it is known to associate particular signal values with particular colors and opacities (known as visualization parameters) to assist visualization. This mapping is done when using data from a 3D data set (voxel data set) to compute a 2D data set (pixel data set) representing a 2D projection of the voxel data set for display on a computer screen or other conventional 2D display apparatus. This process is known as rendering. [0003] The 2D data set is more amenable to user interpretation if different colors and opacities are allocated to different signal values in the 3D data set. The details of the mapping of signal values to colors and opacities are stored i...

AI Technical Summary

Benefits of technology

[0026] By receiving input in response to a user specifying both positive examples of voxels (i.e. those which do correspond to the tissue type of interest) and negative examples of voxels (i.e. those which do not correspond to the tissue type of interest), the method is able to objectively classify further voxels in the data set. Because of this, the method provides for an easy and intuitive to use technique for allowing users to select regions of interest for further examination or removal from the data set.

[0027] The method may include presenting a representative (2D) image derived from the (3D) medical image data set to a user, such as a sagittal, coronal or transverse section view, whereby the user selects voxels by positioning a pointer at appropriate locations in the example image. An example voxel may then be taken to be a voxel whose coordinates in the medical image data set map to the location of the pointer in the example image. Alternatively, for a single positioning of the pointer, a number of example voxels may be selected, for example those in a region surrounding a voxel whose coordinates in the data set map to the location of the pointer in the example image may be taken as being selected. Selecting multiple voxels with a single positioning of the cursor allows for a more statistically significant sample of example voxels to be provided with little additional user input.

[0028] At least one of the one or more characterizing parameters of a voxel may be a function of surrounding voxels. For example, a local average, a local standard deviation, gradient magnitude, Laplacian, minimum value, maximum value or any other parameterization may be used. This allows voxels to be classified on the basis of characteristics of their surroundings, rather than simply on the basis of their voxel value. This means that similar tissue types can be properly classified more accurately than with conventional classification methods based on voxel value alone. This is because subtle difference in “texture” in the vicinity of a voxel can help to distinguish it from other voxels having otherwise similar voxel values. It is also noted that for some modalities such as MR there may be multiple voxel values, such as T1 and T2 in multi-spectral MR, which could each be used to define a separate characterizing parameter. These could be used collectively in combination to set the distinguishing function.

Problems solved by technology

It can therefore be a difficult and laborious task to produce a displayed image that is clinically useful.

Furthermore, there is inevitably an element of user-subjectivity in manually defining a color table and this can create difficulties in comparing and interpreting images created by different users, or even supposedly similar images created by a single user.

A color table chosen for application to one particular projected image will not necessarily be appropriate to another projection of the same 3D data set.

Images