The present invention relates to the registration of images, that is to say the process in which two different images are compared to find how they match each other, and are then displayed superimposed one on the other.
The registration of different images (also often called fusion of images) is useful in a variety of fields. The images being compared and superimposed could be images of the same object acquired using different modalities, which thus show up different features of interest. The fact that different features of interest are shown by the two modalities is useful in itself, but the usefulness can be enhanced by displaying the two images in superimposition. Examples of this technique might be the fusion of an infrared image with a visible light image, for instance in a surveillance, mapping or medical situation, or, particularly in the medical field, the combination of two different modality images such as magnetic resonance images, nuclear medicine images, x-ray images, ultrasound images etc. In general this fusion of different images assists the interpretation of the images.
In some situations the two images to be fused are taken at same time, or nearly the same time, but in other situations it is useful to fuse images taken at different times. For example, in the medical field it may be useful to fuse an image taken during one patient examination with an image taken in a different examination, for instance six months or a year spaced from the first one. This can assist in showing the changes in the patient's condition during that time. The fusion of time-separated images arises also in many other fields, such as surveillance and mapping.
A typical registration (or fusion) technique relies on identifying corresponding points in the two images and calculating a transformation which maps the pixels of one image to the pixels of another. This may use, for example, the well known block matching techniques in which pixels in a block in one image frame are compared with pixels in corresponding blocks in a search window in the other image frame and the transformation is calculated which minimises a similarity measure in the intensities in the blocks, such as the sum of square difference. Other techniques based on identification of corresponding shapes in the two images have also been proposed. Explanations of different registration techniques are found in, for example, U.S. Pat. No. 5,672,877 (ADAC Laboratories), U.S. Pat. No. 5,871,013 (Elscint Limited), and many other text books and published papers.
While such registration techniques are useful, the results can be regarded with suspicion by users. This is particularly true where the transformation which maps features in one image to features in the other involves not only a rigid movement, but also a non-rigid deformation of the image features. Users are typically prepared to accept the validity of a rigid movement, such as a translation and/or rotation, between two different images, but the validity of a shape deformation is much less clear.
The present invention provides an image registration, or fusion, method in which a confidence measure can also be displayed to the user to give the user an idea of the quality of registration. This confidence measure is calculated from the registration process. The measure of confidence can be, for example, the degree of transformation required to perform the mapping, and preferably be based on the degree of non-rigid deformation in the transformation. Thus the confidence measure may exclude rigid motions and represent only the magnitude of the local deformation. The measure can also be based on the local change of volume implied by the mapping transformation from one image to the other.
The measure may be selectively displayed in response to user input. It may be displayed as a visually distinguishable overlay on the display of the fused images. It may comprise a colour overlay with the colour or intensity (or both) indicating the measure of confidence, or the same could be achieved with a monochrome overlay whose grey level represents the measure. Alternatively, a symbol, such as a circle, can be displayed at any selected point in the fused image, whose size and/or shape, for instance the diameter of the circle, is measure of the confidence in the registration. Clearly a number or another symbol could be chosen and another attribute, e.g. colour, rather than size, used to indicate the confidence measure. Preferably, to avoid cluttering the display, the symbol is only displayed at a single point selected by the user, for example by setting the cursor at that position, possibly in response to the user “clicking” at the selected point on the screen.
However the confidence measure is displayed, it need not be on the fused image, but can be next to it, or in a separate display window, or on a copy of the fused image. For example an error bar or number corresponding to the confidence measure at the cursor position would be displayed alongside the fused image.
The method is particularly applicable to fused medical images, though it is also applicable in other fields where images are registered, such as surveillance, mapping etc.
The invention may conveniently be embodied as a computer program comprising program code means for executing the method, and the invention extends to a storage or transmission medium encoding the program and to an image processing and display apparatus which performs the method.
The invention will be further described by way of example with reference to the accompanying drawings, in which:—
As indicated in
(x1,y1,z1)=RIG(x2,y2,z2)+DEF(x2,y2,z2) (1)
The rigid part of the movement may be a translation and a rotation, namely:—
(x1y1,z1)=TRANS(x2,y2,z2)+ROT(x2,y2,z2)+DEF(x2,y2,z2) (3)
In accordance with this embodiment of the invention the size of the deformable part of the transformation DEF is regarded as a measure of the disagreement between the rigid registration (RIG) and the deformable registration (RIG+DEF). Thus a confidence measure M is calculated from the deformable part of the transformation. As one example the confidence measure M may be simply the magnitude (norm) of the local displacement. That is to say:—
M=|DEF(x2,y2,z2)| (4)
Alternatively, the confidence measure may be calculated as the determinant of the local Jacobian of the transformation, which defines the local stretching (change of volume) at a particular location. So if:—
x1=Fx(x2,y2,z2)
y1=Fy(x2,y2,z2)
z1=Fz(x2,y2,z2) (5)
Then the measure M becomes:—
It is also possible to base the measure on the value of the similarity function such as cross-correlation, mutual information, correlation ratio or the like (see for example A. Roche, X. Pennec, G. Malandain, and N. Ayache. Rigid Registration of 3D Ultrasound with MR Images: a New Approach Combining Intensity and Gradient Information. IEEE Transactions on Medical Imaging, 20(10):1038—1049, October 2001) used in matching local blocks in the images, or to combine these various measure together to form a normalised estimate of the “confidence” in the registration process.
Once the measure has been calculated it can be displayed over the fused image. One way of displaying it is, in response to the user “clicking” at a certain point on the display, to display a circle whose diameter represents the value of the confidence measure.
Number | Date | Country | Kind |
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0227887.7 | Nov 2002 | GB | national |
Filing Document | Filing Date | Country | Kind | 371c Date |
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PCT/GB03/04021 | 9/18/2003 | WO | 7/19/2004 |