Image analysis – Image transformation or preprocessing – Changing the image coordinates
Reexamination Certificate
1999-07-26
2001-07-24
Patel, Jayanti K. (Department: 2721)
Image analysis
Image transformation or preprocessing
Changing the image coordinates
C382S131000
Reexamination Certificate
active
06266453
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to systems and methods for aligning image data volumes. More specifically, the present invention relates to aligning three-dimensional image data volumes.
BACKGROUND OF THE INVENTION
In the medical treatment area, it is usually necessary to utilize a number of modalities to view the internal anatomy of the person to be treated. These modalities include X-ray imaging, Magnetic Resonance Imaging (“MRI”), and computed tomography (“CT”) imaging. There also are other modalities such as functional MRI (“fMRI”), single photon emission computed tomography (“SPECT”), and positron emission tomography (“PET”), all of whose images contain physiologic or metabolic information depicting the actions of living tissue.
It is known that each of modalities has certain strengths and weaknesses in the images that it produces. For example, X-radiography (“X-ray”) imaging has high spatial and intensity resolutions, shows bony anatomy with high detail, and is relatively inexpensive to use; but X-ray also presents the viewer with complex two-dimensional (“2-D”) views of superimposed anatomy. X-radiography also has difficulty resolving soft tissue features. MRI has the advantage of displaying three-dimensional (“3-D”) images of soft tissues with high contrast and high spatial resolution, but does not image bone well. CT imagery, based on X-ray absorption, produces 3-D pictures of bony anatomy, and, increasingly, good definition of soft tissue, although MRI remains the preferred modality for viewing soft tissue. However, if the correct modalities are selected and aligned, the resulting combined image data will provide a more complete representation of the internal anatomy of the patient. Given this, the issue is to identify a system and method that will align image data from two modalities very accurately in a reasonable amount of time.
Image alignment, which is part of the science of image fusion, has been used since at least World War II when time course aerial photography was used to support strategic bombing. Blink comparator machines were used to superimpose two images taken at different times by displaying them to the viewer in rapid succession. As these images were being displayed in this manner, their features were matched to quantify the bombing damage.
This same basic comparison technique has been used to superimpose images produced by remote sensors, for example aerial or satellite sensors, but with the refinement of using computer-based methods to align images produced at different times, or at the same time by different channels of the sensor. These images were digital images. The techniques just described for image alignment in remote sensors were reported in Castleman, K. R., “Digital Image Processing”
Prentice
-
Hall.
Englewood Cliffs, N.J., 1979, and Moik, J. G., “Digital Processing of Remotely Sensed Images,”
NASA SP
-431, Washington, D.C., 1980.
The use of computers to effect image alignment became a necessity as the number of images produced by LANDSAT, SPOT, and other satellite systems rapidly rew and there was the need to perform nonlinear warping transforms to match the images taken from different sky-to-ground perspectives. The use of computers also became a necessity to effect alignment of brain tissue images so that useful and effective neurophysiological research could be conducted. This was reported in Hibbard, L, et al.,
Science,
236:1641-1646, 1987. Reviews of various computed alignment techniques have been reported in Brown, L. G., “A Survey of Image Registration Techniques,”
ACM Computing Surveys,
24: 325-376, 1992 and Van den Elsen, P.A., et aL, “Medical Image Matching—A Review with Classification,”
IEEE Engineering in Medicine and Biology,
March 1993, pp. 26-39.
Image fusion of images from at least two modalities is currently being used in radiation onocology because it has been found to provide better tumor definition, which was reported in Rosenman, J. G., et aL, “Image Registration: An Essential Part of Radiation Therapy Treatment Planning,”
International Journal of Radiation Onocology, Biology, and Physics,
40:197-205, 1998. It is anticipated that there will be increased development of software tools for the use in radiation treatment planning (“RTP”) to effect image alignment and display/contouring tools to process and use the fused images that are produced.
Image alignment methods generally fall in two categories: manual alignment by direct inspection of the images being aligned, and automatic alignment by computing a solution to a numerical problem with some type of computer program. Manual alignment (“MA”) is carried out visually by matching corresponding features of two images. MA may be implemented in all RTP systems that offer or purport to offer image fusion The mechanism that most frequently is used in MA is the visual placement of fiducial points or markers from which a transformation is derived, for example, by a least-square minimization of the differences between corresponding landmark points. A method of implementing MA that is not as frequently used is the visual matching of objects imbedded directly in the images being aligned.
MA is usually carried out using a graphical user interface (“UGU”). In most cases, the GUI simultaneously displays the axial, sagittal, and coronal plane views of an area of interesl The GUI must provide efficient navigation of 3-D data, and precise localization of fiducial markers or other tools for alignment or measurement Heretofore, commercial systems have used UNIX workstations or special-purpose computers to achieve the high computation and graphics throughput needed to interactively display large data volumes
Automated image alignment (“AIA”) methods involve obtaining a transformation through computation of the properties of the images to be registered. This may even take place through programmed computer actions without user intervention. Many of the more successful AIA methods are based on correlation and moment invariants matching.
Correlation methods, more accurately, involve the cross-correlation or cross-covariance of image pixel intensities. These methods produce robust and accurate alignments of images. Methods of this type have been reported in Anuta, P. E., “Spatial Registration on Multispectral and Multitemporal Digital Imagery Using Fast Fourier Transform Techniques,”
IEEE Transactions on Geoscience Electronics,
8:353-368, 1970; Bamea, D. I., et al., “A Class Of Algorithms For Fast Digital Image Registration,”
IEEE Transactions on Computers,
21:179-186, 1972; and Pratt, W. K., “Correlation Techniques of Image registration,”
IEEE Transactions on Aerospace and Electronic Systems,
10:353-358, 1974.
According to most correlation methods, the location of the correlation peak will correspond directly to the translation needed to align images that already have a correct rotational alignment. The rotational alignment may have been obtained by correlating the images after resampling them on a polar grid according to the method reported in Hibbard, L, et al.,
Science
236:1641-1646, 1987. This rotational alignment method depends on the use of a fast Fourier transform. Correlation methods are accurate and robust to noise and differences in image content as long as the images are not too different. Accordingly, correlation methods are most often used for the alignment of images of the same kind.
Moment invariants methods are computed methods for image registration. A example of these methods is reported in Jain, A. K., “Fundamentals of Digital Picture Processing,
Prentice
-
Hall
Englewood Cliffs, N.J., 1989. This moment invariants method involves the use of the principal moments computed from a two-dimensional (“2-D”) inertia matrix of some prominent object of the images. The principal moments correspond to a unique set of principal vectors. Image alignment is performed by transforming one image's principal vectors onto the other image's principal vectors. This usually is a fast calculation. However, the moment invariants method depends
Hibbard Lyn
Lane Derek G.
Computerized Medical Systems, Inc.
Hale and Dorr LLP
Patel Jayanti K.
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