A.C. Evans, S. Marrett, L. Collins, and T.M. Peters, "AnatomicalFunctional Correlative Analysis of the Human Brain Using Three Dimensional Imaging Systems," Proc. SPIE Medical Imaging III: Image Processing, R.H. Schneider, S.J. Dwyer III, and J.R. Gilbert, eds. Bellingham, Wash.: SPIE Press, 1989, pp. 264-274.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....methods, which are similar to the methods for object recognition, were proposed for the solution of this organ registration problem. These include, among many others, approximated leastsquares fit between a small number of markers [41] 42] singular value decomposition for matching point pairs [28], 45] high order polynomials for a least squares fit [56] 76] thin plate spline for registering intrinsic landmarks [16] or extrinsic markers [15] parametric correspondence [22] 70] chamfer maps [7] 17] 26] 46] 50] partial contour matching [68] moments and principal axes ....

A.C. Evans, S. Marrett, L. Collins, and T.M. Peters, "AnatomicalFunctional Correlative Analysis of the Human Brain Using Three Dimensional Imaging Systems," Proc. SPIE Medical Imaging III: Image Processing, R.H. Schneider, S.J. Dwyer III, and J.R. Gilbert, eds. Bellingham, Wash.: SPIE Press, 1989, pp. 264-274.

....it is monotonic in FRE 2 ; in the fteen years that followed, much work was done involving numerical simulations, some of which targeted the FRE problem (by authors unaware of Sibson s work) and some of which addressed the TRE problem, which was not addressed by Sibson. In 1989 Evans et al. [15] pointed out the importance of establishing the relationship among FLE, TRE, and the number, N of ducial points used in the registration, and they examined that relationship via computer simulation. In 1992 Mandava et al. 16] performed similar simulations as have others since: in 1992 Hill et ....

A. C. Evans, S. Marrett, D. L. Collins, and T. M. Peters, \Anatomical-functional correlative analysis of the human brain using three dimensional imaging systems", Medical Imaging III: Image Processing, vol. Proc. SPIE 1092, pp. 264-274, 1989.

....methods, which are similar to the methods for object recognition, were proposed for the solution of this organ registration problem. These include, among many others, approximated leastsquares fit between a small number of markers [41] 42] singular value decomposition for matching point pairs [28], 45] high order polynomials for a least squares fit [56] 76] thin plate spline for registering intrinsic landmarks [16] or extrinsic markers [15] parametric correspondence [22] 70] chamfer maps [7] 17] 26] 46] 50] partial contour matching [68] moments and principal axes ....

A.C. Evans, S. Marrett, L. Collins, and T.M. Peters, "AnatomicalFunctional Correlative Analysis of the Human Brain Using Three Dimensional Imaging Systems," Proc. SPIE Medical Imaging III: Image Processing, R.H. Schneider, S.J. Dwyer III, and J.R. Gilbert, eds. Bellingham, Wash.: SPIE Press, 1989, pp. 264-274.

....al. 1984) the relative distribution of CBF was measured in baseline and activation conditions. All subjects also had an MRI scan containing 64 2mm thick T 1 weighted multi slice spin echo images (T R = 550msec ; TE = 30msec) Using a volumetric image registration procedure described previously (Evans et al. 1989, 1991a) the MRI volume from each subject was aligned with the corresponding PET volume. An orthogonal coordinate frame was then established based on the anterior commissure posterior commissural (AC PC) line as identified in the MRI volume (Evans et al. 1992) These anatomical frame ....

Evans AC, Marrett S, Peters TM (1989): Anatomical-functional correlative analysis of the human brain using three-dimensional imaging systems. Proceedings of the International Society of Optical Engineering (SPIE): Medical Imaging III, 264-274.

....anatomy is essential [47] ROI methods, pixel by pixel and pixel clustering are used in analyses which associate the functional data with anatomical regions. ROIs can be used in this second sort of analysis by developing an ROI atlas which outlines areas corresponding to different brain structures [1, 3, 18, 19]. This approach requires that the PET data be registered onto the atlas in some way that ensures the accuracy of the fit. Differences in brain shape and size, and the fact that PET data contains no anatomical information makes this registration problem a difficult one [39] Most registration ....

....to the atlas (or vice versa) If great care is taken with the Chapter 2: Common Methods of Functional Analysis 13 patient positioning at scan time, this step can be simplified. A further step has been taken with the ROI atlas method, this is the development of volume of interest (VOI) atlases [19, 28]. These methods define 3D volumes, rather then 2D areas, which correspond to the anatomy of the brain, and is more in keeping with the 3D nature of the brain. In the case of anatomically based ROIs and VOIs, the shape of the region should reflect the underlying brain structure and the PET values ....

A.C. Evans, S. Marrett, L. Collins, and T. Peters. Anatomical-functional correlative analysis of the human brain using three dimensional imaging systems. In SPIE Medical Imaging III: Image Processing, volume 1092, pages 264--274, 1989.

.... are markers glued to the skin [19, 20, 21, 22, 23, 24, 13, 25, 26, 27, 28, 29, 30] but larger devices that can be fitted snugly to the patient, like individualized foam moulds, head holder frames, and dental adapters have also been used, although they are little reported on in recent literature [31, 32, 33, 34, 35, 19]. Since extrinsic methods by definition cannot include patient related image information, the nature of the registration transformation is often restricted to be rigid (translations and rotations only) Furthermore, if they are to be used with images of low (spatial) information content such as ....

....or directly onto measures computed from the image grey values (voxel property based) 3 4.2. 1 Landmark based registration methods Landmarks can be anatomical, i.e. salient and accurately locatable points of the morphology of the visible anatomy, usually identified interactively by the user [35, 19, 36, 37, 20, 38, 39, 40, 41, 42, 43, 44, 45, 46, 23, 47, 48, 49, 50, 6, 25, 26, 51, 52, 53, 27, 54, 55, 56, 57, 28, 8, 58, 59, 60, 61, 62, 63, 9, 64],orgeometrical, i.e. points at the locus of the optimum of some geometric property, e.g. local curvature extrema, corners, etc, generally localized in an automatic fashion [65, 66, 67, 68, 69, 70, 71, 72, 73, 74] Technically, the identification of landmark points is a segmentation procedure, ....

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A. C. Evans, S. Marrett, L. Collins, and T. M. Peters. Anatomical-functional correlative analysis of the human brain using three dimensional imaging systems. In R.H. Schneider, S.J. Dwyer III, and R.G. Jost, editors, Medical imaging: image processing, volume 1092, pages 264--274, Bellingham, WA, 1989. SPIE press.

....surveys [59, 82, 101, 107, 108] 17 Point Methods Point methods involve the determination of the coordinates of corresponding points in different images and or physical space and the estimation of the geometric transformation using these corresponding points. The points may be either intrinsic [16, 45, 49, 55, 79, 86, 87, 88, 111, 119, 124, 133, 134, 139, 171], extrinsic [3, 14, 30, 47, 49, 56, 58, 60, 79, 80, 81, 105, 114, 116, 135, 136, 149, 151, 163, 164, 178, 188, 189, 190, 198] or a combination of both [117, 118] Intrinsic points are derived from patient specific image properties, e.g. anatomical landmark points. Extrinsic points are derived from ....

....e.g. anatomical landmark points. Extrinsic points are derived from artificially applied markers, e.g. glass beads. Selection of appropriate points is a key factor in the success of a registration using intrinsic points. Anatomic landmark selection is a labor intensive, interactive process [45, 49, 86, 87, 88]. Points must be easily defined in three dimensions and must correspond to a well defined landmark visible in both imaging modalities. Hill et al. suggest several possibilities: a point anatomical structure, e.g. the apical turn of the cochlea; the intersection of two linear structures, e.g. a ....

[Article contains additional citation context not shown here]

A. C. Evans, S. Marrett, D. L. Collins, and T. M. Peters. Anatomical-functional correlative analysis of the human brain using three dimensional imaging systems. Medical Imaging III: Image Processing, Proc. SPIE 1092:264--274, 1989.

....of a symmetric matrix and required that X t Y be nonsingular, a restriction not required for the SVD solution. III. Error Statistics In the field of medical image registration the importance of the error statistics of point based rigidbody registration was recognized by Evans as early as 1989 [40], 21] and has since been considered by many others [12] 41] 4] 22] 42] 43] 1] Here the researchers in image registration were unaware of earlier, related work on the Procrustes problem. In 1979 [23] Sibson in a study of scaling theory first considered the effect of localization error ....

....a bad choice of fiducial configuration, and to the surgeon having a false sense of security about the accuracy of the surgical guidance system. It has long been known that better point based registrations, i.e. smaller TREs, can be obtained by using more fiducial points to guide the registration [40], 21] 12] 41] 22] More specifically, based on numerical simulations it has been conjectured that TRE has an approximate N Gamma1=2 dependence, given that points are added in some consistent way, such as choosing them randomly within or on the surface of a specified region, a sphere, for ....

A. C. Evans, S. Marrett, D. L. Collins, and T. M. Peters, "Anatomical-functional correlative analysis of the human brain using three dimensional imaging systems", Medical Imaging III: Image Processing, vol. Proc. SPIE 1092, pp. 264--274, 1989.

....methods, which are similar to the methods for object recognition, were proposed for the solution of this organ registration problem. These include, among many others, approximated least squares fit between a small number of markers [41, 42] singular value decomposition for matching point pairs [28, 45], high order polynomials for a least squares fit [56, 76] thin plate spline for registering intrinsic landmarks [16] or extrinsic markers [15] parametric correspondence [22, 70] chamfer maps [7, 17, 26, 46, 50] partial contour matching [68] moments and principal axes matching [3, 37, 38, ....

A.C. Evans, S. Marrett, L. Collins, and T.M. Peters, "Anatomical-functional correlative analysis of the human brain using three dimensional imaging systems," in Proc. SPIE Medical Imaging III: Image Processing, R.H. Schneider, S.J. Dwyer III, and J.R. Gilbert, eds. Bellingham, WA: 1092, SPIE Press, 1989, pp. 264--274.

No context found.

A. C. Evans, S. Marrett, L. Collins, and T. M. Peters, "Anatomical-functional correlative analysis of the human brain using three dimensional imaging systems," in Medical imaging: image processing, R. Schneider, S. Dwyer III, and R. Jost, eds., vol. 1092, pp. 264--274, SPIE press, (Bellingham, WA), 1989.

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