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Three-dimensional Visualization and Analysis Methodologies: A Current Perspective¹

¹ From the Department of Radiology, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104-6021. Received April 21, 1998; revision requested May 21 and received December 14; accepted December 21. Address reprint requests to the author.

View larger version (15K): [in a new window]   Figure 1.  Schematic illustrates a typical 3D imaging system.

View larger version (28K): [in a new window]   Figure 2.  Drawing provides graphic representation of the basic terminology used in 3D imaging. abc = scanner coordinate system, rst = display coordinate system, uvw = object coordinate system, xyz = scene coordinate system.

View larger version (142K): [in a new window]   Figure 3.  Graded composition and hanging-togetherness. CT scan of the knee illustrates graded composition of intensities and hanging-togetherness. Voxels within the same object (eg, the femur) are assigned considerably different values. Despite this gradation of values, however, it is not difficult to identify the voxels as belonging to the same object (hanging-togetherness).

View larger version (102K): [in a new window]   Figure 4a.  Preprocessing with a volume of interest operation. (a) Head CT scan includes a specified region of interest (rectangle). (b) Histogram depicts the intensities of the scene designated in a and includes a specified intensity of interest. (c) Resulting image corresponds to the specified region of interest in a.

View larger version (53K): [in a new window]   Figure 4b.  Preprocessing with a volume of interest operation. (a) Head CT scan includes a specified region of interest (rectangle). (b) Histogram depicts the intensities of the scene designated in a and includes a specified intensity of interest. (c) Resulting image corresponds to the specified region of interest in a.

View larger version (118K): [in a new window]   Figure 4c.  Preprocessing with a volume of interest operation. (a) Head CT scan includes a specified region of interest (rectangle). (b) Histogram depicts the intensities of the scene designated in a and includes a specified intensity of interest. (c) Resulting image corresponds to the specified region of interest in a.

View larger version (91K): [in a new window]   Figure 5a.  Preprocessing with suppressing and enhancing filters. (a) Head CT scan illustrates the appearance of an image prior to filtering. (b) Same image as in a after application of a smoothing filter. Note that noise is suppressed in regions of uniform intensity, but edges are also blurred. (c) Same image as in a after application of an edge-enhancing filter. Note that regions of uniform intensity are unenhanced because the gradient in these regions is small. However, the boundaries (especially of skin and bone) are enhanced.

View larger version (96K): [in a new window]   Figure 5b.  Preprocessing with suppressing and enhancing filters. (a) Head CT scan illustrates the appearance of an image prior to filtering. (b) Same image as in a after application of a smoothing filter. Note that noise is suppressed in regions of uniform intensity, but edges are also blurred. (c) Same image as in a after application of an edge-enhancing filter. Note that regions of uniform intensity are unenhanced because the gradient in these regions is small. However, the boundaries (especially of skin and bone) are enhanced.

View larger version (101K): [in a new window]   Figure 5c.  Preprocessing with suppressing and enhancing filters. (a) Head CT scan illustrates the appearance of an image prior to filtering. (b) Same image as in a after application of a smoothing filter. Note that noise is suppressed in regions of uniform intensity, but edges are also blurred. (c) Same image as in a after application of an edge-enhancing filter. Note that regions of uniform intensity are unenhanced because the gradient in these regions is small. However, the boundaries (especially of skin and bone) are enhanced.

View larger version (146K): [in a new window]   Figure 6a.  Shape-based interpolation of a binary CT scene created by designating a threshold. (a) CT scene after shape-based interpolation at a "coarse" resolution and subsequent surface rendering. (b) The same scene after interpolation at a "fine" resolution and surface rendering.

View larger version (141K): [in a new window]   Figure 6b.  Shape-based interpolation of a binary CT scene created by designating a threshold. (a) CT scene after shape-based interpolation at a "coarse" resolution and subsequent surface rendering. (b) The same scene after interpolation at a "fine" resolution and surface rendering.

View larger version (37K): [in a new window]   Figure 7a.  Scene-based registration. (a) Three-dimensional scenes corresponding to proton-density (PD)–weighted MR images of the head obtained in a patient with multiple sclerosis demonstrate a typical "preregistration" appearance. The scenes were acquired at four different times. (b) Same scenes as in a after 3D registration. The progression of the disease (hyperintense lesions around the ventricles) is now readily apparent. At registration, the scenes were re-sectioned with a scene-based interpolation method to obtain sections at the same location.

View larger version (34K): [in a new window]   Figure 7b.  Scene-based registration. (a) Three-dimensional scenes corresponding to proton-density (PD)–weighted MR images of the head obtained in a patient with multiple sclerosis demonstrate a typical "preregistration" appearance. The scenes were acquired at four different times. (b) Same scenes as in a after 3D registration. The progression of the disease (hyperintense lesions around the ventricles) is now readily apparent. At registration, the scenes were re-sectioned with a scene-based interpolation method to obtain sections at the same location.

View larger version (70K): [in a new window]   Figure 8.  Rigid object-based registration. Sequence of 3D MR imaging scenes of the foot allows kinematic analysis of the midtarsal joints. The motion (ie, translation and rotation) of the talus, calcaneus, and navicular and cuboid bones from one position to the other is determined by registering the bone surfaces in the two different positions.

View larger version (98K): [in a new window]   Figure 9.  Fuzzy, boundary-based automatic segmentation. Rendition created with both intensity and gradient criteria shows the fuzzy boundaries of "bone" detected in the CT data from Figure 3.

View larger version (148K): [in a new window]   Figure 10.  Live-wire segmentation. Section created on the basis of data from an MR image of the foot shows a live-wire segment representing a portion of the boundary of interest, which in this case outlines the talus.

View larger version (114K): [in a new window]   Figure 11a.  Hard, region-based, automatic segmentation with use of thresholding. Once the desired scene is selected (a), an intensity interval is specified on a histogram (b). The segmented object is then depicted as a binary scene (c).

View larger version (75K): [in a new window]   Figure 11b.  Hard, region-based, automatic segmentation with use of thresholding. Once the desired scene is selected (a), an intensity interval is specified on a histogram (b). The segmented object is then depicted as a binary scene (c).

View larger version (63K): [in a new window]   Figure 11c.  Hard, region-based, automatic segmentation with use of thresholding. Once the desired scene is selected (a), an intensity interval is specified on a histogram (b). The segmented object is then depicted as a binary scene (c).

View larger version (83K): [in a new window]   Figure 12a.  Clustering. (a) Sections from an MR imaging scene with T2 (top) and PD (bottom) values assigned to voxels. (b) Scatter plot of the sections in a. A cluster outline for cerebrospinal fluid is indicated. (c) Segmented binary section demonstrates cerebrospinal fluid.

View larger version (63K): [in a new window]   Figure 12b.  Clustering. (a) Sections from an MR imaging scene with T2 (top) and PD (bottom) values assigned to voxels. (b) Scatter plot of the sections in a. A cluster outline for cerebrospinal fluid is indicated. (c) Segmented binary section demonstrates cerebrospinal fluid.

View larger version (60K): [in a new window]   Figure 12c.  Clustering. (a) Sections from an MR imaging scene with T2 (top) and PD (bottom) values assigned to voxels. (b) Scatter plot of the sections in a. A cluster outline for cerebrospinal fluid is indicated. (c) Segmented binary section demonstrates cerebrospinal fluid.

View larger version (7K): [in a new window]   Figure 13.  Diagram illustrates fuzzy thresholding.

View larger version (129K): [in a new window]   Figure 14.  Fuzzy thresholding. Rendition of CT data from Figure 3 with fuzzy thresholding depicts bone and soft tissue.

View larger version (123K): [in a new window]   Figure 15a.  Fuzzy connected segmentation. (a, b) Sections from an MR imaging scene with T2 (a) and PD (b) values assigned to voxels. (c–e) Sections created with 3D fuzzy connected segmentation demonstrate the union of white matter and gray matter objects (c), the cerebrospinal fluid object (d), and the union of multiple sclerosis lesions (e) detected from the scene in a and b.

View larger version (119K): [in a new window]   Figure 15b.  Fuzzy connected segmentation. (a, b) Sections from an MR imaging scene with T2 (a) and PD (b) values assigned to voxels. (c–e) Sections created with 3D fuzzy connected segmentation demonstrate the union of white matter and gray matter objects (c), the cerebrospinal fluid object (d), and the union of multiple sclerosis lesions (e) detected from the scene in a and b.

View larger version (120K): [in a new window]   Figure 15c.  Fuzzy connected segmentation. (a, b) Sections from an MR imaging scene with T2 (a) and PD (b) values assigned to voxels. (c–e) Sections created with 3D fuzzy connected segmentation demonstrate the union of white matter and gray matter objects (c), the cerebrospinal fluid object (d), and the union of multiple sclerosis lesions (e) detected from the scene in a and b.

View larger version (70K): [in a new window]   Figure 15d.  Fuzzy connected segmentation. (a, b) Sections from an MR imaging scene with T2 (a) and PD (b) values assigned to voxels. (c–e) Sections created with 3D fuzzy connected segmentation demonstrate the union of white matter and gray matter objects (c), the cerebrospinal fluid object (d), and the union of multiple sclerosis lesions (e) detected from the scene in a and b.

View larger version (35K): [in a new window]   Figure 15e.  Fuzzy connected segmentation. (a, b) Sections from an MR imaging scene with T2 (a) and PD (b) values assigned to voxels. (c–e) Sections created with 3D fuzzy connected segmentation demonstrate the union of white matter and gray matter objects (c), the cerebrospinal fluid object (d), and the union of multiple sclerosis lesions (e) detected from the scene in a and b.

View larger version (132K): [in a new window]   Figure 16a.  Fuzzy connected segmentation. (a) Three-dimensional maximum-intensity-projection (MIP) rendition of an MR angiography scene. (b) MIP rendition of the 3D fuzzy connected vessels detected from the scene in a. Fuzzy connectedness has been used to remove the clutter that obscures the vasculature.

View larger version (88K): [in a new window]   Figure 16b.  Fuzzy connected segmentation. (a) Three-dimensional maximum-intensity-projection (MIP) rendition of an MR angiography scene. (b) MIP rendition of the 3D fuzzy connected vessels detected from the scene in a. Fuzzy connectedness has been used to remove the clutter that obscures the vasculature.

View larger version (72K): [in a new window]   Figure 17.  Montage display of a 3D CT scene of the head.

View larger version (49K): [in a new window]   Figure 18. Figures 18, 19. (18) Three-dimensional display–guided extraction of an oblique section from CT data obtained in a patient with a craniofacial disorder. A plane is selected interactively by means of the 3D display to indicate the orientation of the section plane (left). The section corresponding to the oblique plane is shown on the right. (19) Pseudocolor display. (a) Head MR imaging sections obtained at different times are displayed in green and red, respectively. Where there is a match, the composite image appears yellow. Green and red areas indicate regions of mismatch. (b) On the same composite image displayed after 3D scene-based registration, green and red areas indicate either a registration error or a change in an object (eg, a lesion) over the time interval between the two acquisitions.

View larger version (115K): [in a new window]   Figure 19a. Figures 18, 19. (18) Three-dimensional display–guided extraction of an oblique section from CT data obtained in a patient with a craniofacial disorder. A plane is selected interactively by means of the 3D display to indicate the orientation of the section plane (left). The section corresponding to the oblique plane is shown on the right. (19) Pseudocolor display. (a) Head MR imaging sections obtained at different times are displayed in green and red, respectively. Where there is a match, the composite image appears yellow. Green and red areas indicate regions of mismatch. (b) On the same composite image displayed after 3D scene-based registration, green and red areas indicate either a registration error or a change in an object (eg, a lesion) over the time interval between the two acquisitions.

View larger version (104K): [in a new window]   Figure 19b. Figures 18, 19. (18) Three-dimensional display–guided extraction of an oblique section from CT data obtained in a patient with a craniofacial disorder. A plane is selected interactively by means of the 3D display to indicate the orientation of the section plane (left). The section corresponding to the oblique plane is shown on the right. (19) Pseudocolor display. (a) Head MR imaging sections obtained at different times are displayed in green and red, respectively. Where there is a match, the composite image appears yellow. Green and red areas indicate regions of mismatch. (b) On the same composite image displayed after 3D scene-based registration, green and red areas indicate either a registration error or a change in an object (eg, a lesion) over the time interval between the two acquisitions.

View larger version (12K): [in a new window]   Figure 20.  Schematic illustrates projection techniques for volume mode visualization. Projections are created for rendition either by ray casting from the viewing plane to the scene or by projecting voxels from the scene to the viewing plane.

View larger version (71K): [in a new window]   Figure 21.  Scene-based volume rendering with voxel projection. Rendition of knee CT data from Figure 3 shows bone, fat, and soft tissue.

View larger version (114K): [in a new window]   Figure 22a.  Object-based visualization of the skull in a child with agnathia. (a) Surface-rendered image. (b) Subsequent volume-rendered image was preceded by the acquisition of a fuzzy object representation with use of fuzzy thesholding (cf Fig 13).

View larger version (125K): [in a new window]   Figure 22b.  Object-based visualization of the skull in a child with agnathia. (a) Surface-rendered image. (b) Subsequent volume-rendered image was preceded by the acquisition of a fuzzy object representation with use of fuzzy thesholding (cf Fig 13).

View larger version (103K): [in a new window]   Figure 23a.  Visualization with volume rendering. (a) Object-based volume-rendered image demonstrates bone and soft-tissue structures (muscles) that had been detected earlier as separate fuzzy connected objects in a 3D craniofacial CT scene. The skin is essentially "peeled away" because of its weak connectedness to muscles. (b) Scene-based volume-rendered version of the scene in a was acquired with use of the opacity function (cf Fig 13) separately for bone and soft tissue. The skin has become indistinguishable from muscles because they have similar CT numbers and hence obscures the rendition of the muscles.

View larger version (118K): [in a new window]   Figure 23b.  Visualization with volume rendering. (a) Object-based volume-rendered image demonstrates bone and soft-tissue structures (muscles) that had been detected earlier as separate fuzzy connected objects in a 3D craniofacial CT scene. The skin is essentially "peeled away" because of its weak connectedness to muscles. (b) Scene-based volume-rendered version of the scene in a was acquired with use of the opacity function (cf Fig 13) separately for bone and soft tissue. The skin has become indistinguishable from muscles because they have similar CT numbers and hence obscures the rendition of the muscles.

View larger version (49K): [in a new window]   Figure 24.  Preprocessing and visualization operations. Renditions from CT data were created with use of five different preprocessing and visualization operations.

View larger version (83K): [in a new window]   Figure 25.  Rigid manipulation. Rendition created from CT data obtained in a child demonstrates rigid manipulation for use in surgical planning. This "virtual surgery" mimics an osteotomy procedure used in craniomaxillofacial surgery to advance the frontal bone.