Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534063
J. Qian, T. Mitsa, E. Hoffman
Describes a new approach of surface/contour registration based on a physically deformable model. No prior knowledge about the types of geometric transformation is required for registration. Instead, the authors' approach views the surface as made of elastic material that will change shape in response to the applied external force. The registration of two surfaces/contours is the deformation process of one shape towards the other governed by physical laws. Before the deformation, the two shapes are roughly registered with a global affine transformation. The physically deformable model is then applied to deform one shape to match the other. The point correspondences between the two shapes are established when one shape is finally deformed to the other. In the 2D case, the model is similar to the active contour model but registration is formulated as an equilibrium problem instead of minimization problem. The result is a set of decoupled linear system equations that are easy to solve. It is also shown that, because of physical constraints imposed the authors' model is an improved version of Burr's (1981) dynamic contour model. Experimental results are presented to demonstrate the performance of the model.
{"title":"Contour/surface registration using a physically deformable model","authors":"J. Qian, T. Mitsa, E. Hoffman","doi":"10.1109/MMBIA.1996.534063","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534063","url":null,"abstract":"Describes a new approach of surface/contour registration based on a physically deformable model. No prior knowledge about the types of geometric transformation is required for registration. Instead, the authors' approach views the surface as made of elastic material that will change shape in response to the applied external force. The registration of two surfaces/contours is the deformation process of one shape towards the other governed by physical laws. Before the deformation, the two shapes are roughly registered with a global affine transformation. The physically deformable model is then applied to deform one shape to match the other. The point correspondences between the two shapes are established when one shape is finally deformed to the other. In the 2D case, the model is similar to the active contour model but registration is formulated as an equilibrium problem instead of minimization problem. The result is a set of decoupled linear system equations that are easy to solve. It is also shown that, because of physical constraints imposed the authors' model is an improved version of Burr's (1981) dynamic contour model. Experimental results are presented to demonstrate the performance of the model.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128430644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534061
C. Davatzikos
A key issue in several brain imaging applications, including computer aided neurosurgery, functional image analysis, and morphometrics, is the spatial normalization and registration of tomographic images from different subjects. This paper proposes a technique for spatial normalization of brain images based on elastically deformable models. In the authors' approach they use a deformable surface algorithm to find a parametric representation of the outer cortical surface and then use this representation to obtain a map between corresponding regions of the outer cortex in two different images. Based on the resulting map, the authors then derive a three-dimensional elastic warping transformation which brings two images in register. This transformation models images as inhomogeneous elastic objects which are deformed into registration with each other by external force fields. The elastic properties of the images can vary from one region to the other, allowing more variable brain regions, such as the ventricles, to deform more freely than less variable ones. Finally, the authors use prestrained elasticity to model structural irregularities, and in particular the ventricular expansion occurring with aging or diseases. The performance of the authors' algorithm is demonstrated on magnetic resonance images.
{"title":"Nonlinear registration of brain images using deformable models","authors":"C. Davatzikos","doi":"10.1109/MMBIA.1996.534061","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534061","url":null,"abstract":"A key issue in several brain imaging applications, including computer aided neurosurgery, functional image analysis, and morphometrics, is the spatial normalization and registration of tomographic images from different subjects. This paper proposes a technique for spatial normalization of brain images based on elastically deformable models. In the authors' approach they use a deformable surface algorithm to find a parametric representation of the outer cortical surface and then use this representation to obtain a map between corresponding regions of the outer cortex in two different images. Based on the resulting map, the authors then derive a three-dimensional elastic warping transformation which brings two images in register. This transformation models images as inhomogeneous elastic objects which are deformed into registration with each other by external force fields. The elastic properties of the images can vary from one region to the other, allowing more variable brain regions, such as the ventricles, to deform more freely than less variable ones. Finally, the authors use prestrained elasticity to model structural irregularities, and in particular the ventricular expansion occurring with aging or diseases. The performance of the authors' algorithm is demonstrated on magnetic resonance images.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130778940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534077
A. Chakraborty, L. Staib, J. Duncan
The wide availability of three-dimensional medical images has made their direct analysis a necessity. Accurately segmenting and quantifying structures is a key issue for such images. Conventional gradient-based surface finding however often suffers from a variety of limitations. This paper proposes a surface finding approach that uses in addition to gradient information, region information. This makes the resulting procedure more robust to noise and improper initialization. It uses Gauss's Divergence theorem to find the surface of of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based surface finder. Experimental results show that indeed, as expected, a significant improvement is achieved as a consequence of the use of this extra information. Further these improvements are achieved with little increase in computational overhead, an advantage derived from the application of Gauss's Divergence theorem.
{"title":"An integrated approach for surface finding in medical images","authors":"A. Chakraborty, L. Staib, J. Duncan","doi":"10.1109/MMBIA.1996.534077","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534077","url":null,"abstract":"The wide availability of three-dimensional medical images has made their direct analysis a necessity. Accurately segmenting and quantifying structures is a key issue for such images. Conventional gradient-based surface finding however often suffers from a variety of limitations. This paper proposes a surface finding approach that uses in addition to gradient information, region information. This makes the resulting procedure more robust to noise and improper initialization. It uses Gauss's Divergence theorem to find the surface of of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based surface finder. Experimental results show that indeed, as expected, a significant improvement is achieved as a consequence of the use of this extra information. Further these improvements are achieved with little increase in computational overhead, an advantage derived from the application of Gauss's Divergence theorem.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129521919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534079
B. Mair, D.C. Wilson, Z. Réti
In 1995 Z. Reti presented a method for deblurring images blurred by the discrete Gaussian. The method is based on theorems borrowed from analytic number theory developed by Gauss, G. Jacobi (1829), and Ramanujan. One advantage of this method over similar ones developed for the continuous domain is that it provides exact formulas for the deblurring convolution. In addition, while deblurring the Gaussian in the continuous domain is an ill-posed inverse problem, deblurring the discrete Gaussian model results in a mathematically well-posed problem. The formulas presented here provide error bounds which relate the quality of the reconstructed image to that of the blurred image. This deblurring method is conveniently expressed in terms of multiplication by Toeplitz matrices whose diagonal entries decrease exponentially, thus rendering the method suitable for numerical approximations. Condition numbers are provided for various choices of /spl sigma/.
{"title":"Deblurring the discrete Gaussian blur","authors":"B. Mair, D.C. Wilson, Z. Réti","doi":"10.1109/MMBIA.1996.534079","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534079","url":null,"abstract":"In 1995 Z. Reti presented a method for deblurring images blurred by the discrete Gaussian. The method is based on theorems borrowed from analytic number theory developed by Gauss, G. Jacobi (1829), and Ramanujan. One advantage of this method over similar ones developed for the continuous domain is that it provides exact formulas for the deblurring convolution. In addition, while deblurring the Gaussian in the continuous domain is an ill-posed inverse problem, deblurring the discrete Gaussian model results in a mathematically well-posed problem. The formulas presented here provide error bounds which relate the quality of the reconstructed image to that of the blurred image. This deblurring method is conveniently expressed in terms of multiplication by Toeplitz matrices whose diagonal entries decrease exponentially, thus rendering the method suitable for numerical approximations. Condition numbers are provided for various choices of /spl sigma/.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125296313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534065
S. Aylward, E. Bullitt, S. Pizer, Dave H. Eberly
Introduces a technique for the automated description of tubular objects in 3D medical images. The goal of automated 3D object description is to extract a representation which consistently details the location, size, and structure of objects in 3D images using minimal user interaction. Such a representation provides a means by which objects can be classified, quantifiably evaluated, and registered. It also serves as a region of interest specification for visualization processes. The technique presented in this paper is suited for generating representations of 3D objects with nearly circular cross sections which have, possibly as a result of a global operation (e.g., blurring), intensity extrema near their centers. Such tubular objects commonly occur within human anatomy (e.g., vessels and selected bones). The medial axis of each of these objects is well approximated by its intensity ridge. The scales of the local maxima in medialness at all points along the ridge can be mapped to local width estimates. Together these measures capture the location, size, and structure of tubular objects. This paper covers the mathematical basis, the implementation issues, and the application of this technique to the extraction of vessels from 3D magnetic resonance angiographic images and bones from 3D X-ray computed tomographic images.
{"title":"Intensity ridge and widths for tubular object segmentation and description","authors":"S. Aylward, E. Bullitt, S. Pizer, Dave H. Eberly","doi":"10.1109/MMBIA.1996.534065","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534065","url":null,"abstract":"Introduces a technique for the automated description of tubular objects in 3D medical images. The goal of automated 3D object description is to extract a representation which consistently details the location, size, and structure of objects in 3D images using minimal user interaction. Such a representation provides a means by which objects can be classified, quantifiably evaluated, and registered. It also serves as a region of interest specification for visualization processes. The technique presented in this paper is suited for generating representations of 3D objects with nearly circular cross sections which have, possibly as a result of a global operation (e.g., blurring), intensity extrema near their centers. Such tubular objects commonly occur within human anatomy (e.g., vessels and selected bones). The medial axis of each of these objects is well approximated by its intensity ridge. The scales of the local maxima in medialness at all points along the ridge can be mapped to local width estimates. Together these measures capture the location, size, and structure of tubular objects. This paper covers the mathematical basis, the implementation issues, and the application of this technique to the extraction of vessels from 3D magnetic resonance angiographic images and bones from 3D X-ray computed tomographic images.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"320 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116429135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534073
J. Declerck, J. Feldmar, F. Betting, M. Goris
Single photon emission computed tomography (SPECT) imaging is used to assess the location or the extent of myocardial infarction or ischemia. A method is proposed to decrease the effect of operator variability and morphologically blind sampling in the quantification of scintigraphic myocardial perfusion studies. To effect this, the patient's myocardial images (target cases) are registered automatically over a template image, utilizing non-rigid transformations. The registration method is an adaptation of the Iterative Closest Point algorithm. Experiments have been conducted on a database including 40 pairs of images selected to obtain a group of image abnormalities and variability. Upon the successful clinical validation of this work, a reliable, operator independent method for the analysis and interpretation of myocardial perfusion scintigraphies will be available.
{"title":"Automatic registration and alignment on a template of cardiac stress and rest SPECT images","authors":"J. Declerck, J. Feldmar, F. Betting, M. Goris","doi":"10.1109/MMBIA.1996.534073","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534073","url":null,"abstract":"Single photon emission computed tomography (SPECT) imaging is used to assess the location or the extent of myocardial infarction or ischemia. A method is proposed to decrease the effect of operator variability and morphologically blind sampling in the quantification of scintigraphic myocardial perfusion studies. To effect this, the patient's myocardial images (target cases) are registered automatically over a template image, utilizing non-rigid transformations. The registration method is an adaptation of the Iterative Closest Point algorithm. Experiments have been conducted on a database including 40 pairs of images selected to obtain a group of image abnormalities and variability. Upon the successful clinical validation of this work, a reliable, operator independent method for the analysis and interpretation of myocardial perfusion scintigraphies will be available.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"44 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125678908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534060
J. Feldmar, G. Malandain, J. Declerck, N. Ayache
Presents a new registration and gain correction algorithm for 3D medical images. It is intensity based. The basic idea is to represent the images by 4D points (x/sub j/, y/sub j/, z/sub j/, i/sub j/) and to define a global energy function based on this representation. For minimization, the authors propose a technique which does not require to compute the derivatives of this criterion with respect to the parameters. It can be understood as an extension of the Iterative Closest Point algorithm (P. Besl and N. McKay, 1992; Z. Zhang, 1992) or as an application of the formalism proposed by L. Cohen (Use of auxiliary variables in computer vision problems. In Proceedings of the Fifth International Conference on Computer Vision (ICCV '95), Boston, June 1995). Two parameters allow one to have a coarse to fine strategy both for resolution and deformation. The authors' technique presents the advantage to minimize a well defined global criterion to deal with various classes of transformations (for example rigid, affine and volume spline), to be simple to implement and to be efficient in practice. Results on real brain and heart 3D images are presented to demonstrate the validity of the authors' approach.
{"title":"Extension of the ICP algorithm to non-rigid intensity-based registration of 3D volumes","authors":"J. Feldmar, G. Malandain, J. Declerck, N. Ayache","doi":"10.1109/MMBIA.1996.534060","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534060","url":null,"abstract":"Presents a new registration and gain correction algorithm for 3D medical images. It is intensity based. The basic idea is to represent the images by 4D points (x/sub j/, y/sub j/, z/sub j/, i/sub j/) and to define a global energy function based on this representation. For minimization, the authors propose a technique which does not require to compute the derivatives of this criterion with respect to the parameters. It can be understood as an extension of the Iterative Closest Point algorithm (P. Besl and N. McKay, 1992; Z. Zhang, 1992) or as an application of the formalism proposed by L. Cohen (Use of auxiliary variables in computer vision problems. In Proceedings of the Fifth International Conference on Computer Vision (ICCV '95), Boston, June 1995). Two parameters allow one to have a coarse to fine strategy both for resolution and deformation. The authors' technique presents the advantage to minimize a well defined global criterion to deal with various classes of transformations (for example rigid, affine and volume spline), to be simple to implement and to be efficient in practice. Results on real brain and heart 3D images are presented to demonstrate the validity of the authors' approach.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"75 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122175953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534083
G. Christensen, A. Kane, J. Marsh, M. Vannier
A new method for non-rigid registration of a normal infant CT head atlas with CT data of infants with abnormal skull shape is presented. An individualized atlas is synthesized by computing a volume transformation from the normal atlas to the target data set shape. This process begins rigidly by eliminating translation and rotation differences and proceeds non-rigidly to eliminate anatomical shape differences. Operator specified anatomical landmarks are used to find the initial rigid transformation. Non-rigid registration is achieved by constraining the transformation by the low frequency modes of vibration of a 3D linear-elastic solid while minimizing the squared intensity difference between atlas and target CT image volumes. Results are presented in which the CT atlas was transformed into the shape of several infants with various types of craniofacial deformities.
{"title":"Synthesis of an individualized cranial atlas with dysmorphic shape","authors":"G. Christensen, A. Kane, J. Marsh, M. Vannier","doi":"10.1109/MMBIA.1996.534083","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534083","url":null,"abstract":"A new method for non-rigid registration of a normal infant CT head atlas with CT data of infants with abnormal skull shape is presented. An individualized atlas is synthesized by computing a volume transformation from the normal atlas to the target data set shape. This process begins rigidly by eliminating translation and rotation differences and proceeds non-rigidly to eliminate anatomical shape differences. Operator specified anatomical landmarks are used to find the initial rigid transformation. Non-rigid registration is achieved by constraining the transformation by the low frequency modes of vibration of a 3D linear-elastic solid while minimizing the squared intensity difference between atlas and target CT image volumes. Results are presented in which the CT atlas was transformed into the shape of several infants with various types of craniofacial deformities.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131947316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534055
G. Ettinger, W. Grimson, M. Leventon, Ron Kikinis, V. Gugino, W. Cote, M. Karapelou, L. Aglio, M. Shenton, G. Potts, Eben Alexander
The authors describe a method for mapping the functional regions of the brain using a transcranial magnetic stimulation (TMS) device. This device, when placed on a subject's scalp, stimulates the underlying neurons by generating focused magnetic field pulses. A brain mapping is then generated by measuring responses of different motor and sensory functions to this stimulation. The key process in generating this mapping is the association of the 3D positions and orientations of the TMS probe on the scalp to a 3D brain reconstruction such as is feasible with a magnetic resonance image (MRI). The authors perform this matching process by (1) registering the subject's head position to an a priori MRI scan, (2) tracking the 3D position/orientation of the TMS probe, (3) transforming the TMS probe position/orientation to the MRI coordinate frame, and (4) tracking movements in the subject's head position to factor out any head motion. The resultant process generates a high resolution, accurate brain mapping which supports surgical planning, surgical guidance, neuroanatomy research, and psychiatric therapy. When compared to other functional imaging modalities, this approach exhibits much lower cost, greater portability, and more direct active control over the functional areas being studied.
{"title":"Non-invasive functional brain mapping using registered transcranial magnetic stimulation","authors":"G. Ettinger, W. Grimson, M. Leventon, Ron Kikinis, V. Gugino, W. Cote, M. Karapelou, L. Aglio, M. Shenton, G. Potts, Eben Alexander","doi":"10.1109/MMBIA.1996.534055","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534055","url":null,"abstract":"The authors describe a method for mapping the functional regions of the brain using a transcranial magnetic stimulation (TMS) device. This device, when placed on a subject's scalp, stimulates the underlying neurons by generating focused magnetic field pulses. A brain mapping is then generated by measuring responses of different motor and sensory functions to this stimulation. The key process in generating this mapping is the association of the 3D positions and orientations of the TMS probe on the scalp to a 3D brain reconstruction such as is feasible with a magnetic resonance image (MRI). The authors perform this matching process by (1) registering the subject's head position to an a priori MRI scan, (2) tracking the 3D position/orientation of the TMS probe, (3) transforming the TMS probe position/orientation to the MRI coordinate frame, and (4) tracking movements in the subject's head position to factor out any head motion. The resultant process generates a high resolution, accurate brain mapping which supports surgical planning, surgical guidance, neuroanatomy research, and psychiatric therapy. When compared to other functional imaging modalities, this approach exhibits much lower cost, greater portability, and more direct active control over the functional areas being studied.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125386362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1996-06-21DOI: 10.1109/MMBIA.1996.534068
C. Pisupati, L. Wolff, W. Mitzner, E. Zerhouni
Physiological measurements like branch angles, branch lengths, branch diameters and branch cross-sectional area of the 3-D pulmonary tree structures are clinically essential in evaluating the function of normal and diseased lung and during the breathing process. In order to facilitate these measurements and study relative structural changes, the 3-D lung tree volumes are reduced to a 3-D Euclidean straight line central axis tree. The central axis tree captures the branch topology and geometric features of the tree volume. Since matching 3-D tree volumes is complex, as they change in branch topology and geometry, the authors accomplish it by designing an efficient algorithm that matches their corresponding central axis trees. The algorithm takes two binary central axis trees T/sub 1/=(V/sub 1/,E/sub 1/,W/sub 1/) and T/sub 2/=(V/sub 2/,E/sub 2/,W/sub 2/), where W/sub 1/ and W/sub 2/ are set of tuples containing geometric attributes corresponding to the nodes in T/sub 1/ and T/sub 2/, as inputs and returns the one-to-one matching function f of nodes in T/sub 1/ to T/sub 2/ that preserves the tree topology and closely matches the geometric attributes of these trees, i.e. branch points, branch lengths, and branch angles between mapped nodes of T/sub 1/ and T/sub 2/. Since the topology match alone could result in many choices of the mapping function f, the authors prune these choices by incorporating constraints on the geometric attributes of nodes in T/sub 1/ and T/sub 2/. The authors design a linear time algorithm that matches the branch topology and geometric features of T/sub 1/ and T/sub 2/. The authors' algorithm produced accurate matchings on various airway data sets of a dog lung obtained from Computed Tomography under simulated breathing conditions. T/sub 1/ and T/sub 2/ are obtained by running a two-pass central axis algorithm on the tree volumes.
{"title":"Tracking 3-D pulmonary tree structures","authors":"C. Pisupati, L. Wolff, W. Mitzner, E. Zerhouni","doi":"10.1109/MMBIA.1996.534068","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534068","url":null,"abstract":"Physiological measurements like branch angles, branch lengths, branch diameters and branch cross-sectional area of the 3-D pulmonary tree structures are clinically essential in evaluating the function of normal and diseased lung and during the breathing process. In order to facilitate these measurements and study relative structural changes, the 3-D lung tree volumes are reduced to a 3-D Euclidean straight line central axis tree. The central axis tree captures the branch topology and geometric features of the tree volume. Since matching 3-D tree volumes is complex, as they change in branch topology and geometry, the authors accomplish it by designing an efficient algorithm that matches their corresponding central axis trees. The algorithm takes two binary central axis trees T/sub 1/=(V/sub 1/,E/sub 1/,W/sub 1/) and T/sub 2/=(V/sub 2/,E/sub 2/,W/sub 2/), where W/sub 1/ and W/sub 2/ are set of tuples containing geometric attributes corresponding to the nodes in T/sub 1/ and T/sub 2/, as inputs and returns the one-to-one matching function f of nodes in T/sub 1/ to T/sub 2/ that preserves the tree topology and closely matches the geometric attributes of these trees, i.e. branch points, branch lengths, and branch angles between mapped nodes of T/sub 1/ and T/sub 2/. Since the topology match alone could result in many choices of the mapping function f, the authors prune these choices by incorporating constraints on the geometric attributes of nodes in T/sub 1/ and T/sub 2/. The authors design a linear time algorithm that matches the branch topology and geometric features of T/sub 1/ and T/sub 2/. The authors' algorithm produced accurate matchings on various airway data sets of a dog lung obtained from Computed Tomography under simulated breathing conditions. T/sub 1/ and T/sub 2/ are obtained by running a two-pass central axis algorithm on the tree volumes.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116204690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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