The Experts below are selected from a list of 4674 Experts worldwide ranked by ideXlab platform

Ella A Kazerooni - One of the best experts on this subject based on the ideXlab platform.

  • automatic Seed Point identification and main artery segmentation for pulmonary vascular tree segmentation and tracking in computed tomographic pulmonary angiography ctpa
    Proceedings of SPIE, 2012
    Co-Authors: Chuan Zhou, Heang Ping Chan, Jean Kuriakose, Aamer Chughtai, Lubomir M Hadjiiski, Ella A Kazerooni
    Abstract:

    We are developing a computer-aided detection (CAD) system to assist radiologists in pulmonary embolism (PE) detection in computed tomographic pulmonary angiography (CTPA). Automatic segmentation and tracking of pulmonary vessels is a fundamental step to define the search space for PE detection. For automated tracking of pulmonary arteries, it is important to accurately identify the Seed Points to track the left and right pulmonary vessel trees. In this study, we developed an automatic Seed Point identification and pulmonary main artery (PMA) segmentation method. The Seed Point was derived from the bifurcation region where the pulmonary trunk artery splits into the left and right. A 3D recursive optimal path finding method (RPF) was developed to find the paths from the bifurcation Point to the end of the left and right PMAs. The PMAs were finally extracted along the PMA paths using morphological operation. Two and 18 CTPA cases was used for training and testing, respectively. A set of Points in the central luminal space of the PMA were manually marked as the "reference standard" by two experienced chest radiologists using a computer interface. A total of 3870 were marked in the test set. A voxel located on the computer-identified paths of the PMA was counted as a true PMA voxel when its distance to the closest reference standard Point is within a threshold. Our results show that 95.6% (17681/18502) and 88.8% (16439/18502) of computer identified PMA path Points were within a distance of 10 mm and 8 mm to the closest reference Point, respectively, and 100% (18/18) of the Seed Points were detected in the bifurcation region. 2.7% (104/3870) of the reference standard Points were not contained in the computer segmented vessels and counted as false negative Points.

  • Medical Imaging: Computer-Aided Diagnosis - Automatic Seed Point identification and main artery segmentation for pulmonary vascular tree segmentation and tracking in computed tomographic pulmonary angiography (CTPA)
    Proceedings of SPIE, 2012
    Co-Authors: Chuan Zhou, Heang Ping Chan, Jean Kuriakose, Aamer Chughtai, Lubomir M Hadjiiski, Ella A Kazerooni
    Abstract:

    We are developing a computer-aided detection (CAD) system to assist radiologists in pulmonary embolism (PE) detection in computed tomographic pulmonary angiography (CTPA). Automatic segmentation and tracking of pulmonary vessels is a fundamental step to define the search space for PE detection. For automated tracking of pulmonary arteries, it is important to accurately identify the Seed Points to track the left and right pulmonary vessel trees. In this study, we developed an automatic Seed Point identification and pulmonary main artery (PMA) segmentation method. The Seed Point was derived from the bifurcation region where the pulmonary trunk artery splits into the left and right. A 3D recursive optimal path finding method (RPF) was developed to find the paths from the bifurcation Point to the end of the left and right PMAs. The PMAs were finally extracted along the PMA paths using morphological operation. Two and 18 CTPA cases was used for training and testing, respectively. A set of Points in the central luminal space of the PMA were manually marked as the "reference standard" by two experienced chest radiologists using a computer interface. A total of 3870 were marked in the test set. A voxel located on the computer-identified paths of the PMA was counted as a true PMA voxel when its distance to the closest reference standard Point is within a threshold. Our results show that 95.6% (17681/18502) and 88.8% (16439/18502) of computer identified PMA path Points were within a distance of 10 mm and 8 mm to the closest reference Point, respectively, and 100% (18/18) of the Seed Points were detected in the bifurcation region. 2.7% (104/3870) of the reference standard Points were not contained in the computer segmented vessels and counted as false negative Points.

Farida Cheriet - One of the best experts on this subject based on the ideXlab platform.

  • ICPR - Geodesic Thin Plate Splines for Image Segmentation
    2010 20th International Conference on Pattern Recognition, 2010
    Co-Authors: Herve Lombaert, Farida Cheriet
    Abstract:

    Thin Plate Splines are often used in image registration to model deformations. Its physical analogy involves a thin lying sheet of metal that is deformed and forced to pass through a set of control Points. The Thin Plate Spline equation minimizes that thin plate bending energy. Rather than using Euclidean distances between control Points for image deformation, we are using geodesic distances for image segmentation. Control Points become Seed Points and force the thin plate to pass through given heights. Intuitively, the thin plate surface in the vicinity of a Seed Point within a region should have similar heights. The minimally bended thin plate actually gives a "confidence" map telling what the closest Seed Point is for every surface Point. The Thin Plate Spline has a closed-form solution which is fast to compute and global optimal. This method shows comparable results to the Graph Cuts method.

  • Geodesic Thin Plate Splines for Image Segmentation
    2010 20th International Conference on Pattern Recognition, 2010
    Co-Authors: Herve Lombaert, Farida Cheriet
    Abstract:

    Thin Plate Splines are often used in image registration to model deformations. Its physical analogy involves a thin lying sheet of metal that is deformed and forced to pass through a set of control Points. The Thin Plate Spline equation minimizes that thin plate bending energy. Rather than using Euclidean distances between control Points for image deformation, we are using geodesic distances for image segmentation. Control Points become Seed Points and force the thin plate to pass through given heights. Intuitively, the thin plate surface in the vicinity of a Seed Point within a region should have similar heights. The minimally bended thin plate actually gives a "confidence" map telling what the closest Seed Point is for every surface Point. The Thin Plate Spline has a closed-form solution which is fast to compute and global optimal. This method shows comparable results to the Graph Cuts method.

Jonathan D. Clayden - One of the best experts on this subject based on the ideXlab platform.

  • Tract shape modelling provides evidence of topological change in corpus callosum genu during normal ageing.
    NeuroImage, 2008
    Co-Authors: Mark E. Bastin, Jakub Przemyslaw Piatkowski, Amos Storkey, Laura J. E. Brown, Alasdair M.j. Maclullich, Jonathan D. Clayden
    Abstract:

    Understanding how ageing affects brain structure is an important challenge for medical science. By allowing segmentation of fasciculi-of-interest from diffusion magnetic resonance imaging (dMRI) data, tractography provides a promising tool for assessing white matter connectivity in old age. However, the output from tractography algorithms is usually strongly dependent on the subjective location of user-specified Seed Points, with the result that it can be both difficult and time consuming to identify the same tract reliably in cross-sectional studies. Here we investigate whether a novel method for automatic single Seed Point placement based on tract shape modelling, termed probabilistic model-based neighbourhood tractography (PNT), can reliably segment the same tract from subject to subject in a non-demented cohort aged over 65 years. For the fasciculi investigated (genu and splenium of corpus callosum, cingulum cingulate gyri, corticospinal tracts and uncinate fasciculi), PNT was able to provide anatomically plausible representations of the tract in question in 70 to 90% of subjects compared with 2.5 to 60% if single Seed Points were simply transferred directly from standard to native space. In corpus callosum genu there was a significant negative correlation between a PNT-derived measure of tract shape similarity to a young brain reference tract and age, and a trend towards a significant negative correlation between tract-averaged fractional anisotropy and age; results that are consistent with previous dMRI studies of normal ageing. These data show that it is possible automatically to segment comparable tracts in the brains of older subjects using single Seed Point tractography, if the Seed Point is carefully chosen.

  • Improved segmentation reproducibility in group tractography using a quantitative tract similarity measure.
    NeuroImage, 2006
    Co-Authors: Jonathan D. Clayden, Mark E. Bastin, Amos J. Storkey
    Abstract:

    Abstract The field of tractography is rapidly developing, and many automatic or semiautomatic algorithms have now been devised to segment and visualize neural white matter fasciculi in vivo . However, these algorithms typically need to be given a starting location as input, and their output can be strongly dependent on the exact location of this “Seed Point”. No robust method has yet been devised for placing these Seed Points so as to segment a comparable tract in a group of subjects. Here, we develop a measure of tract similarity , based on the shapes and lengths of the two tracts being compared, and apply it to the problem of consistent Seed Point placement and tract segmentation in group data. We demonstrate that using a single Seed Point transferred from standard space to each native space produces considerable variability in tractography output between scans. However, by Seeding in a group of nearby candidate Points and choosing the output with the greatest similarity to a reference tract chosen in advance–a method we refer to as neighborhood tractography –this variability can be significantly reduced.

Juntong Xi - One of the best experts on this subject based on the ideXlab platform.

  • efficient background segmentation and Seed Point generation for a single shot stereo system
    Sensors, 2017
    Co-Authors: Xiao Yang, Xiaobo Chen, Juntong Xi
    Abstract:

    Single-shot stereo 3D shape measurement is becoming more popular due to its advantages of noise robustness and short acquisition period. One of the key problems is stereo matching, which is related to the efficiency of background segmentation and Seed Point generation, etc. In this paper, a more efficient and automated matching algorithm based on digital image correlation (DIC) is proposed. The standard deviation of image gradients and an adaptive threshold are employed to segment the background. Scale-invariant feature transform (SIFT)-based feature matching and two-dimensional triangulation are combined to estimate accurate initial parameters for Seed Point generation. The efficiency of background segmentation and Seed Point generation, as well as the measuring precision, are evaluated by experimental simulation and real tests. Experimental results show that the average segmentation time for an image with a resolution of 1280 × 960 pixels is 240 milliseconds. The efficiency of Seed Point generation is verified to be high with different convergence criteria.

Amos J. Storkey - One of the best experts on this subject based on the ideXlab platform.

  • Improved segmentation reproducibility in group tractography using a quantitative tract similarity measure.
    NeuroImage, 2006
    Co-Authors: Jonathan D. Clayden, Mark E. Bastin, Amos J. Storkey
    Abstract:

    Abstract The field of tractography is rapidly developing, and many automatic or semiautomatic algorithms have now been devised to segment and visualize neural white matter fasciculi in vivo . However, these algorithms typically need to be given a starting location as input, and their output can be strongly dependent on the exact location of this “Seed Point”. No robust method has yet been devised for placing these Seed Points so as to segment a comparable tract in a group of subjects. Here, we develop a measure of tract similarity , based on the shapes and lengths of the two tracts being compared, and apply it to the problem of consistent Seed Point placement and tract segmentation in group data. We demonstrate that using a single Seed Point transferred from standard space to each native space produces considerable variability in tractography output between scans. However, by Seeding in a group of nearby candidate Points and choosing the output with the greatest similarity to a reference tract chosen in advance–a method we refer to as neighborhood tractography –this variability can be significantly reduced.

  • Improved segmentation reproducibility in group tractography using a quantitative tract similarity measure
    NEUROIMAGE, 2006
    Co-Authors: Amos J. Storkey
    Abstract:

    The field of tractography is rapidly developing, and many automatic or semiautomatic algorithms have now been devised to segment and visualize neural white matter fasciculi in vivo. However, these algorithms typically need to be given a starting location as input, and their output can be strongly dependent on the exact location of this "Seed Point". No robust method has yet been devised for placing these Seed Points so as to segment a comparable tract in a group of subjects. Here, we develop a measure of tract similarity, based on the shapes and lengths of the two tracts being compared, and apply it to the problem of consistent Seed Point placement and tract segmentation in group data. We demonstrate that using a single Seed Point transferred from standard space to each native space produces considerable variability in tractography output between scans. However, by Seeding in a group of nearby candidate Points and choosing the output with the greatest similarity to a reference tract chosen in advance-a method we refer to as neighborhood tractography-this variability can be significantly reduced. (c) 2006 Elsevier Inc. All rights reserved.