Segmentation of corpus callosum using diffusion tensor imaging: validation in patients with glioblastoma

Abstract Background This paper presents a three-dimensional (3D) method for segmenting corpus callosum in normal subjects and brain cancer patients with glioblastoma. Methods Nineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. In this algorithm, diffusion pattern of corpus callosum was used as prior information. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the proposed segmentation method in such cases. Results Dice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations. Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results. Conclusions The proposed method and similarity measure segment corpus callosum by propagating a hyper-surface inside the structure (resulting in high sensitivity), without penetrating into neighboring fiber bundles (resulting in high specificity)

Geodesic tractography segmentation for directional medical image analysis

Acknowledgements page removed per author's request, 01/06/2014.Geodesic Tractography Segmentation is the two component approach presented in this thesis for the analysis of imagery in oriented domains, with emphasis on the application to diffusion-weighted magnetic resonance imagery (DW-MRI). The computeraided analysis of DW-MRI data presents a new set of problems and opportunities for the application of mathematical and computer vision techniques. The goal is to develop a set of tools that enable clinicians to better understand DW-MRI data and ultimately shed new light on biological processes. This thesis presents a few techniques and tools which may be used to automatically find and segment major neural fiber bundles from DW-MRI data. For each technique, we provide a brief overview of the advantages and limitations of our approach relative to other available approaches.Ph.D.Committee Chair: Tannenbaum, Allen; Committee Member: Barnes, Christopher F.; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Vela, Patrici

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

In this PhD Thesis proposal, the principles of diffusion MRI (dMRI) in its application to the human brain mapping of connectivity are reviewed. The background section covers the fundamentals of dMRI, with special focus on those related to the distortions caused by susceptibility inhomogeneity across tissues. Also, a deep survey of available correction methodologies for this common artifact of dMRI is presented. Two methodological approaches to improved correction are introduced. Finally, the PhD proposal describes its objectives, the research plan, and the necessary resources

Blood Vessel Segmentation and shape analysis for quantification of Coronary Artery Stenosis in CT Angiography

This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm

Blood vessel segmentation and shape analysis for quantification of coronary artery stenosis in CT angiography

This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Diffusion Tensor Imaging Segmentation By Watershed Transform On Tensorial Morphological Gradient

While scalar image segmentation has been studied extensively, diffusion tensor imaging (DTI) segmentation is a relatively new and challenging task. Either existent segmentation methods have to be adapted to deal with tensorial information or completely new segmentation methods have to be developed to accomplish this task. Alternatively, what this work proposes is the computation of a tensorial morphological gradient of DTI, and its segmentation by IFT-based watershed transform. The strength of the proposed segmentation method is its simplicity and robustness, consequences of the tensorial morphological gradient computation. It enables the use, not only of well known algorithms and tools from the mathematical morphology, but also of any other segmentation method to segment DTI, since the computation of the tensorial morphological gradient transforms tensorial images in scalar ones. In order to validate the proposed method, synthetic diffusion tensor fields were generated, and Gaussian noise was added to them. A set of real DTI was also used in the method validation. All segmentation results confirmed that the proposed method is capable to segment different diffusion tensor images, including noisy and real ones. © 2008 IEEE.196203Alexander, D., Gee, J., Bajcsy, R., Similarity measures for matching diffusion tensor images (1999) Brit. Mach. Vision ConfAwate, S., Gee, J., A fuzzy, nonparametric segmentation framework for dti and mri analysis (2007) IPMI, pp. 296-307Basser, P., Pierpaoli, C., Microstructural and physiological features of tissues elucidated by quantitative-diffusiontensor mri (1996) J. Magn. Reson, 111 (3), pp. 209-219. , JuneBishop, R.L., Goldberg, S.I., (1980) Tensor Analysis on Manifolds, , DoverCampbell, J.S., Siddiqi, K., Rymar, V.V., Sadikot, A.F., Pike, G.B., Flow-based fiber tracking with diffusion tensor and q-ball data: Validation and comparison to principal diffusion direction techniques (2005) Neuroimage, 27 (4), pp. 725-736. , OctoberD. A. Danielson. Vectors and Tensors in Engineering and Physics. Westview (Perseus), 2003Jones, A.S.D.K., Horsfield, M.A., Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging (1999) Magn. Reson. Medic, 42 (3), pp. 515-525Dougherty, E.R., Lotufo, R.A., (2003) Hands-on Morphological Image Processing, TT59. , SPIEFalcão, A., Cunha, B., Lotufo, R., Design of connected operators using the image foresting transform (2001) Medical Imaging 2001: Image Processing, volume 4322 of SPIE Conference, pp. 468-479. , JulyFalcão, A., Stolfi, J., Lotufo, R., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Trans. Pattern Anal. Mach. Intell, 26 (1), pp. 19-29. , JanFreidlin, R.Z., Özarslan, E., Komlosh, M.E., Chang, L.-C., Koay, C.G., Jones, D.K., Basser, P.J., Parsimonious model selection for tissue segmentation and classification applications: A study using simulated and experimental dti data (2007) IEEE Trans. Med. Imag, 26 (11), pp. 1576-1584Heijmans, H.J.A.M., (1994) Morphological Image Operators, , Academic Press, BostonJonasson, L., Hagmann, P., Bresson, X., Meuli, R., Cuisenaire, O., Thiran, J.-P., White matter mapping in dt-mri using geometric flows (2003) Proc. 9th Intern. Workshop Comput. Aided Syst. Theory, pp. 80-82. , Spain, FebruaryLotufo, R., Falcão, A., The Ordered Queue and the Optimality of the Watershed Approaches (2000) 5th International Symposium on Mathematical Morphology, pp. 341-350. , Palo Alto CA, USA, June, Kluwer AcademicLotufo, R., Falcão, A., Zampirolli, F., IFT-watershed from gray-scale marker (2002) XV Brazilian Symp. on Computer Graph. and Image Proc, pp. 146-152. , Fortaleza, Brazil, Oct, IEEE PressPierpaoli, C., Basser, P.J., Toward a quantitative assessment of diffusion anisotropy (1996) Magn. Reson. Medic, 36 (6), pp. 893-906Rittner, L., Flores, F., Lotufo, R., New tensorial representation of color images: Tensorial morphological gradient applied to color image segmentation (2007) XX Brazilian Symp. on Computer Graph, and Image Proc, pp. 45-52. , Belo Horizonte, Brazil, IEEE PressM. Rousson, C. Lenglet, and R. Deriche. Level set and region based surface propagation for diffusion tensor mri segmentation. In M. Sonka, I. A. Kakadiaris, and J. Kybic, editors, ECCV Workshops CVAMIA and MMBIA, 3117 of Lect. Notes Comp. Sei., pages 123-134. Springer, 2004Wang, Z., Vemuri, B., Dti segmentation using an information theoretic tensor dissimilarity measure (2005) IEEE Trans. Med. ImagWeldeselassie, Y., Hamarneh, G., Dt-mri segmentation using graph cuts (2007) Medical Imaging 2007: Image Processing, , SPIEWiegell, M., Tuch, D., Larson, H., Wedeen, V., Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging (2003) NeuroImage, 19, pp. 391-402Zhukov, L., Museth, K., Breen, D., Whitaker, R., Barr, A., Level set modeling and segmentation of dt-mri brain data (2003) J. Electronic Imaging, 12, pp. 125-133Ziyan, U., Tuch, D., Westin, C., Segmentation of thalamic nuclei from DTI using spectral clustering (2006) MICCAI'06, pp. 807-814. , Lect. Notes Comp. Sci, Denmar