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Multi-Classifier Framework for Atlas-Based Image Segmentation
- Pattern
, 2004
"... We develop and evaluate in this paper a multi-classifier framework for atlas-based segmentation, a popular segmentation method in biomedical image analysis. An atlas is a spatial map of classes (e.g., anatomical structures), which is usually derived from a reference individual by manual segmentation ..."
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Cited by 22 (5 self)
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We develop and evaluate in this paper a multi-classifier framework for atlas-based segmentation, a popular segmentation method in biomedical image analysis. An atlas is a spatial map of classes (e.g., anatomical structures), which is usually derived from a reference individual by manual segmentation. An atlas-based classification is generated by registering an image to an atlas, that is, by computing a semantically correct coordinate mapping between the two. In the present paper, the registration algorithm is an intensitybased non-rigid method that computes a free-form deformation (FFD) defined on a uniform grid of control points. The transformation is regularized by a weighted smoothness constraint term. Different atlases, as well as different parameterizations of the registration algorithm, lead to different and somewhat independent atlas-based classifiers. The outputs of these classifiers can be combined in order to improve overall classification accuracy. In an evaluation study, biomedical images from seven subjects are segmented 1) using three individual atlases; 2) using one atlas and three different resolutions of the FFD control point grid; 3) using one atlas and three different regularization constraint weights. In each case, the three individual segmentations are combined by Sum Rule fusion. For each individual and for each combined segmentation, its recognition rate (relative number of correctly labeled image voxels) is computed against a manual gold-standard segmentation. In all cases, classifier combination consistently improved classification accuracy. The biggest improvement was achieved using multiple atlases, a smaller gain resulted from multiple regularization constraint weights, and a marginal gain resulted from multiple control point spacings. We con...
Braingazer - visual queries for neurobiology research
- IEEE Transactions on Visualization and Computer Graphics
"... Fig. 1: Neural projections in the brain of the fruit fly visualized using the BrainGazer system. Abstract — Neurobiology investigates how anatomical and physiological relationships in the nervous system mediate behavior. Molecular genetic techniques, applied to species such as the common fruit fly D ..."
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Cited by 14 (4 self)
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Fig. 1: Neural projections in the brain of the fruit fly visualized using the BrainGazer system. Abstract — Neurobiology investigates how anatomical and physiological relationships in the nervous system mediate behavior. Molecular genetic techniques, applied to species such as the common fruit fly Drosophila melanogaster, have proven to be an important tool in this research. Large databases of transgenic specimens are being built and need to be analyzed to establish models of neural information processing. In this paper we present an approach for the exploration and analysis of neural circuits based on such a database. We have designed and implemented BrainGazer, a system which integrates visualization techniques for volume data acquired through confocal microscopy as well as annotated anatomical structures with an intuitive approach for accessing the available information. We focus on the ability to visually query the data based on semantic as well as spatial relationships. Additionally, we present visualization techniques for the concurrent depiction of neurobiological volume data and geometric objects which aim to reduce visual clutter. The described system is the result of an ongoing interdisciplinary collaboration between neurobiologists and visualization researchers. Index Terms—Biomedical visualization, neurobiology, visual queries, volume visualization. 1
Shape-Based Averaging
"... Abstract—A new method for averaging multidimensional images is presented, which is based on signed Euclidean distance maps computed for each of the pixel values. We refer to the algorithm as “shape-based averaging ” (SBA) because of its similarity to Raya and Udupa’s shape-based interpolation method ..."
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Cited by 13 (0 self)
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Abstract—A new method for averaging multidimensional images is presented, which is based on signed Euclidean distance maps computed for each of the pixel values. We refer to the algorithm as “shape-based averaging ” (SBA) because of its similarity to Raya and Udupa’s shape-based interpolation method. The new method does not introduce pixel intensities that were not present in the input data, which makes it suitable for averaging nonnumerical data such as label maps (segmentations). Using segmented human brain magnetic resonance images, SBA is compared to label voting for the purpose of averaging image segmentations in a multiclassifier fashion. SBA, on average, performed as well as label voting in terms of recognition rates of the averaged segmentations. SBA produced more regular and contiguous structures with less fragmentation than did label voting. SBA also was more robust for small numbers of atlases and for low atlas resolutions, in particular, when combined with shape-based interpolation. We conclude that SBA improves the contiguity and accuracy of averaged image segmentations. Index Terms—Combination of segmentations, shape-based averaging (SBA), shape-based interpolation (SBI), signed Euclidean distance transform. I.
Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable
"... Abstract—The accuracy of nonrigid image registrations is commonly approximated using surrogate measures such as tissue label overlap scores, image similarity, image difference, or transformation inverse consistency error. This paper provides experimental evidence that these measures, even when used ..."
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Cited by 12 (0 self)
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Abstract—The accuracy of nonrigid image registrations is commonly approximated using surrogate measures such as tissue label overlap scores, image similarity, image difference, or transformation inverse consistency error. This paper provides experimental evidence that these measures, even when used in combination, cannot distinguish accurate from inaccurate registrations. To this end, we introduce a “registration ” algorithm that generates highly inaccurate image transformations, yet performs extremely well in terms of the surrogate measures. Of the tested criteria, only overlap scores of localized anatomical regions reliably distinguish reasonable from inaccurate registrations, whereas image similarity and tissue overlap do not. We conclude that tissue overlap and image similarity, whether used alone or together, do not provide valid evidence for accurate registrations and should thus not be reported or accepted as such. Index Terms—Nonrigid image registration, registration accuracy, unreliable surrogates, validation. I.
AN EFFICIENT NUMERICAL METHOD FOR THE SOLUTION OF THE L2 OPTIMAL MASS TRANSFER PROBLEM
, 2010
"... In this paper we present a new computationally efficient numerical scheme for the minimizing flow approach for the computation of the optimal L2 mass transport mapping. In contrast to the integration of a time dependent partial differential equation proposed in [S. Angenent, S. Haker, and A. Tannen ..."
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In this paper we present a new computationally efficient numerical scheme for the minimizing flow approach for the computation of the optimal L2 mass transport mapping. In contrast to the integration of a time dependent partial differential equation proposed in [S. Angenent, S. Haker, and A. Tannenbaum, SIAM J. Math. Anal., 35 (2003), pp. 61–97], we employ in the present work a direct variational method. The efficacy of the approach is demonstrated on both real and synthetic data.
Information Fusion in Biomedical Image Analysis: Combination of Data vs. Combination of Interpretations
"... Abstract. Information fusion has, in the form of multiple classifier systems, long been a successful tool in pattern recognition applications. It is also becoming increasingly popular in biomedical image analysis, for example in computer-aided diagnosis and in image segmentation. In this paper, we e ..."
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Cited by 8 (0 self)
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Abstract. Information fusion has, in the form of multiple classifier systems, long been a successful tool in pattern recognition applications. It is also becoming increasingly popular in biomedical image analysis, for example in computer-aided diagnosis and in image segmentation. In this paper, we extend the principles of multiple classifier systems by considering information fusion of classifier inputs rather than on their outputs, as is usually done. We introduce the distinction between combination of data (i.e., classifier inputs) vs. combination of interpretations (i.e., classifier outputs). We illustrate the two levels of information fusion using four different biomedical image analysis applications that can be implemented using fusion of either data or interpretations: atlas-based image segmentation, “average image ” tissue classification, multi-spectral classification, and deformation-based group morphometry. 1
Grid-Enabled Non-Rigid Registration Of Medical Images
, 2004
"... Over recent years, non-rigid registration has become a major issue in medical imaging. ..."
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Cited by 7 (4 self)
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Over recent years, non-rigid registration has become a major issue in medical imaging.
The Impact of Atlas Formation Methods on Atlas-Guided Brain Segmentation,” MICCAI, 2007. RR n° 6837 version 3 - 8 Apr 2009 Unité de recherche INRIA Futurs Parc Club Orsay Université - ZAC des Vignes 4, rue Jacques Monod - 91893 ORSAY Cedex (France) Unité
- INRIA Rennes : IRISA, Campus universitaire de Beaulieu - 35042 Rennes Cedex (France) Unité de recherche INRIA Rhône-Alpes : 655, avenue de l’Europe - 38334 Montbonnot Saint-Ismier (France) Unité de recherche INRIA Rocquencourt : Domaine de Voluceau - Rocq
"... Abstract. We analyze the impact of atlas construction within the context of an atlas-guided segmenter applied to a morphometry study in neuroanatomy. Auto-matic segmenters often rely on anatomical information encoded via probabilis-tic atlases. These atlases are frequently constructed by registering ..."
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Cited by 7 (2 self)
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Abstract. We analyze the impact of atlas construction within the context of an atlas-guided segmenter applied to a morphometry study in neuroanatomy. Auto-matic segmenters often rely on anatomical information encoded via probabilis-tic atlases. These atlases are frequently constructed by registering collections of training data. In this paper, we study the impact of registration methods as well as the training data on automatic segmentation results. With respect to registra-tion, we focus our comparison on pairwise vs. group-wise methods and fixed vs. online coordinate systems. For the training data, we consider collections of pop-ulation specific and general population data. To study the impact of these factors, we revisit a previously published statistical group comparison that was based on manual segmentations. For each atlas type, we record the group differences based on automatic segmentations and compare these findings to the original ones. Fur-thermore, we measure the Dice overlap between manual and automatic segmen-tations. Our results indicate some advantages for coordinate systems that are de-veloped in an online fashion. 1
Lung Nodule Growth Analysis from 3D CT Data with a Coupled Segmentation and Registration Framework
"... In this paper we propose a new framework to simultaneously segment and register lung and tumor in serial CT data. Our method assumes nonrigid transformation on lung deformation and rigid structure on the tumor. We use the B-Spline-based nonrigid transformation to model the lung deformation while imp ..."
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In this paper we propose a new framework to simultaneously segment and register lung and tumor in serial CT data. Our method assumes nonrigid transformation on lung deformation and rigid structure on the tumor. We use the B-Spline-based nonrigid transformation to model the lung deformation while imposing rigid transformation on the tumor to preserve the volume and the shape of the tumor. In particular, we set the control points within the tumor to form a control mesh and thus assume the tumor region follows the same rigid transformation as the control mesh. For segmentation, we apply a 2D graph-cut algorithm on the 3D lung and tumor datasets. By iteratively performing segmentation and registration, our method achieves highly accurate segmentation and registration on serial CT data. Finally, since our method eliminates the possible volume variations of the tumor during registration, we can further estimate accurately the tumor growth, an important evidence in lung cancer diagnosis. Initial experiments on five sets of patients’ serial CT data show that our method is robust and reliable. 1.
