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Segmentation of SBFSEM volume data of neural tissue by hierarchical classification
- in Pattern Recognition, Gerhard Rigoll, Ed. 2008
"... Abstract. Three-dimensional electron-microscopic image stacks with almost isotropic resolution allow, for the first time, to determine the com-plete connection matrix of parts of the brain. In spite of major advances in staining, correct segmentation of these stacks remains challenging, be-cause ver ..."
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Abstract. Three-dimensional electron-microscopic image stacks with almost isotropic resolution allow, for the first time, to determine the com-plete connection matrix of parts of the brain. In spite of major advances in staining, correct segmentation of these stacks remains challenging, be-cause very few local mistakes can lead to severe global errors. We propose a hierarchical segmentation procedure based on statistical learning and topology-preserving grouping. Edge probability maps are computed by a random forest classifier (trained on hand-labeled data) and partitioned into supervoxels by the watershed transform. Over-segmentation is then resolved by another random forest. Careful validation shows that the results of our algorithm are close to human labelings. 1
Annotated Contraction Kernels for Interactive Image Segmentation
"... Abstract. This article shows how the interactive segmentation tool termed “Active Paintbrush ” and a fully automatic region merging can both be based on the theoretical framework of contraction kernels within irregular pyramids instead of their own, specialized data structures. We introduce “contino ..."
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Abstract. This article shows how the interactive segmentation tool termed “Active Paintbrush ” and a fully automatic region merging can both be based on the theoretical framework of contraction kernels within irregular pyramids instead of their own, specialized data structures. We introduce “continous pyramids ” in which we purposely drop the common requirement of a fixed reduction factor between successive levels, and we show how contraction kernels can be annotated for a fast navigation of such pyramids. Finally, we use these concepts for improving the integration of the automatic region merging and the interactive tool. 1
Automated Segmentation of Large 3D Images of Nervous Systems Using a Higher-order Graphical Model
, 2011
"... This thesis presents a new mathematical model for segmenting volume images (Chapter 3). The model is an energy function defined on the state space of all possibilities to remove or preserve splitting faces from an initial over-segmentation of the 3D image into supervoxels. It decomposes into potenti ..."
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This thesis presents a new mathematical model for segmenting volume images (Chapter 3). The model is an energy function defined on the state space of all possibilities to remove or preserve splitting faces from an initial over-segmentation of the 3D image into supervoxels. It decomposes into potential functions that are learned automatically from a small amount of empirical training data. The learning is based on features of the distribu-tion of gray values in the volume image and on features of the geometry and topology of the supervoxel segmentation. To be able to extract these features from large 3D images that consist of several billion voxels, a new algorithm is presented in Chapter 4 that constructs a suitable representation of the geometry and topology of volume segmentations in a block-wise fashion, in log-linear runtime (in the number of voxels) and in parallel, using only a prescribed amount of memory. At the core of this thesis is the optimization problem of finding, for a
IMAGE SEGMENTATION WITH THE EXACT WATERSHED TRANSFORM
"... Discrete algorithms for low-level boundary detection are geometrically inaccurate and topologically unreliable. Corresponding continuous methods are often more accurate and need fewer or no heuristics. Thus, we transfer discrete boundary indicators into a continuous form by means of differentiable s ..."
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Discrete algorithms for low-level boundary detection are geometrically inaccurate and topologically unreliable. Corresponding continuous methods are often more accurate and need fewer or no heuristics. Thus, we transfer discrete boundary indicators into a continuous form by means of differentiable spline interpolation and detect boundaries using the exact watershed transform. We demonstrate that this significantly improves the obtained segmentations.
IMAGE SEGMENTATION WITH THE EXACT WATERSHED TRANSFORM
"... Discrete algorithms for low-level boundary detection are geometrically inaccurate and topologically unreliable. Corresponding continuous methods are often more accurate and need fewer or no heuristics. Thus, we transfer discrete boundary indicators into a continuous form by means of differentiable s ..."
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Discrete algorithms for low-level boundary detection are geometrically inaccurate and topologically unreliable. Corresponding continuous methods are often more accurate and need fewer or no heuristics. Thus, we transfer discrete boundary indicators into a continuous form by means of differentiable spline interpolation and detect boundaries using the exact watershed transform. We demonstrate that this significantly improves the obtained segmentations.
Image Segmentation With The Exact Watershed Transform
, 2005
"... Discrete algorithms for low-level boundary detection are geometrically inaccurate and topologically unreliable. Corresponding continuous methods are often more accurate and need fewer or no heuristics. Thus, we transfer discrete boundary indicators into a continuous form by means of differentiable s ..."
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Discrete algorithms for low-level boundary detection are geometrically inaccurate and topologically unreliable. Corresponding continuous methods are often more accurate and need fewer or no heuristics. Thus, we transfer discrete boundary indicators into a continuous form by means of differentiable spline interpolation and detect boundaries using the exact watershed transform. We demonstrate that this significantly improves the obtained segmentations.
Interactive Hierarchical Image Segmentation on Irregular Pyramids
, 2013
"... Image segmentation, in general, is the process of dividing a digital image into segments having a strong correlation with objects in it. Various techniques exist to locate objects of interest formed by visual cues. However, general purpose segmentation methods cannot produce a perfect final segmenta ..."
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Image segmentation, in general, is the process of dividing a digital image into segments having a strong correlation with objects in it. Various techniques exist to locate objects of interest formed by visual cues. However, general purpose segmentation methods cannot produce a perfect final segmentation by using low-level cues only. A way round the problem is rather to create a stack of segmentations with different resolution levels. Higher level knowledge shall then be used to confirm or select regions for further processing. In automatic region-based segmentation, usually such a stack of segmentations is built in a bottom-up manner, guided by low-level image feature data and the defined homogeneity criteria. We should take into account as well that the accuracy of an image segmentation is measurable, but its quality and usability are highly subjective and depend also on the scope of the application. This thesis deals with modifications of such an irregular image segmentation pyramid and embedding additional knowledge about the problem domain such that the results of the image segmentation best suit the user. Based on an existing automatic segmentation framework – where the minimum spanning tree based method tries to capture perceptually important groupings – we
