Topological Skeletonization and Tree-Summarization of Neurons Using Discrete Morse Theory
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Neuroscientific data analysis has classically involved methods for statistical signal and image processing, drawing on linear algebra and stochastic process theory. However, digitized neuroanatomical data sets containing labelled neurons, either individually or in groups labelled by tracer injections, do not fully fit into this classical framework. The tree-like shapes of neurons cannot mathematically be adequately described as points in a vector space. There is therefore a need for new approaches. Methods from computational topology and geometry are naturally suited to the analysis of neuronal shapes. Here we introduce methods from Discrete Morse Theory to extract tree-skeletons of individual neurons from volumetric brain image data, or to summarize collections of neurons labelled by localized anterograde tracer injections. Since individual neurons are topologically trees, it is sensible to summarize the collection of neurons labelled by a localized anterograde tracer injection using a consensus tree-shape. The algorithmic procedure includes an initial pre-processing step to extract a density field from the raw volumetric image data, followed by initial skeleton extraction from the density field using a discrete version of a 1-(un)stable manifold of the density field. Heuristically, if the density field is regarded as a mountainous landscape, then the 1-(un)stable manifold follows the "mountain ridges" connecting the maxima of the density field. We then simplify this skeleton-graph into a tree using a shortest-path approach and methods derived from persistent homology. The advantage of this approach is that it uses global information about the density field and is therefore robust to local fluctuations and non-uniformly distributed input signals. To be able to handle large data sets, we use a divide-and-conquer approach. The resulting software DiMorSC is available on Github.Keywords:
Skeletonization
Tree (set theory)
Persistent Homology
Manifold (fluid mechanics)
This paper brings together three distinct theories with the goal of quantifying shape textures with complex morphologies. Distance fields are central objects in shape representation, while topological data analysis uses algebraic topology to characterize geometric and topological patterns in shapes. The most well-known and widely applied tool from this approach is persistent homology, which tracks the evolution of topological features in a dynamic manner as a barcode. Morse theory is a framework from differential topology that studies critical points of functions on manifolds; it has been used to characterize the birth and death of persistent homology features. However, a significant limitation to Morse theory is that it cannot be readily applied to distance functions because distance functions lack smoothness, which is required in Morse theory. Our contributions to addressing this issue is two fold. First, we generalize Morse theory to Euclidean distance functions of bounded sets with smooth boundaries. We focus in particular on distance fields for shape representation and we study the persistent homology of shape textures using a sublevel set filtration induced by the signed distance function. We use transversality theory to prove that for generic embeddings of a smooth compact surface in $\mathbb{R}^3$, signed distance functions admit finitely many non-degenerate critical points. This gives rise to our second contribution, which is that shapes and textures can both now be quantified and rigorously characterized in the language of persistent homology: signed distance persistence modules of generic shapes admit a finite barcode decomposition whose birth and death points can be classified and described geometrically. We use this approach to quantify shape textures on both simulated data and real vascular data from biology.
Persistent Homology
Discrete Morse theory
Topological data analysis
Homology
Computational topology
Signed distance function
Circle-valued Morse theory
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Morse theory can be seen as the investigation of the relation between functions defined on a manifold and the shape of the manifold itself. The key feature in Morse theory is that information on the topology of the manifold is derived from the information about the critical points of real functions defined on the manifold. Let us first introduce the definition of Morse fonction, and then state the main results provided by Morse theory for the topological analysis of smooth manifolds, such as surfaces. A basic reference for Morse theory is [141], while details about notions of geometry and topology can be found, for example, in [104].
Manifold (fluid mechanics)
Circle-valued Morse theory
Discrete Morse theory
Closed manifold
Differential topology
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We investigate combinatorial dynamical systems on simplicial complexes considered as finite topological spaces. Such systems arise in a natural way from sampling dynamics and may be used to reconstruct some features of the dynamics directly from the sample. We study the homological persistence of Morse decompositions of such systems as a tool for validating the reconstruction. Our approach may be viewed as a step toward applying the classical persistence theory to data collected from a dynamical system. We present experimental results on two numerical examples.
Persistent Homology
Topological data analysis
Discrete Morse theory
Persistence (discontinuity)
Homology
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Taking a discrete approach to functions and dynamical systems, this paper integrates the combinatorial gradients in Forman's discrete Morse theory with persistent homology to forge a unified approach to function simplification. The two crucial ingredients in this effort are the Lefschetz complex, which focuses on the homology at the expense of the geometry of the cells, and the shallow pairs, which are birth-death pairs that can double as vectors in discrete Morse theory. The main new concept is the depth poset on the birth-death pairs, which captures all simplifications achieved through canceling shallow pairs. One of its linear extensions is the ordering by persistence.
Discrete Morse theory
Persistent Homology
Homology
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Topological data analysis can reveal higher-order structure beyond pairwise connections between vertices in complex networks. We present a new method based on discrete Morse theory to study topological properties of unweighted and undirected networks using persistent homology. Leveraging on the features of discrete Morse theory, our method not only captures the topology of the clique complex of such graphs via the concept of critical simplices, but also achieves close to the theoretical minimum number of critical simplices in several analyzed model and real networks. This leads to a reduced filtration scheme based on the subsequence of the corresponding critical weights, thereby leading to a significant increase in computational efficiency. We have employed our filtration scheme to explore the persistent homology of several model and real-world networks. In particular, we show that our method can detect differences in the higher-order structure of networks, and the corresponding persistence diagrams can be used to distinguish between different model networks. In summary, our method based on discrete Morse theory further increases the applicability of persistent homology to investigate the global topology of complex networks.
Persistent Homology
Discrete Morse theory
Computational topology
Topological data analysis
Homology
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Persistent Homology
Equivalence relation
Discrete Morse theory
Topological data analysis
Dynamics
Homology
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Citations (6)
We investigate combinatorial dynamical systems on simplicial complexes considered as {\em finite topological spaces}. Such systems arise in a natural way from sampling dynamics and may be used to reconstruct some features of the dynamics directly from the sample. We study the homological persistence of {\em Morse decompositions} of such systems, an important descriptor of the dynamics, as a tool for validating the reconstruction. Our framework can be viewed as a step toward extending the classical persistence theory to vector cloud data. We present experimental results on two numerical examples.
Persistent Homology
Topological data analysis
Discrete Morse theory
Persistence (discontinuity)
Dynamics
Homology
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Citations (1)
We develop topologically accurate and compatible definitions for the skeleton and watershed segmentation of a 3D digital object that are computed by a single algorithm. These definitions are based on a discrete gradient vector field derived from a signed distance transform. This gradient vector field is amenable to topological analysis and simplification via For-man's discrete Morse theory and provides a filtration that can be used as input to persistent homology algorithms. Efficient implementations allow us to process large-scale x-ray micro-CT data of rock cores and other materials.
Persistent Homology
Discrete Morse theory
Topological data analysis
Signed distance function
Topological skeleton
Vector flow
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Among the newer approaches to data analysis are topological methods (TDA), which proved to be effective in analyzing data. In this thesis we analyze data on 911 calls that include a large number of calls. Firstly, we prepare data by grouping calls together using the Vietoris-Rips complex. We do this because it enables us to also analyze smaller areas and connect them. We analyze this complex in two ways: by using Morse theory and persistent homology. Morse theory is used to acquire critical simplices from the complex. They give us new information about the data. Using persistent homology, we produce persistent diagrams that illustrate how homology of a complex changes depending on a parameter. The initiative to use the TDA on such data came from the Department of Sociology at Harvard, where they had already tried to analyze this data by using various mechanical and mathematical models.
Topological data analysis
Persistent Homology
Discrete Morse theory
Homology
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We investigate combinatorial dynamical systems on simplicial complexes considered as {\em finite topological spaces}. Such systems arise in a natural way from sampling dynamics and may be used to reconstruct some features of the dynamics directly from the sample. We study the homological persistence of {\em Morse decompositions} of such systems, an important descriptor of the dynamics, as a tool for validating the reconstruction. Our framework can be viewed as a step toward extending the classical persistence theory to "vector cloud" data. We present experimental results on two numerical examples.
Persistent Homology
Topological data analysis
Discrete Morse theory
Persistence (discontinuity)
Dynamics
Homology
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