MMPersistence: A mathematical morphology-oriented software library for computing persistent homology on cubical complexes
- URL: http://arxiv.org/abs/2602.15502v1
- Date: Tue, 17 Feb 2026 11:15:56 GMT
- Title: MMPersistence: A mathematical morphology-oriented software library for computing persistent homology on cubical complexes
- Authors: Chuan-Shen Hu,
- Abstract summary: Mathematical morphology (MM) is a powerful and widely used framework in image processing.<n>We propose the MMPersistence library, which integrates MM operations with diverse SEs and PH computation to extract persistence information.<n>By employing SEs of different shapes to construct topological filtrations, the proposed MM-based PH framework encodes both spatial and morphological characteristics of digital images.
- Score: 2.7074235008521246
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Mathematical morphology (MM) is a powerful and widely used framework in image processing. Through set-theoretic and discrete geometric principles, MM operations such as erosion, dilation, opening, and closing effectively manipulate digital images by modifying local structures via structuring elements (SEs), while cubical homology captures global topological features such as connected components and loop structures within images. Building on the GUDHI package for persistent homology (PH) computation on cubical complexes, we propose the MMPersistence library, which integrates MM operations with diverse SEs and PH computation to extract multiscale persistence information. By employing SEs of different shapes to construct topological filtrations, the proposed MM-based PH framework encodes both spatial and morphological characteristics of digital images, providing richer local geometric information than conventional cubical homology alone and establishing a unified foundation for analyzing digital images that integrates topological insight with morphological image processing techniques.
Related papers
- Integrating Multi-scale and Multi-filtration Topological Features for Medical Image Classification [20.820287362872975]
Deep neural networks have shown remarkable performance in medical image classification.<n>We propose a new topology-guided classification framework that extracts multi-scale and multi-filtration persistent topological features.<n>Our approach enhances the model's capacity to recognize complex anatomical structures.
arXiv Detail & Related papers (2025-12-08T06:02:02Z) - Multiscale geometrical and topological learning in the analysis of soft matter collective dynamics [0.4440432588828829]
Joint geometric and topological data analysis (TDA) offers powerful framework for investigating such systems.<n>The method based on the analysis of fields generated from images of skyrmion ensembles offers insights into the nonlinear physical mechanisms of the system's response to external stimuli.
arXiv Detail & Related papers (2025-07-28T18:40:37Z) - PRISM: Probabilistic Representation for Integrated Shape Modeling and Generation [79.46526296655776]
PRISM is a novel approach for 3D shape generation that integrates categorical diffusion models with Statistical Shape Models (SSM) and Gaussian Mixture Models (GMM)<n>Our method employs compositional SSMs to capture part-level geometric variations and uses GMM to represent part semantics in a continuous space.<n>Our approach significantly outperforms previous methods in both quality and controllability of part-level operations.
arXiv Detail & Related papers (2025-04-06T11:48:08Z) - Novel computational workflows for natural and biomedical image processing based on hypercomplex algebras [49.81327385913137]
Hypercomplex image processing extends conventional techniques in a unified paradigm encompassing algebraic and geometric principles.<n>This workleverages quaternions and the two-dimensional planes split framework (splitting of a quaternion - representing a pixel - into pairs of 2D planes) for natural/biomedical image analysis.<n>The proposed can regulate color appearance (e.g. with alternative renditions and grayscale conversion) and image contrast, be part of automated image processing pipelines.
arXiv Detail & Related papers (2025-02-11T18:38:02Z) - A Continuous and Interpretable Morphometric for Robust Quantification of Dynamic Biological Shapes [1.37013665345905]
PF-SDM compactly encodes geometric and topological properties of closed shapes.<n>It provides robust and interpretable features for shape comparison and machine learning.
arXiv Detail & Related papers (2024-10-28T13:28:21Z) - ShapeMamba-EM: Fine-Tuning Foundation Model with Local Shape Descriptors and Mamba Blocks for 3D EM Image Segmentation [49.42525661521625]
This paper presents ShapeMamba-EM, a specialized fine-tuning method for 3D EM segmentation.
It is tested over a wide range of EM images, covering five segmentation tasks and 10 datasets.
arXiv Detail & Related papers (2024-08-26T08:59:22Z) - Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration [59.062085785106234]
This article presents a general Bayesian learning framework for multi-modal groupwise image registration.<n>We propose a novel hierarchical variational auto-encoding architecture to realise the inference procedure of the latent variables.<n>Experiments were conducted to validate the proposed framework, including four different datasets from cardiac, brain, and abdominal medical images.
arXiv Detail & Related papers (2024-01-04T08:46:39Z) - Image2SSM: Reimagining Statistical Shape Models from Images with Radial
Basis Functions [4.422330219605964]
We propose Image2SSM, a novel deep-learning-based approach for statistical shape modeling.
Image2SSM learns a radial-basis-function (RBF)-based representation of shapes directly from images.
It can characterize populations of biological structures of interest by constructing statistical landmark-based shape models of ensembles of anatomical shapes.
arXiv Detail & Related papers (2023-05-19T18:08:10Z) - A Multi-parameter Persistence Framework for Mathematical Morphology [2.1485350418225244]
We look at morphological operations through the lens of persistent homology.
persistent homology is a tool at the heart of the field of topological data analysis.
arXiv Detail & Related papers (2021-03-24T06:46:00Z) - ResNet-LDDMM: Advancing the LDDMM Framework Using Deep Residual Networks [86.37110868126548]
In this work, we make use of deep residual neural networks to solve the non-stationary ODE (flow equation) based on a Euler's discretization scheme.
We illustrate these ideas on diverse registration problems of 3D shapes under complex topology-preserving transformations.
arXiv Detail & Related papers (2021-02-16T04:07:13Z) - Pathological Retinal Region Segmentation From OCT Images Using Geometric
Relation Based Augmentation [84.7571086566595]
We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape.
The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
arXiv Detail & Related papers (2020-03-31T11:50:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.