A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images

• URL: http://arxiv.org/abs/2502.07107v1
• Date: Mon, 10 Feb 2025 23:05:35 GMT
• Title: A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images
• Authors: Kungang Zhang, Daniel W. Apley, Wei Chen, Wing K. Liu, L. Catherine Brinson,
• Abstract summary: The framework integrates unsupervised and supervised learning methods to classify micrographs according to phase/class. It can be used to gradually build a database of microstructure classes relevant to a particular process or group of materials. This framework can iteratively characterize/segment new homogeneous or multiphase materials while expanding the database to enhance performance.
• Score: 4.386263848151506
• License:
• Abstract: Microstructure of materials is often characterized through image analysis to understand processing-structure-properties linkages. We propose a largely automated framework that integrates unsupervised and supervised learning methods to classify micrographs according to microstructure phase/class and, for multiphase microstructures, segments them into different homogeneous regions. With the advance of manufacturing and imaging techniques, the ultra-high resolution of imaging that reveals the complexity of microstructures and the rapidly increasing quantity of images (i.e., micrographs) enables and necessitates a more powerful and automated framework to extract materials characteristics and knowledge. The framework we propose can be used to gradually build a database of microstructure classes relevant to a particular process or group of materials, which can help in analyzing and discovering/identifying new materials. The framework has three steps: (1) segmentation of multiphase micrographs through a recently developed score-based method so that different microstructure homogeneous regions can be identified in an unsupervised manner; (2) {identification and classification of} homogeneous regions of micrographs through an uncertainty-aware supervised classification network trained using the segmented micrographs from Step $1$ with their identified labels verified via the built-in uncertainty quantification and minimal human inspection; (3) supervised segmentation (more powerful than the segmentation in Step $1$) of multiphase microstructures through a segmentation network trained with micrographs and the results from Steps $1$-$2$ using a form of data augmentation. This framework can iteratively characterize/segment new homogeneous or multiphase materials while expanding the database to enhance performance. The framework is demonstrated on various sets of materials and texture images.

Related papers

• Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models [0.0]
  We propose an innovative backbone architecture for analyzing electron micrographs. We create multi-modal representations of the micrographs by tokenizing them into patch sequences and, additionally, representing them as vision graphs. Our framework outperforms traditional methods, overcoming challenges posed by distributional shifts.
  arXiv  Detail & Related papers  (2024-08-24T19:24:44Z)
• MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large Model [11.130574172301365]
  Segment Anything Model (SAM) is a large visual model with powerful deep feature representation and zero-shot generalization capabilities. In this paper, we propose MatSAM, a general and efficient microstructure extraction solution based on SAM. A simple yet effective point-based prompt generation strategy is designed, grounded on the distribution and shape of microstructures.
  arXiv  Detail & Related papers  (2024-01-11T03:18:18Z)
• Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration [50.62725807357586]
  This article presents a general Bayesian learning framework for multi-modal groupwise image registration. We propose a novel hierarchical variational auto-encoding architecture to realise the inference procedure of the latent variables. 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)
• Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives [0.4595477728342621]
  This review paper focuses on the state-of-the-art CNN-based techniques that have been applied to various multi-scale microstructural image analysis tasks. We identify the main challenges with regard to the application of these methods to materials science research.
  arXiv  Detail & Related papers  (2022-07-29T15:27:47Z)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics [77.34726150561087]
  This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
  arXiv  Detail & Related papers  (2022-05-31T13:26:51Z)
• Digital Fingerprinting of Microstructures [44.139970905896504]
  Finding efficient means of fingerprinting microstructural information is a critical step towards harnessing data-centric machine learning approaches. Here, we consider microstructure classification and utilise the resulting features over a range of related machine learning tasks. In particular, methods that leverage transfer learning with convolutional neural networks (CNNs), pretrained on the ImageNet dataset, are generally shown to outperform other methods.
  arXiv  Detail & Related papers  (2022-03-25T15:40:44Z)
• Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms [52.58031087639394]
  micro-expressions are involuntary and transient facial expressions. They can provide important information in a broad range of applications such as lie detection, criminal detection, etc. Since micro-expressions are transient and of low intensity, their detection and recognition is difficult and relies heavily on expert experiences.
  arXiv  Detail & Related papers  (2022-01-30T05:14:13Z)
• MOGAN: Morphologic-structure-aware Generative Learning from a Single Image [59.59698650663925]
  Recently proposed generative models complete training based on only one image. We introduce a MOrphologic-structure-aware Generative Adversarial Network named MOGAN that produces random samples with diverse appearances. Our approach focuses on internal features including the maintenance of rational structures and variation on appearance.
  arXiv  Detail & Related papers  (2021-03-04T12:45:23Z)
• Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships [0.49259062564301753]
  We develop an improved machine learning approach to image recognition, characterization, and building predictive capabilities. A binary alloy (uranium-molybdenum) that is currently under development as a nuclear fuel was studied. A F1 score of 95.1% was achieved for distinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.
  arXiv  Detail & Related papers  (2020-07-27T10:36:18Z)
• Retinal Image Segmentation with a Structure-Texture Demixing Network [62.69128827622726]
  The complex structure and texture information are mixed in a retinal image, and distinguishing the information is difficult. Existing methods handle texture and structure jointly, which may lead biased models toward recognizing textures and thus results in inferior segmentation performance. We propose a segmentation strategy that seeks to separate structure and texture components and significantly improve the performance.
  arXiv  Detail & Related papers  (2020-07-15T12:19:03Z)
• Overview: Computer vision and machine learning for microstructural characterization and analysis [1.4194966466632493]
  Computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics.
  arXiv  Detail & Related papers  (2020-05-28T19:51:23Z)

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.