Segmentation of scanning tunneling microscopy images using variational methods and empirical wavelets

Bui, Kevin; Fauman, Jacob N.; Kes, David; Mandiola, Leticia Torres; Ciomaga, Adina; Salazar, Ricardo; Bertozzi, Andrea L.; Gilles, Jérôme; Goronzy, Dominic P.; Guttentag, Andrew; Weiss, Paul S.

doi:10.1007/s10044-019-00824-0

Cited by 11 publications

(11 citation statements)

References 69 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is due to nano-and microparticles that tend to form polydisperse agglomerates and a surface with an irregular relief. There are more advanced methods for segmentation such as the parametric (with the estimation of the parameters of the grey-level distribution of each class of objects in an image [28,29]), variational [30], neuronet [31] and correlation methods [32]. All the above methods are limited in application and they are not always available in open-ended libraries such as OpenCV.…”

Section: Size-monitoring Methods With the Use Of The Spmmentioning

confidence: 99%

Particle and Particle Agglomerate Size Monitoring by Scanning Probe Microscope

et al. 2022

View full text Add to dashboard Cite

In the present study, the use of a scanning probe microscope is described for monitoring the sizes of nanoparticles. Monitoring is the process of acquiring and analysing the set of overlapping images. The main analysis steps are image segmentation, determination of nanoparticles allocation and their sizes, determination of the overlap of images with one another, and exclusion of repeating measurements for the formation of the correct particle-size sampling. The thorough examination of commercial scanning probe microscopes, software, and image processing libraries showed that their capabilities are limited for image segmentation and determination of sizes in complex structured images. A method based on the surface curvature computation is proposed for the image segmentation (allocation of particles) and determination of particle sizes. The curvature is estimated using the surface area approximation with respect to the circumference. It is proposed to use sample displacement sensors as an aid for image stitching.

show abstract

Section: Size-monitoring Methods With the Use Of The Spmmentioning

confidence: 99%

Particle and Particle Agglomerate Size Monitoring by Scanning Probe Microscope

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

“…To extract chemical properties, STM images can be analyzed using an automated image-processing framework. Since large constant intensity regions and textures carry different information, the first step consists of performing a cartoon (the term cartoon is used, since large constant intensity regions look similar to actual cartoon art) and texture decomposition.…”

Section: Methodsmentioning

confidence: 99%

Extraction of Hidden Science from Nanoscale Images

et al. 2021

Self Cite

View full text Add to dashboard Cite

Scanning probe microscopies and spectroscopies enable investigation of surfaces and even buried interfaces down to the scale of chemical-bonding interactions, and this capability has been enhanced with the support of computational algorithms for data acquisition and image processing to explore physical, chemical, and biological phenomena. Here, we describe how scanning probe techniques have been enhanced by some of these recent algorithmic improvements. One improvement to the data acquisition algorithm is to advance beyond a simple rastering framework by using spirals at constant angular velocity and then switching to constant linear velocity, which limits the piezo creep and hysteresis issues seen in traditional acquisition methods. One can also use image-processing techniques to model the distortions that appear from tip motion effects and to make corrections to these images. Another image-processing algorithm we discuss enables researchers to segment images by domains and subdomains, thereby highlighting reactive and interesting disordered sites at domain boundaries. Lastly, we discuss algorithms used to examine the dipole direction of individual molecules and surface domains, hydrogen-bonding interactions, and molecular tilt. The computational algorithms used for scanning probe techniques are still improving rapidly and are incorporating machine learning at the next level of iteration. That said, the algorithms are not yet able to perform live adjustments during data recording that could enhance the microscopy and spectroscopic imaging methods significantly.

show abstract

“…Image segmentation is used in many application fields, such as medical imaging [2], microscopy imaging [3], remote sensing [4], and document image analysis [5]. The choice between semantic and instance segmentation is generally dependent on the goal of the classification or object detection step that follows the segmentation phase.…”

Section: Introductionmentioning

confidence: 99%

A view of computational models for image segmentation

2022

View full text Add to dashboard Cite

Image segmentation is a central topic in image processing and computer vision and a key issue in many applications, e.g., in medical imaging, microscopy, document analysis and remote sensing. According to the human perception, image segmentation is the process of dividing an image into non-overlapping regions. These regions, which may correspond, e.g., to different objects, are fundamental for the correct interpretation and classification of the scene represented by the image. The division into regions is not unique, but it depends on the application, i.e., it must be driven by the final goal of the segmentation and hence by the most significant features with respect to that goal. Thus, image segmentation can be regarded as a strongly ill-posed problem. A classical approach to deal with ill posedness consists in incorporating in the model a-priori information about the solution, e.g., in the form of penalty terms. In this work we provide a brief overview of basic computational models for image segmentation, focusing on edge-based and region-based variational models, as well as on statistical and machine-learning approaches. We also sketch numerical methods that are applied in computing solutions to these models. In our opinion, our view can help the readers identify suitable classes of methods for solving their specific problems.

show abstract

Segmentation of scanning tunneling microscopy images using variational methods and empirical wavelets

Abstract: 2 Kevin Bui et al.Au{111} and present their corresponding segmentation results.

Cited by 11 publications

References 69 publications

Particle and Particle Agglomerate Size Monitoring by Scanning Probe Microscope

Particle and Particle Agglomerate Size Monitoring by Scanning Probe Microscope

Extraction of Hidden Science from Nanoscale Images

A view of computational models for image segmentation

Contact Info

Product

Resources

About