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Li L, Liu H, Li Q, Tian Z, Li Y, Geng W, Wang S. Near-Infrared Blood Vessel Image Segmentation Using Background Subtraction and Improved Mathematical Morphology. Bioengineering (Basel) 2023; 10:726. [PMID: 37370657 DOI: 10.3390/bioengineering10060726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
The precise display of blood vessel information for doctors is crucial. This is not only true for facilitating intravenous injections, but also for the diagnosis and analysis of diseases. Currently, infrared cameras can be used to capture images of superficial blood vessels. However, their imaging quality always has the problems of noises, breaks, and uneven vascular information. In order to overcome these problems, this paper proposes an image segmentation algorithm based on the background subtraction and improved mathematical morphology. The algorithm regards the image as a superposition of blood vessels into the background, removes the noise by calculating the size of connected domains, achieves uniform blood vessel width, and smooths edges that reflect the actual blood vessel state. The algorithm is evaluated subjectively and objectively in this paper to provide a basis for vascular image quality assessment. Extensive experimental results demonstrate that the proposed method can effectively extract accurate and clear vascular information.
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Affiliation(s)
- Ling Li
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haoting Liu
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Qing Li
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhen Tian
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yajie Li
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Wenjia Geng
- Department of Traditional Chinese Medicine, Peking University People's Hospital, Beijing 100044, China
| | - Song Wang
- Department of Nephrology, Peking University Third Hospital, Beijing 100191, China
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Mariano CA, Sattari S, Ramirez GO, Eskandari M. Effects of tissue degradation by collagenase and elastase on the biaxial mechanics of porcine airways. Respir Res 2023; 24:105. [PMID: 37031200 PMCID: PMC10082978 DOI: 10.1186/s12931-023-02376-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 02/22/2023] [Indexed: 04/10/2023] Open
Abstract
BACKGROUND Common respiratory illnesses, such as emphysema and chronic obstructive pulmonary disease, are characterized by connective tissue damage and remodeling. Two major fibers govern the mechanics of airway tissue: elastin enables stretch and permits airway recoil, while collagen prevents overextension with stiffer properties. Collagenase and elastase degradation treatments are common avenues for contrasting the role of collagen and elastin in healthy and diseased states; while previous lung studies of collagen and elastin have analyzed parenchymal strips in animal and human specimens, none have focused on the airways to date. METHODS Specimens were extracted from the proximal and distal airways, namely the trachea, large bronchi, and small bronchi to facilitate evaluations of material heterogeneity, and subjected to biaxial planar loading in the circumferential and axial directions to assess airway anisotropy. Next, samples were subjected to collagenase and elastase enzymatic treatment and tensile tests were repeated. Airway tissue mechanical properties pre- and post-treatment were comprehensively characterized via measures of initial and ultimate moduli, strain transitions, maximum stress, hysteresis, energy loss, and viscoelasticity to gain insights regarding the specialized role of individual connective tissue fibers and network interactions. RESULTS Enzymatic treatment demonstrated an increase in airway tissue compliance throughout loading and resulted in at least a 50% decrease in maximum stress overall. Strain transition values led to significant anisotropic manifestation post-treatment, where circumferential tissues transitioned at higher strains compared to axial counterparts. Hysteresis values and energy loss decreased after enzymatic treatment, where hysteresis reduced by almost half of the untreated value. Anisotropic ratios exhibited axially led stiffness at low strains which transitioned to circumferentially led stiffness when subjected to higher strains. Viscoelastic stress relaxation was found to be greater in the circumferential direction for bronchial airway regions compared to axial counterparts. CONCLUSION Targeted fiber treatment resulted in mechanical alterations across the loading range and interactions between elastin and collagen connective tissue networks was observed. Providing novel mechanical characterization of elastase and collagenase treated airways aids our understanding of individual and interconnected fiber roles, ultimately helping to establish a foundation for constructing constitutive models to represent various states and progressions of pulmonary disease.
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Affiliation(s)
- Crystal A Mariano
- Department of Mechanical Engineering, University of California at Riverside, Riverside, CA, USA
| | - Samaneh Sattari
- Department of Mechanical Engineering, University of California at Riverside, Riverside, CA, USA
| | - Gustavo O Ramirez
- Department of Mechanical Engineering, University of California at Riverside, Riverside, CA, USA
| | - Mona Eskandari
- Department of Mechanical Engineering, University of California at Riverside, Riverside, CA, USA.
- BREATHE Center, School of Medicine, University of California at Riverside, Riverside, CA, USA.
- Department of Bioengineering, University of California at Riverside, Riverside, CA, USA.
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Sailer S, Mundszinger M, Martin J, Mancini M, Wohlfahrt-Mehrens M, Kaiser U. Quantitative FIB/SEM tomogram analysis of closed and open porosity of spheroidized graphite anode materials for LiBs applications. Micron 2023; 166:103398. [PMID: 36682294 DOI: 10.1016/j.micron.2022.103398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/15/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
The electrochemical behaviour of rounded graphite particles as anode material in a lithium-ion battery strongly depends on the particle properties. The spheroidization process directly affects these properties, including the open porosity that determines the extent of direct contact between liquid electrolyte and carbon surface. Therefore, the quantification of the proportion between open and closed pores is of great interest. Here, we quantify the open and closed porosity of spheroidized porous graphite particles from FIB-SEM tomograms. Quantification is achieved based on two developments: (1) a new sample preparation strategy and (2) a newly developed image evaluation scheme based on neural networks. The sample preparation strategy involves embedding of many graphite powder particles in indium enabling the investigation of several graphite particles in one FIB/SEM tomogram with high stability and with high contrast between the conductive embedding material and porous graphite. A quantitative evaluation of closed and open porosity is achieved by machine learning in form of convolutional neural networks. The convolutional neural network is used to detect the bulk graphite and by further morphological operations, closed and open pores are identified. An error is determined by comparing automatically created quantifications with manual reference values. Our porosity data for two differently spheroidized graphite samples agree qualitatively well with corresponding results from nitrogen physisorption measurements. This approach may allow quantitative data evaluation from porous powders and support understanding of the correlation to the electrochemical behaviour in the lithium-ion battery.
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Affiliation(s)
- Stefan Sailer
- Central Facility for Electron Microscopy, Electron Microscopy Group of Materials Science, Albert-Einstein-Allee 11, Universität Ulm, 89081 Ulm, Germany.
| | - Manuel Mundszinger
- Central Facility for Electron Microscopy, Electron Microscopy Group of Materials Science, Albert-Einstein-Allee 11, Universität Ulm, 89081 Ulm, Germany
| | - Jan Martin
- Center for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW), Helmholtzstraße 8, 89081 Ulm, Germany
| | - Marilena Mancini
- Center for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW), Helmholtzstraße 8, 89081 Ulm, Germany
| | - Margret Wohlfahrt-Mehrens
- Center for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW), Helmholtzstraße 8, 89081 Ulm, Germany
| | - Ute Kaiser
- Central Facility for Electron Microscopy, Electron Microscopy Group of Materials Science, Albert-Einstein-Allee 11, Universität Ulm, 89081 Ulm, Germany.
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Lee JC, Seo H, Lee M, Kim D, Lee HS, Park H, Ball N, Woo J, Kim SY, Nam J, Park S. Investigation of the Effect of 3D Meniscus Geometry on Fluid Dynamics and Crystallization via In Situ Optical Microscopy-Assisted Mathematical Modeling. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2105035. [PMID: 34617325 DOI: 10.1002/adma.202105035] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/04/2021] [Indexed: 06/13/2023]
Abstract
Solution-based thin-film solidification is a complex process involving various transport phenomena that are intricately dependent on multiple experimental parameters. The difficulty of analyzing this process experimentally or conducting exact numerical simulation make it challenging to understand, predict, and control the solidification process. In this work, a simple and effective technique to analyze the thin-film solidification process during solution shearing, based on 3D geometrical model of the meniscus, is proposed. The 3D meniscus geometry, which changes depending on the experimental parameters, is attained using high-speed side-view and top-view in situ microscopy. Thereafter, mass and momentum transport mathematical models are applied to obtain numerical solutions of transport phenomena within the meniscus. Utilizing these results, the underlying mechanism of dendritic growth of small molecule organic semiconductor is elucidated, which has previously been unknown. The 3D meniscus modeling is particularly important for this analysis, as dendrite formation is strongly dependent on the meniscus geometry near the contact line and mass transport variation perpendicular to the coating direction. This technique enables the study of complex relationship between experimental parameters and solidification process, which is widely applicable to various materials and coating systems; whereby, better understanding of thin-film growth and device performance optimization is possible.
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Affiliation(s)
- Jeong-Chan Lee
- Organic and Nano Electronics Laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyeji Seo
- Organic and Nano Electronics Laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Minho Lee
- School of Chemical and Biological Engineering and Institute of Chemical Process, Seoul National University, Seoul, 08826, Republic of Korea
| | - Dongjae Kim
- School of Chemical and Biological Engineering and Institute of Chemical Process, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hyeon Seok Lee
- Organic and Nano Electronics Laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyunmin Park
- Organic and Nano Electronics Laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Nathaniel Ball
- Department of Mechanical and Aerospace Engineering, University of Florida (UF), Gainesville, 32611, USA
| | - Junhee Woo
- Organic and Nano Electronics Laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Su Yeong Kim
- Organic and Nano Electronics Laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jaewook Nam
- School of Chemical and Biological Engineering and Institute of Chemical Process, Seoul National University, Seoul, 08826, Republic of Korea
| | - Steve Park
- Organic and Nano Electronics Laboratory, Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- KI for Health Science and Technology, Saudi Aramco-KAIST CO2 Management Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
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