Machine learning-based data correlation between scanning electron microscopy images and energy-dispersive X-ray spectroscopy profiles
Аутори
Musa, AhmedSung, Baeckkyoung
Abelmann, Leon
Остала ауторства
Morić, IvanaĐorđević, Valentina
Конференцијски прилог (Објављена верзија)
,
© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
Метаподаци
Приказ свих података о документуАпстракт
Characterisation of organic and inorganic microparticles has long been an important
topic in the field of environmental health sciences. Especially, combined analytical
method of scanning electron microscopy (SEM) associated with energy-dispersive X-ray
spectroscopy (EDX) is a commonly exploited approach to obtain extensive data on the size,
morphological, and elemental information from the particulate specimens. Particulate
matter (PM) is a representative atmospheric pollutant that may exert adverse effects
on the human respiratory system, and SEM-EDX is a widely used tool for extracting
PM analysis data, which can be subsequently utilised as physicochemical features for
toxicological predictions.
In this presentation, we show a machine learning-based automation of SEM-EDX
correlation of environmental PM data. First, we segment SEM images using WEKA
trainable segmentation which is based on a random forest algorithm to classify pixels as
foreground and background groups, fo...llowed by finding connected components (pixels
that are foreground and connected vertically or horizontally). These regions are used to
calculate PM shape parameters. Next, element maps are obtained from EDX using curve
fitting with HyperSpy Python package. PM regions from SEM images are utilised to sum
intensities in the same spatial location for the element maps to obtain elemental profiles.
We finally build two models to predict PM elements: (1) Element maps from SEMEDX
data using image-to-image translation, and (2) regression to predict PM element
percentages from shape features. Results from model 1 and 2 are then applied to extract
PM elemental profiles associated with PM morphology information. Our results show
how to efficiently predict EDX and element maps from SEM images with a high degree of
accuracy. This method has a potential to significantly reduce time and labour required for
environmental PM monitoring.
Кључне речи:
environmental health / particulate matter / SEM / EDX / automated data analysis / multiple output regressionИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 108-108Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Musa, Ahmed AU - Sung, Baeckkyoung AU - Abelmann, Leon PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/2053 AB - Characterisation of organic and inorganic microparticles has long been an important topic in the field of environmental health sciences. Especially, combined analytical method of scanning electron microscopy (SEM) associated with energy-dispersive X-ray spectroscopy (EDX) is a commonly exploited approach to obtain extensive data on the size, morphological, and elemental information from the particulate specimens. Particulate matter (PM) is a representative atmospheric pollutant that may exert adverse effects on the human respiratory system, and SEM-EDX is a widely used tool for extracting PM analysis data, which can be subsequently utilised as physicochemical features for toxicological predictions. In this presentation, we show a machine learning-based automation of SEM-EDX correlation of environmental PM data. First, we segment SEM images using WEKA trainable segmentation which is based on a random forest algorithm to classify pixels as foreground and background groups, followed by finding connected components (pixels that are foreground and connected vertically or horizontally). These regions are used to calculate PM shape parameters. Next, element maps are obtained from EDX using curve fitting with HyperSpy Python package. PM regions from SEM images are utilised to sum intensities in the same spatial location for the element maps to obtain elemental profiles. We finally build two models to predict PM elements: (1) Element maps from SEMEDX data using image-to-image translation, and (2) regression to predict PM element percentages from shape features. Results from model 1 and 2 are then applied to extract PM elemental profiles associated with PM morphology information. Our results show how to efficiently predict EDX and element maps from SEM images with a high degree of accuracy. This method has a potential to significantly reduce time and labour required for environmental PM monitoring. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Machine learning-based data correlation between scanning electron microscopy images and energy-dispersive X-ray spectroscopy profiles EP - 108 SP - 108 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_2053 ER -
@conference{ author = "Musa, Ahmed and Sung, Baeckkyoung and Abelmann, Leon", year = "2023", abstract = "Characterisation of organic and inorganic microparticles has long been an important topic in the field of environmental health sciences. Especially, combined analytical method of scanning electron microscopy (SEM) associated with energy-dispersive X-ray spectroscopy (EDX) is a commonly exploited approach to obtain extensive data on the size, morphological, and elemental information from the particulate specimens. Particulate matter (PM) is a representative atmospheric pollutant that may exert adverse effects on the human respiratory system, and SEM-EDX is a widely used tool for extracting PM analysis data, which can be subsequently utilised as physicochemical features for toxicological predictions. In this presentation, we show a machine learning-based automation of SEM-EDX correlation of environmental PM data. First, we segment SEM images using WEKA trainable segmentation which is based on a random forest algorithm to classify pixels as foreground and background groups, followed by finding connected components (pixels that are foreground and connected vertically or horizontally). These regions are used to calculate PM shape parameters. Next, element maps are obtained from EDX using curve fitting with HyperSpy Python package. PM regions from SEM images are utilised to sum intensities in the same spatial location for the element maps to obtain elemental profiles. We finally build two models to predict PM elements: (1) Element maps from SEMEDX data using image-to-image translation, and (2) regression to predict PM element percentages from shape features. Results from model 1 and 2 are then applied to extract PM elemental profiles associated with PM morphology information. Our results show how to efficiently predict EDX and element maps from SEM images with a high degree of accuracy. This method has a potential to significantly reduce time and labour required for environmental PM monitoring.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Machine learning-based data correlation between scanning electron microscopy images and energy-dispersive X-ray spectroscopy profiles", pages = "108-108", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_2053" }
Musa, A., Sung, B.,& Abelmann, L.. (2023). Machine learning-based data correlation between scanning electron microscopy images and energy-dispersive X-ray spectroscopy profiles. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 108-108. https://hdl.handle.net/21.15107/rcub_imagine_2053
Musa A, Sung B, Abelmann L. Machine learning-based data correlation between scanning electron microscopy images and energy-dispersive X-ray spectroscopy profiles. in 4th Belgrade Bioinformatics Conference. 2023;4:108-108. https://hdl.handle.net/21.15107/rcub_imagine_2053 .
Musa, Ahmed, Sung, Baeckkyoung, Abelmann, Leon, "Machine learning-based data correlation between scanning electron microscopy images and energy-dispersive X-ray spectroscopy profiles" in 4th Belgrade Bioinformatics Conference, 4 (2023):108-108, https://hdl.handle.net/21.15107/rcub_imagine_2053 .