Auto-Odgovornost U Slučaju Automatskog Izbora

Larsson, Sara

doi:10.46793/xxsav21.084l

Cited by 3 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third step is the binarization process, which can be subdivided into threshold setting, watershed, and erosion operations. The threshold setting is based on ImageJ ® ’s built-in auto threshold function to differentiate the inter-cell and extra-cell regions 36 , 37 . The watershed algorithm segments the original cell clusters and the interference of cell debris.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning

You,

Yi,

Hsu

et al. 2023

J. Biomed. Opt.

View full text Add to dashboard Cite

An integrated cellular-resolution optical coherence tomography (OCT) module with near-infrared Raman spectroscopy was developed on the discrimination of various skin cancer cells and normal cells. Micron-level three-dimensional (3D) spatial resolution and the spectroscopic capability on chemical component determination can be obtained simultaneously.Aim: We experimentally verified the effectiveness of morphology, intensity, and spectroscopy features for discriminating skin cells.Approach: Both spatial and spectroscopic features were employed for the discrimination of five types of skin cells, including keratinocytes (HaCaT), the cell line of squamous cell carcinoma (A431), the cell line of basal cell carcinoma (BCC-1/ KMC), primary melanocytes, and the cell line of melanoma (A375). The cell volume, compactness, surface roughness, average intensity, and internal intensity standard deviation were extracted from the 3D OCT images. After removing the fluorescence components from the acquired Raman spectra, the entire spectra (600 to 2100 cm −1 ) were used.Results: An accuracy of 85% in classifying five types of skin cells was achieved. The cellular-resolution OCT images effectively differentiate cancer and normal cells, whereas Raman spectroscopy can distinguish the cancer cells with nearly 100% accuracy.Conclusions: Among the OCT image features, cell surface roughness, internal average intensity, and standard deviation of internal intensity distribution effectively differentiate the cancerous and normal cells. The three features also worked well in sorting the keratinocyte and melanocyte. Using the full Raman spectra, the melanoma and keratinocyte-based cell carcinoma cancer cells can be discriminated effectively.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The threshold setting is based on ImageJ ® ’s built-in auto threshold function to differentiate the inter-cell and extra-cell regions. 36 , 37 The watershed algorithm segments the original cell clusters and the interference of cell debris. The structural element during the erosion process was 3 pixels by 3 pixels.…”

Section: Methodsmentioning

confidence: 99%

Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning

You,

Yi,

Hsu

et al. 2023

J. Biomed. Opt.

View full text Add to dashboard Cite

show abstract

“…Auto local thresholding (1C). The 8-bit images are binarized through local adaptive thresholding (using the ImageJ Auto Local Threshold plugin [12]). 'Local' here means that the threshold is computed for each pixel within a window of a certain radius (= 8) around it.…”

Section: Overall Pipeline Structurementioning

confidence: 99%

An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization

et al. 2023

View full text Add to dashboard Cite

Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.

show abstract

“…Segmentation requires the use of a threshold value, which can bias the results. For procedure consistency, segmentation was done using the IsoData algorithm included with Image J [71]. The IsoData algorithm was selected due to the automatic threshold implementation using histograms [72] and the good rankings (quality of results) that it achieves in different surveys [73][74][75].The implementation uses an optimal threshold to separate the image pixels into two different classes (object and background).…”

Section: Image Processingmentioning

confidence: 99%

Links between surface morphology changes and damage in a toughened epoxy adhesive

Trimiño

Cronin

2021

The Journal of Adhesion

View full text Add to dashboard Cite

Owing to the increased use of toughened epoxy adhesives in current transportation light weighting efforts, it is critical that the damage mechanisms observable as strain whitening in these materials are understood and quantified. Quantification of damage is needed for finite element constitutive models used in structural design; however, thin bond lines in adhesive joints limit direct observation of the adhesive. In this study, microscope observations of bulk material specimens subjected to tensile loading were linked to strain whitening and damage in a toughened epoxy adhesive. Cracks on the surface were observed to open during loading, with strain whitening at the crack tips and with the initiation and propagation of shear bands. The stresses approximated at the crack tips suggested that particle cavitation could be occurring in these regions. Image analysis showed that strain whitening was present at crack tips and that these areas served to initiate the shear-bands. Changes in tensile specimen stiffness and strength were evaluated during load-unload and reload testing, and were linked to the presence of crack growth, as well as the formation of shear bands. Considering changes in strength, the predicted damage level before failure (D~18%) was lower than that predicted using traditional load-unload stiffness (D~35%), attributed to short-term viscoelastic effects; however, damage calculated from load-reload material stiffness (D~19%) was in good agreement with the damage estimated from changes in strength. A new approach, calculating damage from direct image analysis of strain whitening on the free surface (D~21%) was in good agreement with damage quantified by changes in strength and stiffness, with the benefit of quantifying damage over the loading history of the test sample and identifying areas of damage localization.

show abstract

Auto-Odgovornost U Slučaju Automatskog Izbora

Cited by 3 publications

References 0 publications

Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning

Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning

An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization

Links between surface morphology changes and damage in a toughened epoxy adhesive

Contact Info

Product

Resources

About