Graph cuts and neural networks for segmentation and porosity quantification in Synchrotron Radiation X-ray μCT of an igneous rock sample

Meneses, Anderson Alvarenga de Moura; Palheta, Dayara Bastos; Pinheiro, Christiano Jorge Gomes; Barroso, R.C.

doi:10.1016/j.apradiso.2017.12.019

Cited by 3 publications

(4 citation statements)

References 48 publications

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“…After that, the Lagrange multiplier [ 19 ] λ z ( z = 1, 2, ⋯, w ) was introduced into the objective function, to reconstruct the objective function. The transformation function K ( A , B , λ ) could be obtained, and its mathematical expression was shown as equation ( 4 ).…”

Section: Methodsmentioning

confidence: 99%

Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage

Tang

et al. 2022

Computational and Mathematical Methods in Medicine

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The aim of this study was to explore the application value of brain computed tomography (CT) images under intelligent segmentation algorithm and serological indexes in the early prediction of hematoma enlargement in patients with intracerebral hemorrhage (ICH). Fuzzy C -means (FCM) intelligence segmentation algorithm was introduced, and 150 patients with early ICH were selected as the research objects. Patient cerebral CT images were intelligently segmented to assess the diagnostic value of this algorithm. According to different hematoma volumes during CT examination, patients were divided into observation group (hematoma enlargement occurred, n = 48 ) and control group (no hematoma enlargement occurred, n = 102 ). The predicative value of hematoma enlargement after ICH was investigated by assessing CT image quality and measuring intracerebral edema, hematoma volume, and serological indicators of the patients of the two groups. The results demonstrated that the sensitivity, specificity, and accuracy of CT images processed by intelligence segmentation algorithm amounted to 0.894, 0.898, and 0.930, respectively. Besides, early edema enlargement and hematoma of patients in the observation group were more significant than those of patients in the control group. Relative edema volume was 0.912, which was apparently lower than that in the control group (1.017) ( P < 0.05 ). In terms of CT signs of ICH patients, the incidence of blend sign, low density sign, and stroke of the observation group was evidently higher than those of the control group ( P < 0.05 ). Besides, absolute lymphocyte count (ALC) and hemoglobin (HGB) concentration of the patients in the observation group were 6.23 × 109 /L and 6.29 × 109 /L, respectively, both of which were higher than those of the control group ( 6.08 × 109 /L and 4.25 × 109 /L). Neutrophil to lymphocyte ratio (NLR) was 0.99 × 109 /L, which was apparently lower than that in the control group ( 1.43 × 109 /L) ( P < 0.05 ). To sum up, cerebral CT images processed by FCM algorithm showed good diagnostic effect on ICH and high clinical values in the early prediction of hematoma among ICH patients.

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Section: Methodsmentioning

confidence: 99%

Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage

Tang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…A significant number of works (Lauridsen et al, 2014;Meneses et al, 2018;Ma et al, 2020) use SRμCT. SRμCT for instance, has been applied to detect gas deposits on hydrophobic surfaces visually.…”

Section: Applications Of Synchrotron Radiation-based Techniquesmentioning

confidence: 99%

“…In the field of earth sciences, SRμCT is effectively utilized for the morphological examination of rocks and soil as well as the evaluation of their internal structures. Meneses et al (2018) employed layer-by-layer SRμCT scanning of pumice to ascertain the porosity and friability-two key characteristics of this igneous rock. Artificial neural networks were trained on the acquired images using a variety of segmentation techniques.…”

Section: Applications Of Synchrotron Radiation-based Techniquesmentioning

confidence: 99%

Synchrotron-based techniques for elemental analysis in soil-plant system under polluted environment

Kirichkov,

Burachevskaya,

Mandzhieva

et al. 2023

EJSS

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Analytical techniques for elemental analysis in the soil-plant system have significance importance, especially emerging techniques such as synchrotron radiation (SR). Improved techniques allow samples to be examined in a non-invasive manner at high speed and resolution, resulting in better sample data. By applying various analytical techniques based on SR, it is possible to gather different information about the structure of the studied samples. In mining ecology, such techniques are widely used in assessing heavy metal-polluted sites, i.e., overburden dumps and areas around operating and mothballed mines. The present review elaborated insights into different analytical techniques for applying SR in plant-soil samples. The review also compared traditional research techniques with SR-based emerging and improved techniques. The need to use SR techniques for the complex diagnostics of sample structures to study their elemental and phase composition is substantiated. Using an integrated approach with SR, we can study the dynamics and speciation of HMs with carrier phases and uncover the mechanisms underlying the interactions between the adsorption centers of minerals, organic components, and heavy metals. It also improves the efficiency and accuracy of analysis and broadens the range of information obtained, which could lead to a more precise analysis of samples.

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“…In such data, usually cells (e.g., osteocyte) can't be seen properly, but information about blood vessels is complete. For such data it is important to calculate some traditional morphology information [16][17][18][19], such as bone surface (B.PM), bone area (B.Ar), porosity (Po), cortical thickness (Ct.Wi), canal number (N.Ca), canal area (Ca.Ar), and etc. Additionally, the degree of anisotropy can be measured.…”

Section: Macro Scale Datamentioning

confidence: 99%

Automation of Bone Tissue Histology

et al. 2019

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In abstract methods of automation of histology, bone structure is considered. Possible inputs are snapshots from a microscope or computed tomography slices. An algorithm is proposed that differentiates objects according to their color (or grayscale) and recover morphology topology. An algorithm to separate morphological objects by their dimensions and color parameters was built. Measured parameters were bone surface, bone area, porosity, cortical thickness, canal number, canal area, and etc. Additionally, we measured the anisotropy properties of the bone tissue: distribution of porosity direction and degree of porosity elongation. A bone example was scanned by computed tomography. All data were measured by the proposed method and the results presented. An example algorithm of work on computed tomography data is shown in this work.

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Graph cuts and neural networks for segmentation and porosity quantification in Synchrotron Radiation X-ray μCT of an igneous rock sample

Cited by 3 publications

References 48 publications

Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage

Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage

Synchrotron-based techniques for elemental analysis in soil-plant system under polluted environment

Automation of Bone Tissue Histology

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