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selection algorithm » detection algorithm (Expand Search), detection algorithms (Expand Search)
dose optimization » based optimization (Expand Search), model optimization (Expand Search), wolf optimization (Expand Search)
image selection » image detection (Expand Search), image collection (Expand Search), sample selection (Expand Search)
binary wave » binary image (Expand Search)
binary map » binary mask (Expand Search), binary image (Expand Search)
map image » ray image (Expand Search), mri image (Expand Search)
wave dose » same dose (Expand Search)
Showing 1 - 8 results of 8 for search '(( binary map image selection algorithm ) OR ( binary wave dose optimization algorithm ))', query time: 0.24s Refine Results
  1. 1
    Effectiveness of image augmentation operations.

    Effectiveness of image augmentation operations. by Hailang Li (12557543)

    Published 2022
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  2. 2
    Comprehensive view of <i>Trackoscope</i>: Design, interface, and tracking mechanics.

    Comprehensive view of <i>Trackoscope</i>: Design, interface, and tracking mechanics. by Priya Soneji (19105487)

    Published 2024
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  3. 3
    Measurement of parapapillary choroidal vessel density (pCVD).

    Measurement of parapapillary choroidal vessel density (pCVD). by Jimin Park (1396840)

    Published 2025
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  4. 4
    PathOlOgics_RBCs Python Scripts.zip

    PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

    Published 2023
    “…</p><p><br></p><p dir="ltr">In the fifth measurement technique, the numbers of sharp <b>surface projections/protrusions</b> were calculated by initially applying Canny's edge detection algorithm to generate an edge map of the cell mask image. …”
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  5. 5
    Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx

    Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

    Published 2025
    “…The study identifies the most effective fast-learning classifiers for hyperspectral urban forest mapping and underscores the potential of hyperspectral imaging and ensemble methods for scalable and operational tree species monitoring.…”
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  6. 6
    DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx

    DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx by Yuhong Huang (115702)

    Published 2021
    “…A total of 4,198 radiomics features were extracted from the pre-biopsy multi-parametric MRI (including dynamic contrast-enhancement T1-weighted images, fat-suppressed T2-weighted images, and apparent diffusion coefficient map) of each patient. …”
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  7. 7
    DataSheet_2_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.pdf

    DataSheet_2_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.pdf by Francesca Piludu (10706391)

    Published 2021
    “…We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort.…”
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  8. 8
    DataSheet_1_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.xlsx

    DataSheet_1_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.xlsx by Francesca Piludu (10706391)

    Published 2021
    “…We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort.…”
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