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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Profile preferentially partial occlusion removal for three-dimensional integral imaging.

S L Xie, P Wang, X Z Sang

    Optics Express
    |November 10, 2016
    PubMed
    Summary

    This study introduces a novel occlusion removal method for integral imaging, focusing on texture-rich occlusion profiles for accurate depth estimation. The technique effectively removes occlusions while preserving scene details, demonstrating robustness across various textures.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • 3D Reconstruction

    Background:

    • Occlusion presents a significant challenge in integral imaging, hindering accurate 3D reconstruction.
    • Traditional methods struggle with detailed occlusion regions, leading to information loss.

    Purpose of the Study:

    • To develop an efficient and robust occlusion removal method for integral imaging.
    • To accurately remove occluded details while preserving the maximum amount of scene information.

    Main Methods:

    • A profile-preferential partial occlusion removal approach is proposed.
    • Leverages significant texture structure within occlusion profiles for reliable depth estimation.
    • Integrates elemental images into a 4D light field for consistent depth and occlusion decisions.

    Main Results:

    • The method accurately removes occlusion details.
    • The occluded scene is retained to a maximum extent.
    • Demonstrates efficiency and robustness for various occlusion textures.

    Conclusions:

    • The proposed method effectively addresses occlusion removal in integral imaging.
    • Utilizing texture information in occlusion profiles enhances depth estimation and reconstruction accuracy.