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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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...
Vector Operations01:20

Vector Operations

Vectors are physical quantities that have both magnitude and direction. The vector operations include addition, subtraction, and scalar multiplication.
A vector multiplied by a scalar value is called scalar multiplication. The result obtained is a new vector with a different magnitude. If the scalar is positive, the direction of the vector remains the same, but if it is negative, the direction of the vector is reversed. For example, the product of the mass and velocity yields the momentum.
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Operations on images using quad trees.

G M Hunter1, K Steiglitz

  • 1MEMBER, IEEE, Decisions and Designs, Inc., McLean, VA 22101.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces efficient quad tree algorithms for image representation and polygon boundary processing. The new methods enable rapid superposition of multiple quad trees and optimal polygon coloring.

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Area of Science:

  • Computer Graphics
  • Image Processing
  • Computational Geometry

Background:

  • Quad trees offer hierarchical spatial data structures for image representation.
  • Efficient algorithms are needed for manipulating complex image data, such as polygons.

Purpose of the Study:

  • To present novel algorithms for quad tree superposition and polygon boundary representation.
  • To develop efficient methods for coloring the interior of polygons using quad trees.

Main Methods:

  • An algorithm for superposing N quad trees in time proportional to the total number of nodes.
  • Warnock-type algorithms for building quad trees of polygon boundaries.
  • Algorithms for coloring polygon interiors based on quad tree representations.

Main Results:

  • Superposition of N quad trees achieved in time proportional to their total nodes.
  • Polygon boundary and interior coloring algorithms demonstrated with O(v + p + q) time complexity.
  • Algorithms shown to be asymptotically optimal for fixed resolution (q).

Conclusions:

  • The developed quad tree algorithms provide efficient solutions for image representation and polygon processing.
  • The methods offer significant performance improvements, especially for large datasets and high resolutions.
  • This work contributes to advancements in computational geometry and computer graphics rendering.