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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

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The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Oral Cavity01:11

Oral Cavity

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The oral cavity, or the mouth, is a complex structure in humans that plays a vital role in our day-to-day lives. Its role is not only in chewing and swallowing food; it also plays a role in speech and facial expressions.
Teeth: The teeth are the hardest structures in our bodies. Humans have two sets of teeth throughout their lifetime: deciduous (baby) teeth and permanent teeth. Each tooth consists of several parts: the crown (visible part), the root (embedded in the jaw), enamel (hard outer...
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Nose and Nasal Cavity01:24

Nose and Nasal Cavity

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The nose is composed of an observable exterior segment (external nose) and an internal segment within the skull known as the nasal cavity (internal nose). The external nose, visible on the face, consists of a framework of bone and hyaline cartilage enveloped in skin and muscle and lined with a mucous membrane. This structure is supported by the frontal bone, nasal bones, and maxillary bone and is supplemented by a cartilaginous framework comprising the septal nasal cartilage, lateral nasal...
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Standing Waves in a Cavity01:28

Standing Waves in a Cavity

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A household microwave and lasers are examples of standing electromagnetic waves in a cavity. When two conducting metal plates are placed parallel at the nodal planes, it creates a cavity where standing waves are formed. The cavity between the two planes is analogous to a stretched string held at the points x = 0 and x = L. Here, the distance 'L' between the two planes must be an integer multiple of half of the wavelength. The wavelengths that satisfy this condition are given by:
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Updated: Feb 5, 2026

Dynamic Navigation in Endodontics: Guided Access Cavity Preparation by Means of a Miniaturized Navigation System
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CBCT image based segmentation method for tooth pulp cavity region extraction.

Lei Wang1, Ju-Peng Li1, Zhi-Pu Ge2

  • 11 Signal and image processing laboratory, School of Electronic Information Engineering, Beijing Jiao tong University , Beijing , China.

Dento Maxillo Facial Radiology
|September 15, 2018
PubMed
Summary
This summary is machine-generated.

A new method efficiently segments tooth pulp cavities in cone beam CT images. This approach offers more accurate and robust results than existing methods, improving dental research and applications.

Keywords:
medical image processingreciprocal cross entropytooth pulp cavity region segmentation

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Area of Science:

  • Dental Imaging
  • Medical Image Analysis
  • Computational Anatomy

Background:

  • Accurate segmentation of the tooth pulp cavity is crucial for dental research and clinical applications.
  • Existing segmentation methods often face challenges with accuracy and robustness.

Purpose of the Study:

  • To develop and evaluate an efficient method for segmenting tooth pulp cavity regions in cone beam CT (CBCT) images.
  • To improve the accuracy and reliability of pulp cavity segmentation for further research.

Main Methods:

  • CBCT images of 50 teeth were analyzed.
  • Image preprocessing included gamma transform.
  • Fusion of greyscale, neighbor average greyscale, and gradient information.
  • Segmentation utilized the plane intercept histogram of reciprocal cross entropy algorithm to find an optimal threshold for binarization.

Main Results:

  • The proposed method achieved accurate and complete segmentation of tooth pulp cavities.
  • It demonstrated superior robustness against interferences compared to popular methods (reciprocal cross entropy, active contour, region growing, level set).
  • Quantitative analysis confirmed the method's effectiveness with low average non-coincidence rate (RANOA).

Conclusions:

  • The developed method efficiently extracts tooth pulp cavity regions from dental CBCT images.
  • It provides more accurate segmentation results compared to existing techniques across various conditions.
  • The method is suitable for subsequent applications in dental research and practice.