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Related Concept Videos

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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Imaging Studies III: Computed Tomography01:27

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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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Related Experiment Video

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Thyroid nodule recognition in computed tomography using first order statistics.

Wenxian Peng1,2,3, Chenbin Liu4, Shunren Xia5,6

  • 1Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China.

Biomedical Engineering Online
|June 9, 2017
PubMed
Summary
This summary is machine-generated.

First-order texture features in CT images effectively distinguish thyroid nodules from normal tissue. This method shows high accuracy, aiding radiologists in nodule detection and diagnosis.

Keywords:
Computed tomographyTexture analysisTexture featureThyroid nodule

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Area of Science:

  • Radiology
  • Medical Imaging Analysis
  • Biomarker Discovery

Background:

  • Computed tomography (CT) is crucial for early thyroid nodule detection.
  • Pixel intensity variations in CT images differentiate nodules from normal thyroid tissue.
  • Heterogeneous intensity characterizes nodules, while normal tissue is homogeneous.

Purpose of the Study:

  • To assess the feasibility of using first-order texture features for thyroid nodule identification in CT images.
  • To develop a system that assists radiologists in recognizing thyroid nodules.

Main Methods:

  • Utilized 284 thyroid CT images (150 healthy, 134 nodules).
  • Extracted first-order texture features (entropy, uniformity, intensity, standard deviation, kurtosis, skewness) from manually delineated regions of interest.
  • Employed support vector machine analysis for classification and evaluated performance using accuracy, sensitivity, specificity, PPV, NPV, and AUC.

Main Results:

  • Significant differences (P < 0.05) observed in entropy, uniformity, mean intensity, standard deviation, and skewness between normal and nodule tissues.
  • Achieved optimal classification with high performance metrics: accuracy (0.880), sensitivity (0.821), specificity (0.933), PPV (0.917), and NPV (0.854).

Conclusions:

  • First-order texture features serve as effective imaging biomarkers for thyroid nodules.
  • The developed system can aid radiologists in identifying nodules on CT images.