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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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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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An Efficient Pipeline for Abdomen Segmentation in CT Images.

Hasan Koyuncu1, Rahime Ceylan2, Mesut Sivri3

  • 1Engineering Faculty, Department of Electrical and Electronics Engineering, Selcuk University, 42250, Konya, Turkey. hasankoyuncu@selcuk.edu.tr.

Journal of Digital Imaging
|October 26, 2017
PubMed
Summary

This study introduces a new statistical pipeline for accurate abdomen segmentation in computed tomography (CT) scans, overcoming common image quality issues. The method achieves high performance, enabling better real-time medical diagnoses.

Keywords:
Abdomen segmentationComputed tomographyEdge detectionImage registrationStatistical pipeline

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Computed tomography (CT) scans often present image quality challenges like discontinuous edges and poor contrast, hindering accurate abdomen segmentation.
  • Existing segmentation techniques struggle with these handicaps, impacting their utility in real-time diagnostic systems.
  • Efficient abdomen segmentation is crucial for subsequent analyses such as feature selection and classification in medical imaging.

Purpose of the Study:

  • To develop an efficient and robust statistical pipeline for abdomen segmentation in CT scans.
  • To create a method that is unaffected by common image quality disadvantages inherent in CT imaging.
  • To provide a reliable foundation for real-time diagnostic systems requiring precise abdominal region analysis.

Main Methods:

  • A statistical pipeline integrating intensity-based, morphological, and histogram-based procedures was designed.
  • The pipeline was optimized using 16 training CT images with inherent segmentation disadvantages.
  • Performance was evaluated on 16 test and 26 validation CT images using six key performance metrics.

Main Results:

  • The proposed method demonstrated high segmentation accuracy across training, testing, and validation datasets.
  • Achieved average Jaccard index of 98.95/99.36/99.57%, Dice coefficient of 99.47/99.67/99.79%, and classification accuracy of 99.38/99.63/99.87%.
  • The pipeline proved effective in overcoming common CT image handicaps, ensuring reliable abdomen segmentation.

Conclusions:

  • A novel statistical pipeline effectively performs abdomen segmentation in CT scans, robust to common image quality issues.
  • The developed method offers a significant advancement for applications requiring precise abdomen segmentation, including organ and tumor analysis.
  • This study provides a detailed and reliable approach for abdomen segmentation, supporting the development of advanced real-time diagnostic tools.