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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Imaging Studies II: Ultrasonography01:24

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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Related Experiment Video

Updated: Sep 21, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Artificial intelligence optimized image segmentation techniques for renal cyst detection.

Bhawna Dhruv1, Neetu Mittal1, Megha Modi2

  • 1AIIT, Amity University Uttar Pradesh, Noida, India.

Journal of Medical Engineering & Technology
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic medical image segmentation algorithm to precisely identify kidney cysts. The Genetic Algorithm (GA) demonstrated superior effectiveness and robustness in segmenting kidney CT images for better disease assessment.

Keywords:
Image segmentationgenetic algorithmkidneymedical imageoptimisation

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Medical images often suffer from low contrast, noise, and ill-defined boundaries, hindering accurate analysis.
  • Effective medical image segmentation is crucial for disorder identification, treatment planning, and surgical guidance.
  • Kidney cysts require precise identification due to potential links to severe kidney dysfunction.

Purpose of the Study:

  • To present an automatic medical image segmentation algorithm to address imaging challenges.
  • To accurately identify and demarcate kidney cysts for improved diagnostic capabilities.
  • To evaluate the performance of Genetic Algorithm (GA), Ant Colony Optimisation (ACO), and Fuzzy C Means (FCM) clustering for kidney cyst segmentation.

Main Methods:

  • Development of an automatic medical image segmentation algorithm.
  • Comparative analysis of GA, ACO, and FCM for segmenting kidney CT images.
  • Focus on precise identification and visualization of kidney cysts and surrounding pathologies.

Main Results:

  • The Genetic Algorithm (GA) exhibited superior performance in terms of visualization and pathological assessment.
  • GA provided better representation of kidney cysts, enhancing diagnostic assurance.
  • Experimental results confirmed GA's effectiveness and robustness in segmenting kidney CT images.

Conclusions:

  • Automatic segmentation using GA offers significant advantages for medical image analysis.
  • The proposed algorithm, particularly with GA, improves the assessment of kidney cyst extent and nature.
  • Enhanced visualization and diagnostic assurance are key benefits for medical practitioners and patients.