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Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network.

C Priya1, S Sudha2

  • 1Department of ECE, Syed Ammal Engineering College, Landhai, India. harinikpriya@gmail.com.

Journal of Medical Systems
|March 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm using Fruitfly optimization for accurate epicardial and pericardial fat segmentation in CT scans. The method enhances diagnostic precision for coronary heart disease risk assessment.

Keywords:
Epicardial fatFat segmentationFruitfly algorithmGWOModified region growingOptimal neural network

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Cardiovascular Disease Research

Background:

  • Epicardial adipose tissue (EAT) is strongly linked to coronary heart disease (CHD), posing diagnostic challenges due to its overlap with pericardial fat and influence from individual factors.
  • Accurate segmentation and classification of cardiac fats are crucial for precise risk assessment and treatment strategies.
  • Computed Tomography (CT) is a primary diagnostic tool, necessitating improved algorithmic approaches for fat analysis.

Purpose of the Study:

  • To develop and validate a robust algorithm for accurate segmentation and classification of epicardial, pericardial, and mediastinal fats using CT images.
  • To enhance the diagnostic capabilities for conditions associated with visceral fat accumulation around the heart.
  • To improve the efficiency and accuracy of cardiac fat quantification in clinical practice.

Main Methods:

  • Implementation of a modified region growing algorithm optimized with the Fruitfly Algorithm for precise fat segmentation in CT images.
  • Extraction of Gray-Level Co-occurrence Matrix (GLCM) features from CT images for fat characterization.
  • Development of a Grey Wolf Optimizer (GWO) based neural network for the classification of cardiac fats.

Main Results:

  • The proposed methodology successfully segments epicardial, pericardial, and mediastinal fats with high accuracy, sensitivity, and specificity.
  • Quantitative analysis demonstrated a clear distinction between different types of cardiac fats, outperforming existing methods.
  • Performance metrics including accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR) were systematically evaluated and compared.

Conclusions:

  • The Fruitfly Algorithm-based modified region growing approach offers a significant advancement in the accurate segmentation and classification of cardiac fats from CT scans.
  • This computational technique holds promise for improving the early detection and management of cardiovascular diseases linked to adipose tissue distribution.
  • The study advocates for the integration of advanced computational methods to enhance efficiency and precision in healthcare diagnostics.