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Updated: Jan 22, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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Brain Tumor Detection Using Depth-First Search Tree Segmentation.

S Janardhanaprabhu1, V Malathi2

  • 1Department of Electronics and Communication Engineering, AURCM, Madurai, Tamil Nadu, India. sjanatce@gmail.com.

Journal of Medical Systems
|June 30, 2019
PubMed
Summary

This study introduces a Depth-First Search (DFS) algorithm for segmenting brain tumors in Magnetic Resonance Imaging (MRI). The DFS method enhances accuracy and reduces computational complexity for precise tumor detection.

Keywords:
ANFISMedical image processingSVM

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Area of Science:

  • Medical Image Processing
  • Computational Anatomy
  • Graph Theory Applications

Background:

  • Medical image processing enables visual perception of anatomical abnormalities.
  • Image segmentation is crucial for identifying substances and their margins in medical images.
  • Accurate brain tumor diagnosis via Magnetic Resonance Imaging (MRI) necessitates precise segmentation due to large data volumes.

Purpose of the Study:

  • To present an automated segmentation technique for precise brain tumor detection in MRI.
  • To introduce a novel Depth-First Search (DFS) segmentation algorithm based on graph theory.
  • To compare the proposed algorithm's performance with existing systems and classifiers.

Main Methods:

  • Pixels in MRI scans are organized into a tree-like structure based on proximity using a graph theory approach.
  • A Depth-First Search (DFS) algorithm is employed for image segmentation.
  • Performance evaluation includes comparison with other systems and assessment of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) classifiers.

Main Results:

  • The proposed DFS segmentation algorithm effectively distinguishes healthy cells from brain tumor-affected cells.
  • Experimental results demonstrate reduced computational complexity compared to existing methods.
  • Enhanced accuracy in tumor segmentation was achieved with the proposed approach.

Conclusions:

  • The developed DFS-based segmentation algorithm offers an accurate and computationally efficient solution for brain tumor analysis in MRI.
  • This method aids in obtaining precise information crucial for effective treatment planning.
  • The study highlights the potential of graph theory-based image processing for medical diagnostics.