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Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature

L Arokia Jesu Prabhu1, A Jayachandran2

  • 1Department of CSE, Chandy College of Engineerin, Tuticorin, India. arokiajeruprabhu@gmail.com.

Journal of Medical Systems
|November 5, 2018
PubMed

Insights

This study introduces a novel hybrid Fuzzy Support Vector Machine (SVM) method for classifying meningioma brain tumors using MRI images. The developed technique achieved a high accuracy of 91.64% in identifying brain tumors.

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Oncology

Background:

  • Meningioma is a common brain tumor arising from meninges, accounting for 30% of all brain tumors.
  • Accurate classification of meningioma is crucial for effective patient treatment and management.
  • Existing segmentation and classification methods require improvement for enhanced diagnostic accuracy.

Purpose of the Study:

  • To develop and evaluate a novel hybrid Fuzzy SVM classification method for meningioma brain tumors using MRI.
  • To improve the accuracy of brain tumor classification through advanced image processing techniques.
  • To compare the performance of the proposed method against traditional SVM and other models.

Main Methods:

  • The proposed method involves four stages: cell boundary processing using Mathematical Morphology, adaptive thresholding for segmentation, and a novel hybrid Fuzzy SVM for classification.
  • Mathematical Morphology was employed for discrete region processing of cell boundaries.
  • An adaptive binarization technique was utilized for crucial segmentation steps in medical image analysis.

Main Results:

  • The proposed hybrid Fuzzy SVM method achieved a classification accuracy of 91.64% for meningioma brain tumors.
  • This represents a significant improvement over standard SVM (74.24%), MM with FSVM (82.67%), and MM with RBF (62.71%).
  • The integration of Mathematical Morphology and hybrid Fuzzy SVM demonstrated superior performance in meningioma classification.

Conclusions:

  • The developed hybrid Fuzzy SVM method offers a highly accurate and effective approach for meningioma brain tumor classification from MRI data.
  • The study highlights the potential of combining advanced image processing techniques like Mathematical Morphology with hybrid classifiers for improved cancer diagnosis.
  • This research contributes to the field of automated medical image analysis for brain tumor detection and characterization.
Keywords:
ClassificationFeature extractionMRISVMTexturebrain tumor

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