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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

MRI segmentation using dialectical optimization.

Wellington P dos Santos¹, Francisco M de Assis, Ricardo E de Souza

¹Departamento de Engenharia Elétrica, Universidade Federal de Campina Grande, Campina Grande, Paraíba, Brazil. wps@dsc.upe.br

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 8, 2009

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This study introduces a novel dialectics-based optimization method using fuzzy logic for improved image segmentation. The new approach enhances results compared to traditional k-means, offering better quantization error in MR image analysis.

Area of Science:

Computational Intelligence
Image Processing
Biomedical Engineering

Background:

Computational Intelligence methods like genetic algorithms are inspired by biological and social sciences.
Existing optimization methods have limitations in modeling complex interactions.
Image segmentation is crucial for medical image analysis.

Purpose of the Study:

To propose a new optimization method based on dialectics and fuzzy logic.
To apply this method to image segmentation, specifically for Magnetic Resonance (MR) images.
To compare the proposed method's performance against established algorithms.

Main Methods:

Developed a dialectics-based optimization approach using fuzzy membership functions.
Modeled interactions between 'poles' (basic units) within dialectical systems.

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Implemented a segmentation method optimizing k-means clustering with the proposed dialectics approach.

Utilized synthetic multispectral MR brain images from the BrainWeb simulator.

Main Results:

The proposed dialectics-based method demonstrated improved performance in MR image segmentation.
Quantization error was reduced compared to standard k-means clustering.
The method effectively models the influence of interactions between system components.

Conclusions:

The novel dialectics-based optimization method offers a promising advancement for image segmentation tasks.
Fuzzy logic integration effectively models complex interactions, leading to enhanced results.
This approach provides a valuable alternative for MR image analysis, outperforming traditional k-means.