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An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network.

S Amiri¹, M M Movahedi¹, K Kazemi²

¹Department of Medical Physics and Biomedical Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.

Journal of Biomedical Physics & Engineering

|December 16, 2014

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This study presents an automated algorithm using multi-layer perceptron neural networks (MLPNN) for segmenting brain tissues in MR images. The method accurately identifies gray matter (GM) and white matter (WM), aiding in neuro-degenerative disorder research.

Area of Science:

Medical Imaging
Neuroscience
Artificial Intelligence

Background:

Accurate brain tissue segmentation in MR images is crucial for analyzing neuro-degenerative disorders and morphological differences.
Image artifacts like noise and low contrast can lead to classification errors in segmentation.
Volumetric analysis of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) requires precise segmentation.

Purpose of the Study:

To develop an automated algorithm for segmenting human brain 2D structural MR images.
To identify and classify white matter (WM) and gray matter (GM) tissues.
To utilize multi-layer perceptron neural networks (MLPNN) for improved MR image segmentation.

Main Methods:

An automated algorithm based on multi-layer perceptron neural networks (MLPNN) was developed.

Keywords:

Artificial neural networks Image segmentation Multi-layer perceptron

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2D MR images undergo intensity enhancement and removal of non-brain tissue.

Each pixel is characterized by 13 features (8 statistical, 5 non-statistical) for classification.

Main Results:

The algorithm was evaluated on 20 real MR images, demonstrating robust performance.
Average Jaccard similarity for GM was 75.7% and for WM was 67.8%.
Average Dice similarity for GM was 86.0% and for WM was 80.7%.

Conclusions:

The developed MLPNN-based method shows promising results for 2D MR image segmentation.
The algorithm effectively categorizes white matter (WM) and gray matter (GM).
This method can assist in the segmentation of MR images, particularly for WM and GM analysis.