Artificial neural network based approach for medical image segmentation
Summary :
Table of Contents
- Abstract
- Introduction
- Pixel classification method based on kohonen self organizing map
- Implementation of kohonen algorithm
- Experiments over medical images
- Conclusion
- Bibliography
Abstract
image segmentation plays a crucial role in many medical imaging applications and is an important but
inherently difficult problem. This paper discusses the method that classifies unsupervised image using a
Kohonen self-organizing map (SOM) neural network. Kohonen has developed an algorithm with self organizing properties for a network of adaptive elements. These elements receive an input signal and the signal representations are automatically mapped onto a set of output responses so that these responses acquire the same topological order as the input signal. Images can be used as input signals and the networks can adjust to extract certain topological features. image segmentation can be performed satisfactorily. By empirical knowledge, it can be supposed that the number of neurons increases, so does the quality of segmentation. This paper concentrates on some MR (Magnetic Resonance) brain images segmentation and also the implementation aspects of SOM.
Keywords: artificial neural network (ANN), Self-Organizing Map (SOM), image segmentation
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