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Unsupervised Neural Techniques Applied to MR Brain Image Segmentation

DOI: 10.1155/2012/457590

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The primary goal of brain image segmentation is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting, since accurate segmentation in white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders such as dementia, schizophrenia or Alzheimer’s disease (AD). Then, image segmentation results in a very interesting tool for neuroanatomical analyses. In this paper we show three alternatives to MR brain image segmentation algorithms, with the Self-Organizing Map (SOM) as the core of the algorithms. The procedures devised do not use any a priori knowledge about voxel class assignment, and results in fully-unsupervised methods for MRI segmentation, making it possible to automatically discover different tissue classes. Our algorithm has been tested using the images from the Internet Brain Image Repository (IBSR) outperforming existing methods, providing values for the average overlap metric of 0.7 for the white and grey matter and 0.45 for the cerebrospinal fluid. Furthermore, it also provides good results for high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). 1. Introduction Nowadays, magnetic resonance imaging (MRI) systems provide an excellent spatial resolution as well as a high tissue contrast. Nevertheless, since actual MRI systems can obtain 16-bit depth images corresponding to 65535 gray levels, the human eye is not able to distinguish more than several tens of gray levels. On the other hand, MRI systems provide images as slices which compose the 3D volume. Thus, computer-aided tools are necessary to exploit all the information contained in an MRI. These are becoming a very valuable tool for diagnosing some brain disorders such as Alzheimer’s disease [1–5]. Moreover, modern computers, which contain a large amount of memory and several processing cores, have enough process capabilities for analyzing the MRI in reasonable time. Image segmentation consists in partitioning an image into different regions. In MRI, segmentation consists of partitioning the image into different neuroanatomical structures which corresponds to different tissues. Hence, analyzing the neuroanatomical structures and the distribution of the tissues on the image, brain disorders or anomalies can be figured out. Hence, the importance of having effective tools for grouping and recognizing different anatomical tissues, structures and fluids is growing with the improvement of


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