Medical Image Segmentation Using Artificial Neural Networks

Segmentation of tissues and structures from medical images is the first step in many image analysis applications developed for medical diagnosis. Development of treatment plans and evaluation of disease progression are other applications. These applications stem from the fact that diseases affect specific tissues or structures, lead to loss, atrophy (volume loss), and abnormalities. Consequently, an accurate, reliable, and automatic segmentation of these tissues and structures can improve diagnosis and treatment of diseases. Manual segmentation, although prone to rater drift and bias, is usually accurate but is impractical for large datasets because it is tedious and time consuming. Automatic segmentation methods can be useful for clinical applications if they have: 1) ability to segment like an expert; 2) excellent performance for diverse datasets; and 3) reasonable processing speed. Artificial Neural Networks (ANNs) have been developed for a wide range of applications such as function approximation, feature extraction, optimization, and classification. In particular, they have been developed for image enhancement, segmentation, registration, feature extraction, and object recognition. Among these, image segmentation is more important as it is a critical step for high-level processing such as object recognition. Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Hopfield, Cellular, and Pulse-Coupled neural networks have been used for image segmentation. These networks can be categorized into feed-forward (associative) and feedback (auto-associative) networks. MLP, Self-Organized Map (SOM), and RBF neural networks belong to the feed-forward networks while Hopfield, Cellular, and Pulse-Coupled neural networks belong to the feedback networks. This chapter is organized as follows. Section 2 explains methods that benefit from feedback networks such as Hopfield, Cellular, and Pulse-Coupled neural networks for image segmentation. In Section 3, we review the methods that use feedforward networks such as MLP and RBF neural networks. Then, we present our recent method. In this method, deep brain structures are segmented using Geometric Moment Invariants (GMIs) and MLP neural networks.