Automatic Landmark Extraction using Self-Organising Maps
نویسندگان
چکیده
A large number of registration techniques rely on manually selected landmark points. A system based on neural principles has been developed to automatically extract landmark types and positional information from magnetic resonance images. A single self-organising map is used to develop the features (landmark types) so that the final landmarks represent statistically significant contour sections. The combination of landmark types and positional information form the landmark set, which can then be used for automated registration. The paper discusses the landmark extraction system as well as the steps necessary for subsequent image registration.
منابع مشابه
Automatic Landmarking of 2D Medical Shapes Using the Growing Neural Gas Network
MR Imaging techniques provide a non-invasive and accurate method for determining the ultra-structural features of human anatomy. In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. Our approach is based on an automated landmark extraction algorithm which automatically selects points along the contour of the ventr...
متن کاملAutomatic landmark extraction using Growing Neural Gas (GNG)
A new method for automatically building statistical shape models from a set of training examples and in particular from a class of hands. In this method, landmark extraction is achieved using a self-organising neural network, the Growing Neural Gas (GNG), which is used to preserve the topology of any input space. Using GNG, the topological relations of a given set of deformable shapes can be le...
متن کاملAutomatic Registration of Complex Images Using a Self Organizing Neural System 1
| We present a system for automatic mapping of complex gray-scale images onto each other. The system includes a Neocognitron-like structure for hierarchical feature extraction, a 3D Self Organising Map to determine feature classes for unsupervised training, and algorithmic methods for landmark correspondence and image warping. We present results showing successful registration of MRI brain scan...
متن کاملAutomatic Classification using Self-Organising Neural Networks in Astrophysical Experiments
Self-Organising Maps (SOMs) are effective tools in classification problems, and in recent years the even more powerful Dynamic Growing Neural Networks, a variant of SOMs, have been developed. Automatic Classification (also called clustering) is an important and difficult problem in many Astrophysical experiments, for instance, Gamma Ray Burst classification, or gamma-hadron separation. After a ...
متن کاملAutomatically Building 2D Statistical Shapes Using the Topology Preservation Model GNG
Image segmentation is very important in computer based image interpretation and it involves the labeling of the image so that the labels correspond to real world objects. In this study, we utilise a novel approach to automatically segment out the ventricular system from a series of MR brain images and to recover the shape of hand outlines from a series of 2D training images. Automated landmark ...
متن کامل