Segmentation of noisy images

Electrical Engineering, Mathematics and Computer Scienc

Automated extraction, labelling and analysis of the coronary vasculature from arteriograms

For clinical decision-making and documentation purposes we have developed techniques to extract, label and analyze the coronary vasculature from arteriograms in an automated, quantitative manner. Advanced image processing techniques were applied to extract and analyze the vasculatures from non-subtracted arteriograms while artificial intelligence techniques were employed to assign anatomical labels

Fuzzy-Rule Generation Using Incremental Learning for a Knowledge-Based Anaesthesia Monitor

This paper discusses an incremental learning algorithm: Fuzzy Incremental Learning. The algorithm was developed to obtain a rule-base for a knowledge-based signal analysis system to be used in anaesthesia. The algorithm has a domain-dependent part, which can be obtained by simplified expert interviews. Furthermore, it deals with uncertainty in the observations and it is capable of updating the knowledge base as soon as new events have been observed. 1 Introduction This section explains the motivation for the Fuzzy Incremental Learning algorithm. It discusses the need for a knowledge-based signal analysis system in anaesthesia monitoring and the problem of knowledge acquisition. 1.1 Anaesthesia Monitoring The Intelligent Anaesthesia Monitor project, in which the present study is embedded, aims at a knowledgebased signal analysis system that improves a patient's safety during surgery. Currently, the anaesthetist is overloaded by data from the equipment surrounding the patient in moder..

Fundamentals of Image Processing

Imaging Science and TechnologyApplied Science

A Proposal For Fuzzy Rule Generation Using Temporal Reasoning

Knowledge acquisition is difficult, especially when the domain knowledge is not structured. In this paper a framework based on machine learning is proposed in order to generate a rule-base for signal analysis in the case of anesthesia monitoring. During surgery, clinical parameters are measured. From training samples, rules for a knowledge-based system can be learned that describe alarm situations in terms of the clinical parameters. The training samples consist of time series of the clinical parameters and an expert decision which defines an alarm or noalarm situation. The samples are classified into concepts. Fuzzy rules are generated that relate the concepts with a decision. By means of temporal reasoning on the time series, fuzzy temporal concepts and rules can be generated. The (temporal) concepts are used to recognize an alarm situation. The framework can be applied to image sequence analysis as well. 1. INTRODUCTION Since the eigthies expert systems are extensively used to solv..

Estimating Facial Expressions by Reasoning

This paper discusses our ideas about the automatic estimation of facial expressions. This research takes place within an intended project which has as objective: "The design and implementation of a non-invasive, audio-visually controlled facial synthesis system". Here, the (acted) facial expressions of a user have to be recognized on the basis of audio and video recordings, which can then be applied to a computer model of a human face, or even of an animation character, to create life-like animation films