Scale-invariant segmentation of dynamic contrast-enhanced perfusion MR-images with inherent scale selection

Selection of the best set of scales is problematic when developing signaldriven approaches for pixel-based image segmentation. Often, different possibly conflicting criteria need to be fulfilled in order to obtain the best tradeoff between uncertainty (variance) and location accuracy. The optimal set of scales depends on several factors: the noise level present in the image material, the prior distribution of the different types of segments, the class-conditional distributions associated with each type of segment as well as the actual size of the (connected) segments. We analyse, theoretically and through experiments, the possibility of using the overall and class-conditional error rates as criteria for selecting the optimal sampling of the linear and morphological scale spaces. It is shown that the overall error rate is optimised by taking the prior class distribution in the image material into account. However, a uniform (ignorant) prior distribution ensures constant class-conditional error rates. Consequently, we advocate for a uniform prior class distribution when an uncommitted, scaleinvariant segmentation approach is desired. Experiments with a neural net classifier developed for segmentation of dynamic MR images, acquired with a paramagnetic tracer, support the theoretical results. Furthermore, the experiments show that the addition of spatial features to the classifier, extracted from the linear or morphological scale spaces, improves the segmentation result compared to a signal-driven approach based solely on the dynamic MR signal. The segmentation results obtained from the two types of features are compared using two novel quality measures that characterise spatial properties of labelled images

基于 P2P 网络的信息过滤与推荐技术研究

共享信息的集中存储对存放这些信息的服务器提出了较高的要求，同时，服务器将成为整个系统的瓶颈。为此，提出了一种基于P2P 的信息共享与推荐模型，解决了信息集中存放产生的问题。接着，对该模型中涉及到的基于内容的过滤，提出了一种基于词汇链的方法，较好地解决了纯粹单一关键词无法准确描述文本的问题，并对信息推荐中使用最成功的协同过滤算法进行了描述。给出了文本过滤的实验结果及其分析。国家“863”计划基金资助项目(2001AA114110) 福建省自然科学基金资助项目(A0310009) 福建省科技计划基金资助项目(2001J005) 厦门大学“985”二期信息创新平台项目和厦门大学院士启动基金资助项

An investigation into the use of genetic algorithms for shape recognition

The use of the genetic algorithm for shape recognition has been investigated in relation to features along a shape boundary contour. Various methods for encoding chromosomes were investigated, the most successful of which led to the development of a new technique to input normalised 'perceptually important point' features from the contour into a genetic algorithm. Chromosomes evolve with genes defining various ways of 'observing' different parts of the contour. The normalisation process provides the capability for multi-scale spatial frequency filtering and fine/coarse resolution of the contour features. A standard genetic algorithm was chosen for this investigation because its performance can be analysed by applying schema analysis to the genes. A new method for measurement of gene diversity has been developed. It is shown that this diversity measure can be used to direct the genetic algorithm parameters to evolve a number of 'good' chromosomes. In this way a variety of sections along the contour can be observed. A new and effective recognition technique has been developed which makes use of these 'good' chromosomes and the same fitness calculation as used in the genetic algorithm. Correct recognition can be achieved by selecting chromosomes and adjusting two thresholds, the values of which are found not to be critical. Difficulties associated with the calculation of a shape's fitness were analysed and the structure of the genes in the chromosome investigated using schema and epistatic analysis. It was shown that the behaviour of the genetic algorithm is compatible with the schema theorem of J. H. Holland. Reasons are given to explain the minimum value for the mutation probability that is required for the evolution of a number of' good' chromosomes. Suggestions for future research are made and, in particular, it is recommended that the convergence properties of the standard genetic algorithm be investigated