Michal Swiercz

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¶ It is well known that intracranial pressure (ICP) is influenced by an array of predictable and unpredictable factors, which gives rise to a signal heavily loaded with stochastic, i.e. random components. Hence, statistical modelling of this signal has proved to be of limited utility, in spite of the very sophisticated mathematical methods applied. In(More)
¶ Intracranial pressure (ICP) is commonly used by neurosurgeons as a source of valuable information about the current condition of the neurosurgical patient. Nevertheless, despite years of effort, extracting clinically valuable information from the ICP signal is still problematical. Approaches, using current values of ICP, may fail to disclose imminent(More)
In this paper a fast, parallel watershed algorithm for seg-mentation of digital grey-scale images is presented. We show an original parallelisation technique based on the " shared nothing " principle and its application to a modified path-tracing watershed algorithm, which allows a vast majority of computations to be broken up into several independent tasks(More)
The paper presents an effective and robust method of classifying binary patterns. It starts with classification of foreground pixels of binary image into several spatial classes, which is performed using morphological image processing. By performing this classification with structuring elements of increasing sizes, the spatial class distribution functions(More)
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