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In this paper a novel multi-stage automatic method for brain tumour detection and neovasculature assessment is presented. First, the brain symmetry is exploited to register the magnetic resonance (MR) series analysed. Then, the intracranial structures are found and the region of interest (ROI) is constrained within them to tumour and peritumoural areas(More)
This paper presents a study aimed to assess applicability of artificial neural networks (ANNs) in human activity recognition from simple features derived from accelerometric signals. Secondary goal was to select the most descriptive signal features and sensor locations to be used as inputs to ANNs. Five triaxial accelerometers were attached to human body in(More)
  • Marcin Rudzki
  • 2011
This paper presents an automatic method for image contrast enhancement necessary as a preprocessing step during vasculature detection in medical images. The method takes as input two volumetric images: one containing the original data from a computed tomography (CT) study and the other one containing segmented liver as a binary mask. The aim of the method(More)
The paper presents a Computer Aided Diagnosis (CAD) software for brain tumor detection and analysis from Magnetic Resonance Imaging (MRI). The software utilizes a novel multi-stage method that is capable of dealing with three main types of brain tumors, i.e. HG gliomas, metastases and meningiomas and yields object masks as well as quantitative parameters.(More)
The paper presents an automatic method for liver vasculature segmentation in volumetric images obtained during Computed Tomography (CT) patient examination. The method, apart from the CT image, also requires an additional image containing binary liver mask. A modified multiscale vesselness filter (VF) performs vasculature detection. VF response is used to(More)
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