Ewout Vansteenkiste

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There is a growing interest in using multiresolution noise filters in a variety of medical imaging applications. We review recent wavelet denoising techniques for medical ultrasound and for magnetic resonance images and discuss some of their potential applications in the clinical investigations of the brain. Our goal is to present and evaluate noise(More)
Even though it is known that neonatal seizures are associated with acute brain lesions, the relationship of electroencephalographic (EEG) seizures to acute perinatal brain lesions visible on magnetic resonance imaging (MRI) has not been objectively studied. EEG source localization is successfully used for this purpose in adults, but it has not been(More)
In patients with intractable epilepsy, focal cortical dysplasia (FCD) is the most frequent malformation of cortical development. Identification of subtle FCD lesions using brain MRI scans is very often based on the cortical thickness measurement, where brain cortex segmentation is required as a preprocessing step. However, the accuracy of the selected(More)
In this paper we present a novel 3D scanner to capture the texture characteristics of worn carpets into images of the depth. We first compare our proposed scanner to a Metris scanner previously attempted for this application. Then, we scan the surface of samples from the standard EN1471 using our proposed scanner. We found that our proposed scanner offers(More)
Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the(More)
Fuzzy clustering techniques have been widely used in automated image segmentation. However, since the standard fuzzy c-means (FCM) clustering algorithm does not consider any spatial information, it is highly sensitive to noise. In this paper, we present an extension of the FCM algorithm to overcome this drawback, by incorporating spatial neighborhood(More)
The quantitative analysis of medical ultrasound images for the purpose of diagnosis is a difficult task due to the speckle noise present in the images. Nowadays medical doctors depend strongly on the visual interpretation of the images which is subjective to some account. Trying to reduce this noise should assist the experts in a better understanding of(More)
Segmenting cerebral blood vessels is of great importance in diagnostic and clinical applications, especially in quantitative diagnostics and surgery on aneurysms and arteriovenous malformations (AVM). Segmentation of CT angiography images requires algorithms robust to high intensity noise, while being able to segment low-contrast vessels. Because of this,(More)
Current clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For three-dimensional (3D) images, however, these models have been little explored so far. In this work, first, two novel designs of a multislice channelized(More)
In this letter, we present a new FCM-based method for spatially coherent and noise-robust image segmentation. Our contribution is twofold: 1) the spatial information of local image features is integrated into both the similarity measure and the membership function to compensate for the effect of noise; and 2) an anisotropic neighborhood, based on phase(More)