John Chiverton

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Tomographic biomedical images are commonly affected by an imaging artefact known as the partial volume (PV) effect. The PV effect produces voxels composed of a mixture of tissues in anatomical magnetic resonance imaging (MRI) data resulting in a continuity of these tissue classes. Anatomical MRI data typically consist of a number of contiguous regions of(More)
This paper describes a novel automatic statistical morphology skull stripper (SMSS) that uniquely exploits a statistical self-similarity measure and a 2-D brain mask to delineate the brain. The result of applying SMSS to 20 MRI data set volumes, including scans of both adult and infant subjects is also described. Quantitative performance assessment was(More)
A new fully automatic object tracking and segmentation framework is proposed. The framework consists of a motion-based bootstrapping algorithm concurrent to a shape-based active contour. The shape-based active contour uses finite shape memory that is automatically and continuously built from both the bootstrap process and the active-contour object tracker.(More)
An active contour based tracking framework is described that generates and integrates dynamic shape information without having to learn a priori shape constraints. This dynamic shape information is combined with dynamic pho-tometric foreground model matching and background mismatching. Boundary based optical flow is also used to estimate the location of the(More)
We are interested in segmentation and tracking using high-level shape information, particularly for objects that undergo arbitrary and smooth deformations , but without the a priori learning of shape constraints. We introduce a shape based level set active contour framework that learns shape information on-line, simultaneously combining the newly learnt(More)
Accurate automatic segmentation of anatomical structures is usually considered a difficult problem to solve because of anatomical variability and varying imaging conditions. A prior description of the shape of the anatomical structure to be segmented can reduce the ambiguity associated with the segmentation task. However this prior information has to be(More)
—Accurate classification of signals composed of two or more classification classes (e.g., biomedical imaging data with pathological structures) might utilize a density that takes account of the signal acquisition process. A new density based on a Gaussian point spread function (PSF) and another utilizing a phenomenological observation known as Benford's Law(More)