Biomedical subjects

E A Ashton

Publications and source records attributed to E A Ashton.

3 recordsLinked to original sources

PubMed1997

A novel volumetric feature extraction technique with applications to MR images.

A semiautomated feature extraction algorithm is presented for the extraction and measurement of the hippocampus from volumetric magnetic resonance imaging (MRI) head scans. This algorithm makes use of elements of both deformable model and region growing techniques and allows incorporation of a priori operator knowledge of hippocampal location and shape. Experimental results indicate that the algorithm is able to estimate hippocampal volume and asymmetry with an accuracy which approaches that of laborious manual outlining techniques.

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PubMed1995

Segmentation and feature extraction techniques, with applications to MRI head studies.

To obtain a three-dimensional reconstruction of the hippocampus from a volumetric MRI head study, it is necessary to separate that structure not only from the surrounding white matter, but also from contiguous areas of gray matter--the amygdala and cerebral cortex. At present it is necessary for a physician to manually segment the hippocampus on each slice of the volume to obtain such a reconstruction. This process is time consuming, and is subject to inter- and intraoperator variation as well as large discontinuities between slices. We propose a novel technique, making use of a combination of gray scale and edge-detection algorithms and some a priori knowledge, by which a computer may make an unsupervised identification of a given structure through a series of contiguous images. This technique is applicable even if the structure includes so-called false contours or missing contours. Applications include three-dimensional reconstruction of difficult-to-segment regions of the brain, and volumetric measurements of structures from series of two-dimensional images.

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PubMed1995

Multiple resolution Bayesian segmentation of ultrasound images.

We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation for a probabilistic segmentation algorithm. Results appear to provide improvements over previous techniques in terms of low-contrast detail and accuracy.

Algorithms↗