Biomedical subjects

R Velthuizen

Publications and source records attributed to R Velthuizen.

3 recordsLinked to original sources

PubMed1998

Automatic tumor segmentation using knowledge-based techniques.

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRI's) of the human brain is presented. The MRI's consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised k-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

Algorithms↗

PubMed1997

Monitoring brain tumor response to therapy using MRI segmentation.

The performance evaluation of a semi-supervised fuzzy c-means (SFCM) clustering method for monitoring brain tumor volume changes during the course of routine clinical radiation-therapeutic and chemo-therapeutic regimens is presented. The tumor volume determined using the SFCM method was compared with the volume estimates obtained using three other methods: (a) a k nearest neighbor (kNN) classifier, b) a grey level thresholding and seed growing (ISG-SG) method and c) a manual pixel labeling (GT) method for ground truth estimation. The SFCM and kNN methods are applied to the multispectral, contrast enhanced T1, proton density, and T2 weighted, magnetic resonance images (MRI) whereas the ISG-SG and GT methods are applied only to the contrast enhanced T1 weighted image. Estimations of tumor volume were made on eight patient cases with follow-up MRI scans performed over a 32 week interval during treatment. The tumor cases studied include one meningioma, two brain metastases and five gliomas. Comparisons with manually labeled ground truth estimations showed that there is a limited agreement between the segmentation methods for absolute tumor volume measurements when using images of patients after treatment. The average intraobserver reproducibility for the SFCM, kNN and ISG-SG methods was found to be 5.8%, 6.6% and 8.9%, respectively. The average of the interobserver reproducibility of these methods was found to be 5.5%, 6.5% and 11.4%, respectively. For the measurement of relative change of tumor volume as required for the response assessment, the multi-spectral methods kNN and SFCM are therefore preferred over the seedgrowing method.

Adult↗

PubMed1993

Enhanced detection of normal retinal nerve-fiber striations using a charge-coupled device and digital filtering.

We used a 1024 x 1024 pixel, 15-microns, 16-bit-encoding, multi-pin-phase charge-coupled device (CCD) to obtain images of the normal human retinal nerve fiber layer. This device, which operates at room temperature, offers significantly better signal-to-noise ratio, linearity, and dynamic range than do photographic film, video imaging techniques, or commercially available CCDs. We demonstrate the use of a nonlinear digital filter, together with filter windows, that enhances fine detail of NFL striations, while suppressing noise, in limited areas of the CCD images. High-sensitivity imaging of this type, together with appropriate digital processing, may prove useful in diagnosing and following nerve-fiber-layer damage due to glaucoma.

Fundus Oculi↗