Biomedical subjects

W A Yasnoff

Publications and source records attributed to W A Yasnoff.

5 recordsLinked to original sources

PubMed1992

US HealthLink: a national information resource for health care professionals.

US HealthLink is a new, comprehensive online medical information system designed specifically for health care professionals. Available to individuals for a fixed fee, it includes literature, news, diagnostic decision support, drug interactions, electronic mail, and bulletin boards. It also provides user-specific current awareness via clipping service, and fax delivery of both clipping and electronic mail information. US HealthLink can now be utilized to access a wide variety of medical information sources inexpensively.

Health Personnel↗

PubMed1979

Automated scene analysis of CT scans.

Since the advent of computed tomography, there has been an increasing realization that CT scans contain quantitative as well as qualitative information useful in the diagnostic process. Often however, the use of this information is impeded by the tedious manual outlining of the areas of interest in the scan. To alleviate this problem, we have developed a scene segmentation algorithm which will automatically delineate areas of interest in a CT scan. This procedure uses known information about the expected objects in the scan in conjuction with an algorithm to label those objects. The resultant scene segmentation divides the scan into four anatomical areas: skull, normal brain, high density lesions and CSF. After an area of interest is interactively selected by the clinician, volume, density or other quantitative measures may be computed. Limitations of the algorithm and its clinical applications are discussed.

Absorptiometry, Photon↗

PubMed1978

Ventricular volume in man computed from CAT scans.

A new interactive computers has been developed to measure ventricular volume from CAT scans. Testing this system on phantoms demonstrated an accuracy to within 16%. Then a series of scans of patients with obstructive hydrocephalus was analyzed using both tradional linear measures and the computer method. None of the traditional measures were directly proportional to the calculated volume. The area measure of ventricular volume correlated well with the computer-generated values. Clinical studies which attempt to quantitate ventricular volume should use a computerized or planigraphic measure.

Cerebral Ventricles↗

PubMedDate not supplied

Stereotactic surgery with image processing of computerized tomographic scans.

The three-dimensional information from computerized tomographic scans has been transformed by image processing for use in stereotactic surgery. A lateral image of the target and calvarium can be superimposed on the lateral x-ray film taken at operation. Computer processing also eliminates extraneous data on the scan and gives the precise distance of the target from the midline. Testing the technique on a phantom shows it to be accurate to better than 0.5 cm. Application of the method for the stereotactic biopsy of a deep tumor is illustrated.

Adenocarcinoma↗

PubMedDate not supplied

Error measures for objective assessment of scene segmentation algorithms.

Scene segmentation is an important element in pattern recognition problems. Previous efforts to evaluate and compare scene segmentation procedures have been largely subjective. Quantitative error measures would facilitate objective comparison of scene segmentation algorithms. A theoretical discussion leading to a new generalized quantitative error measure, G2, based on comparison of both pixel class proportions and spatial distributions of "true" and test segmentations, is presented. This error measure was tested on 14 manual segmentations and 40 gynecologic cytology specimens segmented with five different scene segmentation techniques. Results indicate that G2 seems to have the desirable properties of correlation with human observation, categorization of error allowing for weighting, invariance with picture size and ease of computation necessary for a useful scene segmentation error measure.

Autoanalysis↗