Biomedical subjects

L H Oliver

Publications and source records attributed to L H Oliver.

5 recordsLinked to original sources

PubMed1985

Reproducibility of left ventricular area and volume measurements using a computer endocardial edge-detection algorithm in normal subjects.

The variability of serially recorded 2-dimensional echocardiograms in normal subjects was determined. During a 2-week period, 10 normal subjects underwent echocardiography 5 times, in 2 laboratories, with use of different ultrasonographs. The video recordings were analyzed using a computer image analysis system (Quantic 1200) to provide standardized left ventricular short-axis areas and area ejection fraction (EF). Left ventricular volumes and volume EF were calculated. The 95% confidence limits of the percent difference for end-diastolic area and volume between 2 samples in a given subject were +/- 16.8 and +/- 16.7%, respectively. The limits for end-systolic area and volume were +/- 15.8 and +/- 17.0%, respectively. The 95% confidence limits for differences of area and volume EF between 2 recordings were +/- 12.8 and +/- 9.7%, respectively. No correlation was found between clinical grade (image quality) and the variability of area measurements. A good correlation (r = 0.98) was found between area and volume EF for any given subject over the 5 observations. These confidence limits are narrower than those previously recorded.

Adult↗

PubMed1977

High resolution analysis of cervical cells--a progress report.

This paper presents preliminary results of research toward the development of a high resolution analysis stage for a dual resolution image processing-based prescreening device for cervical cytology. Experiments using both manual and automatic methods for cell segmentation are described. In both cases, 1500 cervical cells were analyzed and classified as normal or abnormal (dysplastic or malignant) using a minimum Mahalanobis distance classifier with eight subclasses of normal cells, and five subclasses of abnormal cells. With manual segmentation, false positive and false negative error rates of 2.98 and 7.73% were obtained. Similar experiments using automatic cell segmentation methods yielded false positive and false negative error rates of 3.90 and 11.56%, respectively. In both cases, independent training and testing data were used.

Autoanalysis↗

PubMed1977

Estimating false positive and false negative error rates in cervical cell classification.

The performance of a cell recognition system on unknown data is often estimated in terms of its error rates on a test set. This paper investigates methods for producing estimates of error rates in cervical cell classification. Classification performance curves calculated using these methods are given for several classification schemes used to classify 1500 cervical cells.

Autoanalysis↗

PubMedDate not supplied

A second-generation computer-based edge detection algorithm for short-axis, two-dimensional echocardiographic images: accuracy and improvement in interobserver variability.

The present study tested the hypothesis that a second-generation endocardial edge detection algorithm that used a priori endocardial and epicardial information would improve accuracy and reduce the variability of border definition. Five nonexpert observers utilized the version 2 algorithm on 20 cycles of two-dimensional short-axis images (five excellent, seven good, and eight poor quality studies stored digitally from a previously reported project). Manually defined areas by five recognized experts on these 20 cardiac cycles were considered to be "true areas." Areas defined by the experts with version 1 of the algorithm were also used for comparison. Regression of the version 2 areas with mean, manually defined excellent quality areas yielded a similar correlation (r = 0.985) to that reported between the manual and the version 1 areas (r = 0.986). For all 20 cycles in the series, however, the correlation between version 2 and the manually defined areas was lower (r = 0.952) than that of the same correlation with version 1 areas (r = 0.980). For all studies the interobserver variability (percent area difference) was +/- 14.4% for manually defined borders, +/- 11.1% for version 1-defined borders, and +/- 7.7% for version 2-defined borders. No difference in variability was observed for excellent quality studies (+/- 5.3% versus 5.2%) between version 1 and version 2 areas. However, the version 2 algorithm significantly reduced interobserver variability for good and poor quality studies (+/- 8.4% to 7.6%, p less than 0.025, and 16.3% to 9.1%, p less than 0.05, respectively). We concluded that: the version 2 algorithm provided accuracy and significantly reduced the variability of area measurement in good and poor quality studies and that epicardial information was important to the improvement by providing wall thickness information to assist in filling areas of dropout and avoidance of intracavitary structures.

Algorithms↗

PubMedDate not supplied

Clinical validation of an edge detection algorithm for two-dimensional echocardiographic short-axis images.

The purpose of this study was to validate an edge detection algorithm for short-axis two-dimensional echocardiographic studies in a protocol that stimulated its implementation at multiple clinical laboratories. Six short-axis two-dimensional echocardiographic studies were solicited from each of five clinical laboratories. A single cardiac cycle from each of the resulting 30 studies was entered into the computer system. Five expert observers came to the laboratory on separate occasions and traced endocardial borders from the short-axis studies on 2 separate days. The computer algorithm generated borders on each frame of the cardiac cycles on the basis of regions of search defined by the observers. Of the 30 original studies, five were considered excellent, seven were good, nine were poor, and nine were technically inadequate by consensus of the five observers. The correlation coefficient for computer-defined borders with manually defined borders in the excellent quality studies was 0.985. Interobserver variability was expressed as the mean percent area difference for all possible pairings of observers. The mean percent area differences were decreased from +/- 9.8% to +/- 5.3%, +/- 12.5% to +/- 8.4%, and +/- 17.4% to +/- 15.6% when comparing observer with computer-generated borders in the excellent, good, and poor quality studies, respectively. Intraobserver variability was expressed as decrease in mean percent area difference on corresponding frames between days 1 and 2. Intraobserver variability was decreased from +/- 6.5% to +/- 4.5%, +/- 10.8% to +/- 7.0%, and +/- 14.0% to +/- 11.9%, respectively. All reductions in variability were statistically significant at p less than 0.01. Observer acceptance of computer-defined borders was estimated at 94%, 93%, and 97% for excellent, good, and poor quality studies, respectively. Once the observer defined a region of search, computer process time to generate all borders in the cardiac cycle was approximately 4 minutes. The conclusion is that the algorithm produces accurate, reliable, and acceptable borders.

Algorithms↗