Biomedical subjects

C E Priebe

Publications and source records attributed to C E Priebe.

4 recordsLinked to original sources

PubMed1999

Smoothing bandwidth selection for response latency estimation.

Stimulus response latency is the delay between stimulus onset and the evoked modulation in neural activity. A common technique to estimate latencies involves binning the spike arrival times to form a peri-stimulus histogram. This histogram is smoothed using a fixed bandwidth. The estimated latency is the first time following stimulus onset in which the smoothed histogram exceeds the midpoint between the minimum and maximum of the smoothed histogram. We demonstrate that the choice of smoothing bandwidth is critical to the accuracy of this latency estimation technique. We suggest a bootstrap resampling technique for bandwidth selection which results in a robust latency estimate.

Animals↗

PubMed1998

Estimating stimulus response latency.

Stimulus response latency is the delay in the onset of stimulus-evoked neuronal activity. We develop maximum likelihood and least squares estimators of stimulus response latency and present a comparison of the performance of these methods with estimators commonly used in the neuroscience literature. The formal statistical change-point estimation problem is nontrivial due to the inclusion of a 'nuisance parameter', the end of stationarity in the stimulus-evoked activity. Our results suggest that the automation of the estimation of stimulus response latency will benefit from the use of the maximum likelihood estimator.

Action Potentials↗

PubMed1997

A method for detecting microcalcifications in digital mammograms.

Microcalcification clusters are often an important indicator for the detection of malignancy in mammograms. In many cases, microcalcifications are the only indication of a malignancy. However, the detection of microcalcifications can be a difficult process. They are small and can be embedded in dense tissue. This paper presents a method for automatically detecting microcalcifications. We utilize a high-boost filter to suppress background clutter enabling segmentation even in very dense breast tissue. We then use a threshholding and region growing technique to extract candidate microcalcifications. Likely microcalcifications are then identified by a linear classifier. We apply this method to images selected from the LLNL/UCSF Digital Mammogram Library, and produce a receiver operating characteristic (ROC) curves to detail the trade-off between probability of detection and false alarms. Finally, we exam the ability to properly select a threshold to achieve a desired probability of detection based upon a training set. This is a US government work. There are no restrictions on its use.

Breast Neoplasms↗

PubMed1994

The application of fractal analysis to mammographic tissue classification.

As a first step in determining the efficacy of using computers to assist in diagnosis of medical images, an investigation has been conducted which utilizes the patterns, or textures, in the images. To be of value, any computer scheme must be able to recognize and differentiate the various patterns. An obvious example of this in mammography is the recognition of tumorous tissue and non-malignant abnormal tissue from normal parenchymal tissue. We have developed a pattern recognition technique which uses features derived from the fractal nature of the image. Further, we are able to develop mathematical models which can be used to differentiate and classify the many tissue types. Based on a limited number of cases of digitized mammograms, our computer algorithms have been able to distinguish tumorous from healthy tissue and to distinguish among various parenchymal tissue patterns. These preliminary results indicate that discrimination based on the fractal nature of images may well represent a viable approach to utilizing computers to assist in diagnosis.

Breast Neoplasms↗