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Biomedical subjects

M E Jernigan

Publications and source records attributed to M E Jernigan.

3 recordsLinked to original sources

Estimation of bladder wall location in ultrasound images.

A method of automatically estimating the location of the bladder wall in ultrasound images is proposed. Obtaining this estimate is intended to be the first stage in the development of an automatic bladder volume calculation system. The first step in the bladder wall estimation scheme involves globally processing the images using standard image processing techniques to highlight the bladder wall. Separate processing sequences are required to highlight the anterior bladder wall and the posterior bladder wall. The sequence to highlight the anterior bladder wall involves Gaussian smoothing and second differencing followed by zero-crossing detection. Median filtering followed by thresholding and gradient detection is used to highlight as much of the rest of the bladder wall as was visible in the original images. Then a 'bladder wall follower'--a line follower with rules based on the characteristics of ultrasound imaging and the anatomy involved--is applied to the processed images to estimate the bladder wall location by following the portions of the bladder wall which are highlighted and filling in the missing segments. The results achieved using this scheme are presented.

Female

Classification of brain-stem auditory evoked potentials by syntactic methods.

A syntactic pattern recognition procedure for classification of brain-stem auditory evoked potential (BSAEP) is presented. A pre-processing stage of zero-phase bandpass filtering enhances the peaks and suppresses the noise. A finite-state grammar was designed to identify the peaks. Attributes of the peaks (latencies and amplitudes) that are identified are checked for their acceptability. A training run on 70 subjects of known diagnosis was performed to fine-tune the system and build up necessary acceptance criteria. Peak latency differences are used for the classification rather than absolute peak latencies. Acceptance criteria for peak latency differences were empirically optimized. A data base of normal BSAEPs, created during the training run, was updated and used during the test run. Test of the classifier using 60 subjects yielded a classification accuracy of 83%. The classifier has acceptable accuracy and can be modified for other evoked potentials such as visual and somatosensory by establishing relevant attribute tables.

Brain Stem