Biomedical subjects

N Kehtarnavaz

Publications and source records attributed to N Kehtarnavaz.

3 recordsLinked to original sources

PubMed2000

A string matching computer-assisted system for dolphin photoidentification.

This paper presents a syntactic/semantic string representation scheme as well as a string matching method as part of a computer-assisted system to identify dolphins from photographs of their dorsal fins. A low-level string representation is constructed from the curvature function of a dolphin's fin trailing edge, consisting of positive and negative curvature primitives. A high-level string representation is then built over the low-level string via merging appropriate groupings of primitives in order to have a less sensitive representation to curvature fluctuations or noise. A family of syntactic/semantic distance measures between two strings is introduced. A composite distance measure is then defined and used as a dissimilarity measure for database search, highlighting both the syntax (structure or sequence) and semantic (attribute or feature) differences. The syntax consists of an ordered sequence of significant protrusions and intrusions on the edge, while the semantics consist of seven attributes extracted from the edge and its curvature function. The matching results are reported for a database of 624 images corresponding to 164 individual dolphins. The identification results indicate that the developed string matching method performs better than the previous matching methods including dorsal ratio, curvature, and curve matching. The developed computer-assisted system can help marine mammalogists in their identification of dolphins, since it allows them to examine only a handful of candidate images instead of the currently used manual searching of the entire database.

Animal Identification Systems↗

PubMed1995

Classification of brain compartments and head injury lesions by neural networks applied to MRI.

An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and "unknown." A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classification of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network.

Artificial Intelligence↗

PubMedDate not supplied

Assisting manual dolphin identification by computer extraction of dorsal ratio.

Marine biologists use a measurement called the "Dorsal Ratio" in the process of manual identification of bottlenose dolphins. The dorsal ratio denotes the relative distances of the two largest notches from the tip on the dorsal fin. The manual computation of this ratio is time consuming, labor intensive, and user dependent. This paper presents a computer-assisted system to extract the dorsal ratio for use in identification of individual animals. The first component of the system consists of active contour modeling where the trailing edge of the dorsal fin is detected. This is followed by a curvature module to find the characteristic fin points: tip and two most prominent notches. Curvature smoothing is performed at various smoothing scales, and wavelet coefficients are utilized to select an appropriate smoothing scale. The dorsal ratio is then computed from the curvature function at the appropriate smoothing scale. The system was tested using 296 digitized images of dolphins, representing 94 individual dolphins. The results obtained indicate that the computer extracted dorsal ratio can be used in place of the manually extracted dorsal ratio as part of the manual identification process.

Algorithms↗