Library

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Electronic Resource
    Electronic Resource
    Classification of brain compartments and head injury lesions by neural networks applied to MRI (1995)
    Springer
    Neuroradiology 37 (1995), S. 535-541 
    Details
    ISSN: 1432-1920
    Keywords: Head injury ; Magnetic resonance imaging ; Neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract 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. Classifications 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00593713
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Classification of brain compartments and head injury lesions by neural networks applied to MRI (1995)
    Springer
    Neuroradiology 37 (1995), S. 535-541 
    Details
    ISSN: 1432-1920
    Keywords: Key words Head injury ; Magnetic resonance imaging ; Neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract 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. Classifications 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00593713
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    A String Matching Computer-Assisted System for Dolphin Photoidentification (2000)
    Springer
    Annals of biomedical engineering 28 (2000), S. 1269-1279 
    Details
    ISSN: 1573-9686
    Keywords: Dolphin photographic identification ; Computer-assisted system ; Dorsal fin curvature representation ; Syntactic/semantic curve modeling ; String matching ; Dolphin image database
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine , Technology
    Notes: Abstract 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. © 2000 Biomedical Engineering Society. PAC00: 8780Tq, 4230Sy, 0705Pj
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1114/1.1317532
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...