The recognition of substantia nigra of brain stem ultrasound images based on Principal Component Analysis
Result description
This paper is about recognition of substantia nigra of brain stem ultrasound images based on Principal Component Analysis. As input we have a collection of sonographical slices which were preprocessed and optimized and we must detect a ROI substantia nigra. Furthermore shows a principle of PCA and practical implementation with results and contains a comparison of results from different software. The main goal is a classification of these images and recognition results. This processing is important for detection of Parkinson´s disease, reflected well recognition of ROI substantia nigra. We got an output as selected principal components and we assessed a threshold for classification. Core implementation were realized in C# optimized application and computed in another existing software. We used cropped images contains ROI and we optimized PCA algorithm to effective computing.
Keywords
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
The recognition of substantia nigra of brain stem ultrasound images based on Principal Component Analysis
Original language description
This paper is about recognition of substantia nigra of brain stem ultrasound images based on Principal Component Analysis. As input we have a collection of sonographical slices which were preprocessed and optimized and we must detect a ROI substantia nigra. Furthermore shows a principle of PCA and practical implementation with results and contains a comparison of results from different software. The main goal is a classification of these images and recognition results. This processing is important for detection of Parkinson´s disease, reflected well recognition of ROI substantia nigra. We got an output as selected principal components and we assessed a threshold for classification. Core implementation were realized in C# optimized application and computed in another existing software. We used cropped images contains ROI and we optimized PCA algorithm to effective computing.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Mathematical Models for Engineering Science
ISBN
978-960-474-252-3
ISSN
1792-6734
e-ISSN
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Number of pages
9
Pages from-to
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Publisher name
Wseas Press
Place of publication
Španělsko
Event location
Španělsko
Event date
Jan 1, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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Basic information
Result type
D - Article in proceedings
CEP
IN - Informatics
Year of implementation
2010