The recognition of substantia nigra of brain stem ultrasound images based on Principal Component Analysis

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The recognition of substantia nigra of brain stem ultrasound images based on Principal Component Analysis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

The recognition of substantia nigra of brain stem ultrasound images based on Principal Component Analysis

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Mathematical Models for Engineering Science

ISBN

978-960-474-252-3

ISSN

1792-6734

e-ISSN

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Počet stran výsledku

9

Strana od-do

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Název nakladatele

Wseas Press

Místo vydání

Španělsko

Místo konání akce

Španělsko

Datum konání akce

1. 1. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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