Automated left ventricle posterior wall segmentation using kohonen self-organizing map

Automated left ventricle posterior wall segmentation using kohonen self-organizing map

Author Baracho, Salety Ferreira Autor UNIFESP Google Scholar
Melo, Vinicius Veloso de Autor UNIFESP Google Scholar
Coelho, Regina Celia Autor UNIFESP Google Scholar
Abstract Image segmentation of left ventricle using long-axis view of the echocardiogram is important to assist the operator in the extraction of functional parameters. The correct obtaining of this parameter can help an early diagnosis of such disease and is welcome to the medical community. However, it is not such an easy task due to the inherent equipment operator bias and the inter- and intra-observer variability. To aid in such issue, in this paper we present an automatic segmentation of the left ventricle posterior wall in echocardiographic images. Our approach employs the Self-Organizing Map to cluster the image's pixels and some image processing methods to perform the final segmentation and calculation of the left ventricle thickness. Results show that our approach, besides fully automatic, is more accurate than similar result from the literature obtained with semi-automatic methods.
Keywords Image Segmentation
Left Ventricle Posterior Wall
Kohonen's Self-Organizing Map
Border ExtractionEchocardiography
Language English
Sponsor Conselho Nacional de Desenvolvimento Cientifico e Tecnologico - Brasil (CNPq) [486950/2013-1]
"Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil" (CAPES)
Sao Paulo Research Foundation (FAPESP) [2012/01505-6]
Grant number CNPq: 486950/2013-1
FAPESP: 2012/01505-6
Date 2016
Published in Proceedings Of 2016 5th Brazilian Conference On Intelligent Systems (BRACIS 2016). New york, p. 456-461, 2016.
Publisher Wiley-Blackwell
Extent 456-461
Access rights Closed access
Type Conference paper
Web of Science ID WOS:000401813700077

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