Automated Left Ventricle Posterior Wall Segmentation Using Kohonen Self-Organizing Map

Autor(es)

Baracho, Salety Ferreira ; de Melo, Vinicius Veloso ; Coelho, Regina Celia ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: PROCEEDINGS OF 2016 5TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS 2016); v. N/A, p. 6-pg., 2016-01-01.

Resumo

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. (AU)

• Processo FAPESP: 12/01505-6 - Simulador computacional para análise e interpretação de sinais do eletrocardiograma e animação tridimensional do coração
• Beneficiário: Regina Célia Coelho
• Modalidade de apoio: Auxílio à Pesquisa - Regular