Autor(es)

Ramos, Jonathan S. ; Watanabe, Carolina Y. V. ; Nogueira-Barbosa, Marcello H. ; Traina, Agma J. M. ; Assoc Comp Machinery

• Tipo de documento: Artigo Científico
• Fonte: SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING; v. N/A, p. 8-pg., 2019-01-01.

Resumo

Segmentation of medical images is a critical issue: several process of analysis and classification rely on this segmentation. With the growing number of people presenting back pain and problems related to it, the automatic or semi-automatic segmentation of fractured vertebral bodies became a challenging task. In general, those fractures present several regions with non-homogeneous intensities and the dark regions are quite similar to the structures nearby. Aimed at overriding this challenge, in this paper we present a semi-automatic segmentation method, called Balanced Growth (BGrowth). The experimental results on a dataset with 102 crushed and 89 normal vertebrae show that our approach significantly outperforms well-known methods from the literature. We have achieved an accuracy up to 95% while keeping acceptable processing time performance, that is equivalent to the state-of-the-art methods. Moreover, BGrowth presents the best results even with a rough (sloppy) manual annotation (seed points). (AU)

• Processo FAPESP: 17/23780-2 - Recuperação por conteúdo de imagens médicas para apoio a decisão clínica usando a abordagem radiômica
• Beneficiário: Jonathan da Silva Ramos
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
• Beneficiário: Agma Juci Machado Traina
• Modalidade de apoio: Auxílio à Pesquisa - Temático