A Prostate MRI Segmentation Tool Based on Active Contour Models Using a Gradient Vector Flow

Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) con...

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Detalles Bibliográficos
Autores: Rodríguez, J., Ochoa-Ruíz, G., Mata C
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p20991
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20991
Access Level:acceso abierto
Palabra clave:Active Contour Models
snake segmentation
GVF
prostate imaging
Descripción
Sumario:Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling the radiologists to use novel and easy to use semi-automatic segmentation techniques instead of manual segmentation. In this work, a detailed specification of the proposed segmentation algorithm using an Active Contour Models (ACM) aided with a Gradient Vector Flow (GVF) component is defined. The purpose is to help the manual segmentation process of the main ROIs of prostate gland zones. Finally, an experimental case of use and a discussion part of the results are presented.