Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach

Kristians Diaz, Benjamin Castaneda

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

16 Citas (Scopus)


This paper presents a semi-automated algorithm for prostate boundary segmentation from three-dimensional (3D) ultrasound (US) images. The US volume is sampled into 72 slices which go through the center of the prostate gland and are separated at a uniform angular spacing of 2.5 degrees. The approach requires the user to select four points from slices (at 0, 45, 90 and 135 degrees) which are used to initialize a discrete dynamic contour (DDC) algorithm. 4 Support Vector Machines (SVMs) are trained over the output of the DDC and classify the rest of the slices. The output of the SVMs is refined using binary morphological operations and DDC to produce the final result. The algorithm was tested on seven ex vivo 3D US images of prostate glands embedded in an agar mold. Results show good agreement with manual segmentation.

Idioma originalInglés
Título de la publicación alojadaMedical Imaging 2008
Subtítulo de la publicación alojadaImage Processing
EstadoPublicada - 2008
EventoMedical Imaging 2008: Image Processing - San Diego, CA, Estados Unidos
Duración: 17 feb. 200819 feb. 2008

Serie de la publicación

NombreProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (versión impresa)1605-7422


ConferenciaMedical Imaging 2008: Image Processing
País/TerritorioEstados Unidos
CiudadSan Diego, CA


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