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dc.contributor.authorPrevost, Raphaël*
dc.contributor.authorCohen, Laurent D.*
dc.contributor.authorCorréas, Jean-Michel*
dc.contributor.authorArdon, Roberto*
dc.date.accessioned2012-06-20T14:12:24Z
dc.date.available2012-06-20T14:12:24Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/9520
dc.descriptionSPIE Medical Imaging 2012: Image Processing, Feb 2012, San Diego, CA, United States
dc.language.isoenen
dc.subjectfast marching
dc.subjectfront propagation
dc.subjectsegmentation
dc.subjectcontrast-enhanced
dc.subject3D
dc.subjectkidney
dc.subjectlesions
dc.subjectfi ltering
dc.subjectcysts
dc.subjectultrasound – detection
dc.subject.ddc621.3en
dc.titleAutomatic detection and segmentation of renal lesions in 3D contrast-enhanced ultrasound images
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherDepartment of Adult Radiology Hôpital Necker - Enfants Malades;France
dc.contributor.editoruniversityotherMedisysResearch Lab (Medisys) Philips Healthcare;France
dc.description.abstractenContrast-enhanced ultrasound (CEUS) is a valuable imaging modality in the detection and evaluation of different kinds of lesions. Three-dimensional CEUS acquisitions allow quantitative volumetric assessments and better visualization of lesions, but automatic and robust analysis of such images is very challenging because of their poor quality. In this paper, we propose a method to automatically segment lesions such as cysts in 3D CEUS data. First we use a pre-processing step, based on the guided filtering framework, to improve the visibility of the lesions. The lesion detection is then performed through a multi-scale radial symmetry transform. We compute the likelihood of a pixel to be the center of a dark rounded shape. The local maxima of this likelihood are considered as lesions centers. Finally, we recover the whole lesions volume with multiple front propagation based on image intensity, using a fast marching method. For each lesion, the final segmentation is chosen as the one which maximizes the gradient flux through its boundary. Our method has been tested on several clinical 3D CEUS images of the kidney and provides promising results. Copyright 2012 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The original version of this work can be found by using the doi:10.1117/12.911103
dc.identifier.citationpages83141D
dc.relation.ispartoftitleSPIE Proceedings Vol. 8314, Medical Imaging 2012: Image Processing
dc.relation.ispartofeditorDavid R. Haynor; Sébastien Ourselin
dc.relation.ispartofpublnameSPIE
dc.relation.ispartofdate2012
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-00703131
dc.description.sponsorshipprivateouien
dc.subject.ddclabelTraitement du signalen
dc.relation.ispartofisbn9780819489630
dc.relation.confcountryUNITED STATES
dc.identifier.doi10.1117/12.911103
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-03-10T17:03:59Z
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