Automatic detection and segmentation of renal lesions in 3D contrast-enhanced ultrasound images
Prevost, Raphaël; Cohen, Laurent D.; Corréas, Jean-Michel; Ardon, Roberto (2012), Automatic detection and segmentation of renal lesions in 3D contrast-enhanced ultrasound images, in David R. Haynor; Sébastien Ourselin, SPIE Proceedings Vol. 8314, Medical Imaging 2012: Image Processing, SPIE, p. 83141D. 10.1117/12.911103
Type
Communication / ConférenceExternal document link
https://hal.archives-ouvertes.fr/hal-00703131Date
2012Conference country
UNITED STATESBook title
SPIE Proceedings Vol. 8314, Medical Imaging 2012: Image ProcessingBook author
David R. Haynor; Sébastien OurselinPublisher
SPIE
ISBN
9780819489630
Pages
83141D
Publication identifier
Metadata
Show full item recordAbstract (EN)
Contrast-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:10.1117/12.911103Subjects / Keywords
fast marching; front propagation; segmentation; contrast-enhanced; 3D; kidney; lesions; fi ltering; cysts; ultrasound – detectionRelated items
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