KEYWORDS: Image segmentation, 3D image processing, Kidney, Ultrasonography, Image filtering, 3D acquisition, 3D visualizations, Visualization, Image registration, Digital filtering
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.
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