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14 February 2012 Automatic detection and segmentation of renal lesions in 3D contrast-enhanced ultrasound images
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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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Raphael Prevost, Laurent D. Cohen, Jean-Michel Correas, and Roberto Ardon "Automatic detection and segmentation of renal lesions in 3D contrast-enhanced ultrasound images", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141D (14 February 2012);

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