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5 May 2010 Lesion segmentation algorithm for contrast enhanced CT images
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Lesion segmentation is crucial in liver pathology diagnosis and surgery planning. Its goal is to identify lesion's shape, location and connectivity to the vessel system. Nowadays, the clinical practice is to use volumetric contrast enhanced CT images, acquired before and after contrast agent injection. However, currently, liver CAD systems work on a single volumetric image, i.e. the volume exhibiting the best contrast enhancement. Therefore, the motivation of our work is to explore the gray-level enhancement in the different abdominal tissues and organs present in all acquired images. The described method at first aligns all images and progresses with the segmentation - a combination of an initial clustering approach and the Expectation-Maximization algorithm to optimally model the joint histogram by a sum of multivariate Gaussian distributions. It is performed hierarchically: firstly, it is invoked on the whole abdominal volume, secondly on the detected liver region, and finally over the lesions. Experiments show that if the contrast information is sufficient, the results are accurate in comparison to the ground truth: reference segmentation performed by a radiologist using a commercial tool. Moreover, we show that our method provides beneficial information to radiologists about the lesion nature and its behavior throughout the different phases.
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A. Markova, F. Temmermans, R. Deklerck, E. Nyssen, and J. de Mey "Lesion segmentation algorithm for contrast enhanced CT images", Proc. SPIE 7723, Optics, Photonics, and Digital Technologies for Multimedia Applications, 77231T (5 May 2010);

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