KEYWORDS: Image segmentation, Liver, 3D modeling, Current controlled voltage source, Computed tomography, 3D image processing, Image processing algorithms and systems, Associative arrays, Solid modeling, Medical imaging
In this paper, we propose a novel iterative active contour algorithm, i.e. Iterative Contextual CV Model (ICCV), and
apply it to automatic liver segmentation from 3D CT images. ICCV is a learning-based method and can be divided into
two stages. At the first stage, i.e. the training stage, given a set of abdominal CT training images and the corresponding
manual liver labels, our task is to construct a series of self-correcting classifiers by learning a mapping between
automatic segmentations (in each round) and manual reference segmentations via context features. At the second stage,
i.e. the segmentation stage, first the basic CV model is used to segment the image and subsequently Contextual CV
Model (CCV), which combines the image information and the current shape model, is iteratively performed to improve
the segmentation result. The current shape model is obtained by inputting the previous automatic segmentation result
into the corresponding self-correcting classifier. The proposed method is evaluated on the datasets of MICCAI 2007 liver
segmentation challenge. The experimental results show that we obtain more and more accurate segmentation results by
the iterative steps and satisfying results are obtained after about six iterations. Also, our method is comparable to the
state-of-the-art work on liver segmentation.
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