Iterative contextual CV model for liver segmentation

Hongwei Ji; Jiangping He; Xin Yang

doi:10.1117/12.2050203

10 January 2014 Iterative contextual CV model for liver segmentation

Hongwei Ji, Jiangping He, Xin Yang

Proceedings Volume 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013); 90690N (2014) https://doi.org/10.1117/12.2050203
Event: Fifth International Conference on Graphic and Image Processing, 2013, Hong Kong, China

Abstract

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.

Citation Download Citation

Hongwei Ji, Jiangping He, and Xin Yang "Iterative contextual CV model for liver segmentation", Proc. SPIE 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013), 90690N (10 January 2014); https://doi.org/10.1117/12.2050203

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KEYWORDS

Image segmentation

Liver

3D modeling

Current controlled voltage source

Computed tomography

3D image processing

Associative arrays

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