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Efficient level set formulation for segmentation and correction with application to medical images
Author(s): Yunyun Yang; Wenjing Jia; Xiu Shu
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Paper Abstract

Accurate medical image segmentation can greatly improve doctors’ speed of diagnosis and diagnosis rate. But the medical image is usually accompanied by the intensity inhomogeneity, which seriously interferes with the accuracy of segmentation results. In this paper, we propose a novel active contour model with the level set formulation to deal with this problem. With the bias field added into the energy functional, our model not only can accurately segment inhomogeneous images, but also can effectively eliminate the intensity inhomogeneity to get homogeneous correction images. Since our energy functional has a special form similar to the L1 regularization problem, we prefer to apply the split Bregman method to efficiently minimize the energy functional. Then, we use a variety of medical images to test the performance of our model. Experimental results demonstrate that our model can be applied in medical images with satisfactory results. Besides, qualitative and quantitative comparisons with the LSE model further demonstrate the superiority of our model in segmentation accuracy, correction effect and efficiency. The robustness to initial contour and noises is also verified to be the outstanding advantage of our model.

Paper Details

Date Published: 14 August 2019
PDF: 8 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793G (14 August 2019); doi: 10.1117/12.2540075
Show Author Affiliations
Yunyun Yang, Harbin Institute of Technology (China)
Wenjing Jia, Harbin Institute of Technology (China)
Xiu Shu, Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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