NDLI: Efficient and robust segmentation and correction model for medical images

Content Provider	IET Digital Library
Author	Yang, Yunyun Jia, Wenjing
Abstract	Accurate segmentation of medical images plays a very important role in clinical diagnosis so that the segmentation technology for medical images attracts more and more attention. However, most medical images usually suffer from severe intensity inhomogeneity and make accurate segmentation difficult. In this study, the authors propose an efficient and robust active contour model for simultaneous image segmentation and correction. The proposed model not only can accurately segment images with severe intensity inhomogeneity and serious noise but also can eliminate the intensity varying information to get the homogeneous correction images. They first present the level set formulation of the two-phase model, which is then extended to the multi-phase formulation. The split Bregman method is applied to efficiently minimise the proposed energy functionals. The proposed model is tested with lots of synthetic images and medical images with promising results. Experimental results demonstrate that the proposed model can accurately segment and correct the inhomogeneous images with serious noise. Quantitative comparison results of the proposed model and other models illustrate the proposed model is more accurate and more efficient. What's more, the proposed model not only is insensitive to the initial contour, but also is robust to the noise.
Starting Page	2245
Ending Page	2254
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 12, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0698
Journal	IET Image Processing
Publisher Date	2019-08-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accurate Segmentation Difficult Biology And Medical Computing Computer Vision And Image Processing Technique Correction Model Efficient Contour Model Homogeneous Correction Image Image Segmentation Inhomogeneous Image Medical Image Medical Image Processing Minimisation Optical, Image And Video Signal Processing Optimisation Technique Patient Diagnostic Method And Instrumentation Robust Active Contour Model Robust Segmentation Segment Image Segmentation Technology Severe Intensity Inhomogeneity Simultaneous Image Segmentation Synthetic Image
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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