NDLI: New method for simultaneous moderate bias correction and image segmentation

Content Provider	IET Digital Library
Author	Yang, Yunyun Wang, Ruofan Feng, Chong
Abstract	This study proposes a new method for simultaneous image segmentation and moderate bias correction. Though many methods are proposed to deal with the image intensity inhomogeneity, some problems still exist and have influenced the segmentation results a lot. In this study, a new model is proposed for image segmentation and correction based on the multiplicative intrinsic component optimization (MICO) model. First, the new model in the level set formulation for gray images has been presented and the split Bregman method for fast minimization has been applied. The proposed model is tested with lots of magnetic resonance images and some medical colour images with promising results. Experimental results show that the proposed model can simultaneously segment images and correct bias field moderately. In the experimental part for gray images, a qualitative comparison between the proposed model and the MICO model in both segmentation and bias-correction results is made. Besides, the proposed model with the Chan-Vese model and the illumination and reflectance estimation model in the experimental part for colour images are compared. Moreover, the proposed model can segment nature colour images successfully. It is clear that the proposed model has a good performance on many characteristics such as accuracy, efficiency, and robustness.
Starting Page	939
Ending Page	945
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 6, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5171
Journal	IET Image Processing
Publisher Date	2019-01-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bias Field Correction Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Chan–Vese Model Computer Vision And Image Processing Technique Grey Magnetic Resonance Image Illumination Estimation Model Image Colour Analysis Image Intensity Inhomogeneity Image Segmentation Medical Colour Image Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Minimisation Multiplicative Intrinsic Component Optimisation Model Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Reflectance Estimation Model Spectroscopy Split Bregman Method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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