Abstract
With the increasing use of surgical robots, robust and accurate segmentation techniques for brain tissue in the brain magnetic resonance image are needed to be embedded in the robot vision module. However, the brain magnetic resonance image segmentation results are often unsatisfactory because of noise and intensity inhomogeneity. To obtain accurate segmentation of brain tissue, one new multiphase active contour model, which is based on multiple descriptors mean, variance, and the local entropy, is proposed in this study. The model can bring about a more full description of local intensity distribution. Also, the entropy is introduced to improve the performance of robustness to noise of the algorithm. The segmentation and bias correction for brain magnetic resonance image can be simultaneously incorporated by introducing the bias factor in the proposed approach. At last, three experiments are carried out to test the performance of the method. The results in the experiments show that method proposed in this study performed better than most current methods in regard to accuracy and robustness. In addition, the bias-corrected images obtained by proposed method have better visual effect.
Highlights
With the development of pattern recognition, artificial intelligence, and image processing technology, various kinds of robotics have been widely used
To evaluate the performance of the MDACM, we apply it to some brain magnetic resonance (MR) image
One multiphase active contour models (ACMs) segmentation method named MDACM for brain MR image with inhomogeneous intensity and serious noise is proposed, which can be embedded into the visual module of brain surgery robots
Summary
With the development of pattern recognition, artificial intelligence, and image processing technology, various kinds of robotics have been widely used. More accurate and robust segmentation algorithms for brain image with intensity inhomogeneity and noise are very important and necessary. Many active contour models (ACMs) that are considered as a well-performed class of segmentation algorithms with promising result have been proposed.[6] For the sake of images with intensity, some ACMs introduce the local grayscale statistics.[7,8,9,10] Vese and Chan[7] provided a piecewise model, which can solve the problem in some case. The methods proposed by Li et al.,[14,15] Gharge and Bhatia,[16] and Konduri and Thirupathaiah[17] cannot accurately describe the local intensity distribution of brain MR image. The segmentation results can provide important information for the diagnosis and treatment of brain diseases These preliminary processing can reduce the burden of doctors and improve the success rate of operation.
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