Objective

2

Medical image segmentation results can help doctors predict

diagnose

and make a treatment plan. Medical images are affected by many factors in the process of collection in that the same tissue has different gray levels and is usually corrupted by strong noise. Existing medical images segmentation methods are undesirable because the description of the intensity distribution of the image is insufficient and the robustness to noise is poor. An active contour model with high accuracy and strong robustness to noise is proposed to obtain precise segmentation of medical images.

Method

2

First

entropy of an image is introduced with the local mean and the variance as the descriptor to represent the intensity distribution of the image. The entropy can reflect the richness of image information and measure the degree of heterogeneity of the image within segmentation regions. Great homogeneity of the partition corresponds to high entropy. The local entropy with mean and variance as the local image descriptors can obtain a high fitting degree for image intensity distribution. The value of the local entropy is not sensitive to a single noise pixel because it is determined by several pixels in a continuous domain. Therefore

local entropy has a certain filtering effect. Moreover

the normalization of intensity probability can smooth the noise

which can guarantee the robustness to noise of the proposed method especially when dealing with brain MR images with serious noises. Second

the bias field factor is introduced in the Bayesian framework to establish the energy function of the active contour model. The proposed method adopts the commonly used model to describe images with intensity inhomogeneity. The introduced bias field factor is spatially variant. The introduction of the bias factor can describe the inhomogeneous intensity well when model energy function is established. In the minimization of energy function

the bias factor is involved in the calculation

and the bias correction process is incorporated into the segmentation process to help improve segmentation accuracy. The evolution equation of the level set is obtained using the gradient descent method. At the end of evolution

the bias field correction is realized at the same time of the segmentation. We obtain the image segmentation result by minimizing this energy. Energy minimization is achieved by an iterative process. We minimize the energy with respect to each of its variables in each iteration given that the other three variables are updated in the previous iteration.

Result

2

Synthetic images and medical images

such as the heart

blood vessel

and brain

are used to perform simulation experiments. Experiment 1 verifies the effectiveness of the proposed algorithm. The MDAC model is used to segment intensity inhomogeneous images with noise

and results show that the bias-corrected image is achieved with accurate segmentation. Moreover

the gray histogram of the images before and after bias correction is calculated. The gray histogram of the image before bias correction has three peaks due to inhomogeneous intensity

and the boundaries are unclear. After bias correction

the gray histogram of the image has only two peaks because of the removal of the intensity inhomogeneity and the boundary is clear. In Experiment 2

the accuracy of the MDAC model and other two classic models

namely

LIC and LGDF models

is compared. The MDAC model has the best visual effects and quantitative analysis.

Conclusion

2

Experimental results show that the algorithm accuracy is guaranteed by mean

variance and local entropy to describe image intensity distribution. The introduction of local entropy ensures the accuracy of the algorithm and improves the robustness to the algorithm noise. In the energy function

the bias factor is embedded

which ensures that the algorithm performs bias correction at the same time of segmentation and further improves the accuracy of the algorithm. This method is extended to multiphase medical image segmentation. We attempt to employ partition entropy as data fitting energy to improve segmentation accuracy.