Magnetic resonance imaging is a powerful diagnostic technique which can provide high spatial resolution, slice selection at any orientation and excellent soft tissue contrast. However, automated quantitative analysis of MR images remains a difficult problem. One impediment to automated, quantitative MR Generally speaking, bias correction methods can be broadly categorized into two classes: prospective methods (Li
The PCNN was originally presented by Eckhorn in order to explain the synchronous neuronal burst phenomena in the cat and other little mammals’ visual cortex (Eckhorn
In every computational iteration, this model parameters adjustment process is very inconvenient. So, in this study, we employ a simplified PCNN model, the single neuron model of simplified PCNN is shown as Fig. 1. The mathematic expressions of this simplified PCNN system can be described as follows:
where, F
The PCNN model has a strong biological background; it has many advantageous characteristics, which are similar to human vision. In the practical
This is the reason that we select this model to estimate MR image bias field. Our main method is to use Pulse synchronization theory to realize clustering of Gray Matter (GM) and White Matter (WM) and adjust parameters of PCNN model to yield a satisfied result as bias field of tested image. Here, using time signature G (n) to determine whether end PCNN’s iteration, we can obtain firing map corresponding to G (n) maximum as tested image’s bias field. G (n) is computed as:
The method main process is shown in Fig. 2.
where, σ (C) and μ (C) are the standard deviation and mean intensity of class C, respectively. To reflect more overlap information between the intensity distributions of distinct tissue classes. We adopt the coefficient of joint variations (CJV) to estimate correction results of the proposed method in this study:
Which is the sum of the standard deviations of two distinct classes; C
Comparing with classical fuzzy C-means clustering correction method by Pham and Prince (1999b), so-called FCM method. Quantitative evaluation of the proposed method, FCM method was performed by computing the CJV (GM,WM) of the gray and white matters for all images from the two experimental images. Table 1 includes the results of non-uniformity correction of the images from the above two images. In general, the clustering result of FCM method is better than other clustering method. But, from the above data, the proposed method outperformed the FCM method in this study and was also faster than the FCM and other methods.
To a simulated MR T1-weighted image, the average processing time of the proposed method is about 0.6 sec and usual clustering method is about 1.5 sec on the same processing platform.
A novel simplified PCNN model based is proposed which is used to correct intensity inhomogeneities in MRI and the iterative firing map of PCNN model as bias field which is corresponding to the time signature G (n) maximum. This method is simply and also very effective, it needn’t acquire transcendental information of intensity non-uniform MR images and require no any assumptions and user interaction. The corrected results are very satisfied to simulated and real MR images at a fast rate, and I think this method is valuable tool in MR image analysis.
This study has been supported by the Postdoctoral Foundation of China (Grant No. 20100471665). |
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作者：**Changtao He, Fangnian Lang, Hongliang Li and Haixu Wang**

来源：http://scialert.net/fulltext/?doi=itj.2011.1437.1441#70444_b