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Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method
Computer Methods and Programs in Biomedicine ( IF 6.1 ) Pub Date : 2020-10-16 , DOI: 10.1016/j.cmpb.2020.105809
Asieh Khosravanian , Mohammad Rahmanimanesh , Parviz Keshavarzi , Saeed Mozaffari

Background and Objective

Brain tumor segmentation is a challenging issue due to noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual MRI segmentation is a very tedious, time-consuming, and user-dependent task. This paper aims to presents a novel level set method to address aforementioned challenges for reliable and automatic brain tumor segmentation.

Methods

In the proposed method, a new functional, based on level set method, is presented for medical image segmentation. Firstly, we define a superpixel fuzzy clustering objective function. To create superpixel regions, multiscale morphological gradient reconstruction (MMGR) operation is used. Secondly, a novel fuzzy energy functional is defined based on superpixel segmentation and histogram computation. Then, level set equations are obtained by using gradient descent method. Finally, we solve the level set equations by using lattice Boltzmann method (LBM). To evaluate the performance of the proposed method, both synthetic image dataset and real Glioma brain tumor images from BraTS 2017 dataset are used.

Results

Experiments indicate that our proposed method is robust to noise, initialization, and intensity non-uniformity. Moreover, it is faster and more accurate than other state-of-the-art segmentation methods with the averages of running time is 3.25 seconds, Dice and Jaccard coefficients for automatic tumor segmentation against ground truth are 0.93 and 0.87, respectively. The mean value of Hausdorff distance, Mean absolute Distance (MAD), accuracy, sensitivity, and specificity are 2.70, 0.005, 0.9940, 0.9183, and 0.9972, respectively.

Conclusions

Our proposed method shows satisfactory results for Glioma brain tumor segmentation due to superpixel fuzzy clustering accurate segmentation results. Moreover, our method is fast and robust to noise, initialization, and intensity non-uniformity. Since most of the medical images suffer from these problems, the proposed method can more effective for complicated medical image segmentation.



中文翻译:

基于超像素模糊聚类和格子玻尔兹曼法的神经胶质瘤脑肿瘤快速分割集方法

背景与目的

由于噪声,伪影和磁共振图像(MRI)中的强度不均匀,脑肿瘤分割是一个具有挑战性的问题。手动MRI分割是一项非常繁琐,耗时且取决于用户的任务。本文旨在提出一种新颖的水平集方法,以解决可靠和自动脑肿瘤分割的上述挑战。

方法

在提出的方法中,提出了一种基于水平集方法的医学图像分割新功能。首先,我们定义了一个超像素模糊聚类目标函数。为了创建超像素区域,使用了多尺度形态梯度重建(MMGR)操作。其次,基于超像素分割和直方图计算,定义了一种新颖的模糊能量函数。然后,使用梯度下降法获得水平集方程。最后,我们使用格子Boltzmann方法(LBM)求解水平集方程。为了评估所提出方法的性能,使用了来自BraTS 2017数据集的合成图像数据集和真实胶质瘤脑肿瘤图像。

结果

实验表明,我们提出的方法对噪声,初始化和强度不均匀性均具有鲁棒性。此外,与其他最新的分割方法相比,它更快,更准确,平均运行时间为3.25秒,针对地面真实情况自动进行肿瘤分割的Dice和Jaccard系数分别为0.93和0.87。Hausdorff距离,平均绝对距离(MAD),准确性,敏感性和特异性的平均值分别为2.70、0.005、0.9940、0.9183和0.9972。

结论

由于超像素模糊聚类的精确分割结果,我们提出的方法显示出令人满意的脑胶质瘤脑肿瘤分割结果。而且,我们的方法对噪声,初始化和强度不均匀性都快速且可靠。由于大多数医学图像都存在这些问题,因此所提出的方法对于复杂的医学图像分割可以更加有效。

更新日期:2020-10-30
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