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An efficient implementation of Fuzzy C-Means and watershed algorithms for MRI segmentation

机译：MRI分割的模糊C均值和分水岭算法的有效实现

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摘要

Image segmentation is one of the most common steps in digital image processing. It classifies a digital image into different segments. There are many algorithms for image segmentation such as thresholding, edge detection, and region growing, which finding a suitable algorithm for medical image segmentation is a challenging task. This is due to noise, low contrast, and steep light variations of medical images. The main goal of this paper is improving the performance of fuzzy c-means clustering. Improving is achieved using parallel implementation of this algorithm. Fuzzy c-means clustering is an important iterative clustering algorithm, but it is computationally intensive and it uses the same data between the iterations. The center of the clusters changes in each iteration, which requires considerable amount of time for large data sets. The parallel fuzzy c-means clustering is implemented by using task pipeline concept in CUDA technology. The experimental results show that the performance is improved up to 23.35×. After that watershed algorithm is applied for the final segmentation. The implementation results show that the accuracy of diagnosis in magnetic resonance imaging 97/33% is improved. This improvement is achieved using enhancing edges and reducing noises in images.
机译：图像分割是数字图像处理中最常见的步骤之一。它将数字图像分类为不同的段。存在许多用于图像分割的算法，例如阈值化，边缘检测和区域增长，因此找到合适的医学图像分割算法是一项艰巨的任务。这是由于噪声，低对比度和医学图像的陡峭光变化引起的。本文的主要目标是提高模糊c均值聚类的性能。使用该算法的并行实现可以实现改进。模糊c均值聚类是一种重要的迭代聚类算法，但是它的计算量很大，并且在两次迭代之间使用相同的数据。群集的中心在每次迭代中都会更改，这对于大型数据集而言需要大量的时间。并行模糊c均值聚类是通过CUDA技术中的任务流水线概念实现的。实验结果表明，该性能提高了23.35倍。之后，将分水岭算法应用于最终分割。实施结果表明，磁共振成像诊断的准确性提高了97/33％。使用增强的边缘并减少图像中的噪点可以实现此改进。

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