快速且精確的影像分割一直都是電腦視覺領域中很大的挑戰，原因在於自然和醫學影像普遍受到雜訊以及像素強度不均的干擾，為了克服這些因素，模糊分群法被大量應用在影像分割中，尤其在加入核心概念後，能夠有效地處理影像上的雜訊及異值點，並透過水平集函數的實作，使影像上的曲線能夠收斂到物體的輪廓，但是水平集函數的計算複雜度較高，也需要透過再初始化或能量限制項維持函數的平滑程度及穩定性。
本研究中提出演算法架構是將格子波爾茲曼方法應用在解開核心模糊分群的水平集函數中，格子波爾茲曼方法透過每個像素獨立運算，獲得水平集函數迭代式的解答，並同時維持水平集函數的平滑性質，這也代表著能夠高度地被圖形處理器平行化，因此這個演算法能提供快速、穩定並且不易受到初始輪廓影像的準確結果，我們在實驗中，藉由合成和自然影像的分割結果，證實了演算法的穩定以及準確度，並透過圖形處理器的執行時間，展現演算法的效率。

Fast and accurate image segmentation is still a challenging task in computer vision because real-world images are often distorted by noise and intensity inhomogeneity. In order to overcome these problems, fuzzy clustering is extensively applied to image segmentation because of the strong ability to reject local minimum. It also incorporates with kernel metrics to enhance robustness against noise and outliers and construct a nonlinear energy function based on a variational level set framework. However, level set implementation often costs a lot of CPU times, and needs “re-initialization”or regularizing terms to keep level set function smooth and stable.
Our research provides the algorithm which solve level set equation of kernel fuzzy active contour model by using Lattice Boltzmann Method solver. Lattice Boltzmann Method can recover the level set PDE by computing pixel-by-pixel independently and maintain the stability and smoothness of level set function in the meanwhile. Therefore, this algorithm is fast, stable and independent to the position of the initial curve. Experiments on synthetic and real-world images demonstrate the stability and performance of the proposed method, and also show the efficiency of algorithm by using graphics processing unit.

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