Khadidos Alaa, Sanchez Victor, Li Chang-Tsun
IEEE Trans Image Process. 2017 Apr;26(4):1979-1991. doi: 10.1109/TIP.2017.2666042. Epub 2017 Feb 8.
Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow field and the evolving contour's normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than state-of-the-art edge-based level set segmentation methods, particularly around weak edges.
由于水平集方法具有良好的边界检测精度，因此已被广泛用于实现用于图像分割应用的活动轮廓。在医学图像分割的背景下，弱边缘和不均匀性仍然是重要问题，可能会阻碍基于使用水平集方法实现的活动轮廓的任何分割方法的准确性。本文提出了一种基于使用水平集方法实现的活动轮廓的方法，用于此类医学图像的分割。所提出的方法使用基于目标能量泛函最小化的水平集演化，其能量项根据它们在检测边界中的相对重要性进行加权。这种相对重要性是基于从位于演化轮廓内外的相邻区域收集的局部边缘特征来计算的。所采用的局部边缘特征是边缘强度以及图像的梯度向量流场与演化轮廓法线之间的对齐程度。我们评估了所提出的方法在真实MRI和CT切片、X射线图像以及超声图像中对各个区域的分割效果。评估结果证实了使用局部边缘特征加权能量力以减少泄漏的优势。这些结果还表明，所提出的方法比基于边缘的最新水平集分割方法能产生更准确的边界检测结果，特别是在弱边缘周围。
