论文标题
改善乳房超声图像的分割：半自动两指针直方图拆分技术
Improving Segmentation of Breast Ultrasound Images: Semi Automatic Two Pointers Histogram Splitting Technique
论文作者
论文摘要
由于其噪音，斑点和伪影，乳房超声（BUS）图像中自动分割病变区域是一个挑战性的。总线图像的边缘图也无济于事，因为在大多数情况下，边缘图不会提供任何信息。几乎所有分割技术都将图像的边缘图作为其第一步，尽管有一些算法也试图避免边缘映射。从理论上改善乳房超声图像的边缘图可以提高自动分割的机会，从而更加精确。在本文中，我们提出了一种使用两个指针的半自动直方图拆分技术。在这里，用户只需要选择两个最初猜测的点，该点表示感兴趣的区域（ROI）。该方法将自动研究内部直方图，并使用两个指针将其拆分。与使用相同算法和相同初始化的常规分割相比，输出总线图像改进了边缘图，最终将其分割更好。此外，我们进一步处理了边缘图以具有更少的边缘像素与面积比，从而提高了均匀性和将来易于分割的机会。
Automatically segmenting lesion area in breast ultrasound (BUS) images is a challenging one due to its noise, speckle and artifacts. Edge-map of BUS images also does not help because in most cases the edge-map gives no information whatsoever. Almost all segmentation technique takes the edge-map of the image as its first step, though there are a few algorithms that try to avoid edge-maps as well. Improving the edge-map of breast ultrasound images theoretically improves the chances of automatic segmentation to be more precise. In this paper, we propose a semi-automatic technique of histogram splitting using two pointers. Here the user only has to select two initially guessed points denoting a circle on the region of interest (ROI). The method will automatically study the internal histogram and split it using two pointers. The output BUS image has improved edge-map and ultimately the segmentation on it is better compared to regular segmentation using same algorithm and same initialization. Also, we further processed the edge-map to have less edge-pixels to area ratio, improving the homogeneity and the chances of easy segmentation in the future.
