Zimmer Y, Tepper R, Akselrod S
Medical Physics Department, Tel Aviv University, Israel.
Ultrasound Med Biol. 1996;22(9):1183-90. doi: 10.1016/s0301-5629(96)00167-6.
Segmentation is often an important step in medical image analysis. The local entropy is a possible variable for segmenting ultrasound images containing fluid surrounded by a soft tissue. A commonly used tool for image segmentation is thresholding. Recently, a new thresholding technique, known as "minimum cross entropy thresholding" (MCE), has been proposed. We present a multivariate extension of MCE in which the segmented variable (gray level) is replaced by a weighted combination of several image parameters. We propose to use a bivariate extension of MCE, which uses a linear combination of the gray level and the local entropy. The results obtained are demonstrated for ultrasound images of ovarian cysts.
分割通常是医学图像分析中的一个重要步骤。局部熵是用于分割包含被软组织包围的液体的超声图像的一个可能变量。图像分割的一种常用工具是阈值处理。最近，一种新的阈值处理技术，即“最小交叉熵阈值处理”（MCE）被提出。我们提出了MCE的多变量扩展，其中分割变量（灰度级）被几个图像参数的加权组合所取代。我们建议使用MCE的双变量扩展，它使用灰度级和局部熵的线性组合。所获得的结果在卵巢囊肿的超声图像上得到了验证。
