Koss J E, Newman F D, Johnson T K, Kirch D L
IEEE Trans Med Imaging. 1999 Jul;18(7):640-8. doi: 10.1109/42.790463.
Abdominal organ segmentation is highly desirable but difficult, due to large differences between patients and to overlapping grey-scale values of the various tissue types. The first step in automating this process is to cluster together the pixels within each organ or tissue type. We propose to form images based on second-order statistical texture transforms (Haralick transforms) of a CT or MRI scan. The original scan plus the suite of texture transforms are then input into a Hopfield neural network (HNN). The network is constructed to solve an optimization problem, where the best solution is the minima of a Lyapunov energy function. On a sample abdominal CT scan, this process successfully clustered 79-100% of the pixels of seven abdominal organs. It is envisioned that this is the first step to automate segmentation. Active contouring (e.g., SNAKE's) or a back-propagation neural network can then be used to assign names to the clusters and fill in the incorrectly clustered pixels.
腹部器官分割非常必要但难度很大，这是因为患者之间存在很大差异，而且各种组织类型的灰度值相互重叠。实现这一过程自动化的第一步是将每个器官或组织类型内的像素聚类在一起。我们建议基于CT或MRI扫描的二阶统计纹理变换（哈勒克变换）来生成图像。然后将原始扫描图像加上纹理变换集输入到霍普菲尔德神经网络（HNN）中。构建该网络是为了解决一个优化问题，其中最佳解决方案是李雅普诺夫能量函数的最小值。在一个腹部CT扫描样本上，这个过程成功地将七个腹部器官79%至100%的像素聚类。可以预想，这是实现分割自动化的第一步。然后可以使用主动轮廓法（例如蛇形模型）或反向传播神经网络为聚类指定名称，并填充聚类错误的像素。
