序列颅脑CT图像颅腔内结构自动化提取及分割

Automatic extraction and segmentation of the intracranial structure from cerebral computed tomography

目的自动化提取和分割序列颅脑CT图像颅腔内结构。方法本研究首先利用颅脑CT的解剖学结构,基于区域生长法和形态学方法提取出序列颅脑CT颅腔内结构。然后针对应用EM(期望最大化)算法分割图像时,初始值选取难点,提出了一种改进的基于参数受限高斯混合模型的EM分割算法,实现了对颅内结构的有效分割。结果实验结果表明,该算法能够实现从颅底到颅顶的所有CT图像颅腔内结构的计算机自动化提取和分割,结果准确。结论本文算法在绝大多数情况下是有效的。

This paper＇describes a new method for extracting and segmenting intracranial structure from serial images of cerebral computerized tomography automatically. A region growing- and morphology-based approach was first developed to extract intracranial structures from the serial images of cerebral computerized tomography, and focusing on the problems of parameter initialization of the expectation maximization （EM） algorithm, an improved EM algorithm based on parameterlimited GMM was presented to segment the intracranial structures successfully. Experimental results of the algorithm showed that this method was effective for all cerebral computerized tomography images from bottom to top of the cerebrum.