基于图象内容的自动分析是当今医学影像领域的研究热点，分析颅骨 CT图象是否缺如能为医生的诊断提供帮助.为此，提出了一套新的实用方法，该方法首先采用基于 k-均值聚类动态选取种子像素和生长准则的区域增长法精确地将颅骨从图象中自动分割出来，然后利用边界跟踪法找出分割出的区域边界，分析其形状，以圆形度作为描述参数，最后利用熵函数推导出计算机自动诊断颅骨缺如的规则.实验证明，该方法通过对图象内容的分析，对于未参加训练的 10 0例，从第 3脑室下部层面到大脑皮质上部层面，颅脑图象缺如现象的诊断识别率达到了10 0 %.该研究为颅脑图象的自动诊断奠定了基础

Automatic analysis based on image content is a hotspot with bright future of medical image diagnosis technology research. Analysis of the want of skull can help doctor to diagnose. In this paper, a new method is proposed to automatic detect the want of skull based on CT image content. Region growing method, which seeds and growing rules are chosen by k means clustering dynamically, is applied for image automatic segmentation. The segmented region boundary is found by boundary tracing. The shape of the boundary is analyzed, and the circularity is taken as description parameter. Then, the rules for computer automatic diagnosis of the want of skull are reasoned by entropy function. This method is used to analyze the images from the third ventricles below layer to cerebral cortex top layer. Experimental result shows that the recognition rate is 100% for the 100 images, those are chosen from medical image database randomly and are not included in the training examples. This method integrates gray and shape feature, and isn't affected by image size and position. This research achieves high recognition rate and sets a basis for automatic analysis of brain image.