The segmentation of lung tumor serial images is one of the key techniques of Computer Lung Tumor Three-dimensional Aided Diagnosis System. The complex relation between tumor and its adjacent tissue makes it difficult to get good result. For providing doctors more accuate lung image

an automatic segmentation of lung tumor in CT serial images is presented based on texture analysis and radial basis function(RBF) neural network. With the correlation of tumor's gray level and position in sequential slices

we got the training swatch of tumor region automatically. Some second-order statistical texture parameters were computed for composing feature space; a classification procedure based on RBF neural network was applied to this space to segment the tumor. Compared with region growth algorithm and the multi-criterion segmentation algorithm

the experiment demonstrates that the proposed method can make full use of three-dimensional information of tumor serial images

and reduce manual intervention as possible. The segment results also confirm the validity and the clinical value of the proposed method.