Improving an SVM-based Liver Segmentation Strategy by the F-score Feature Selection Method

摘要

A fast and accurate computer-aided liver segmentation plays a vital role in the virtual hepatic surgery. Large amount of features yielded in supervised segmentation methods may lead to slow training and classifying processes. Therefore, feature selection is of importance in order to speed up the liver segmentation. Recently, a hybrid method was proposed by Liu et al. combining thresholding, classifier and region growing. However, this method suffers from long process time caused by the large amount of features. F-score is a simple technique to measure the discrimination of different features. We therefore combine F-score to the hybrid method to reduce the time required in the training and testing stage.Four sets of abdominal CT images were obtained from Shan Dong University. The data consists of multiple, serial, axial computed tomography images derived from helical, 64 multi-slice CT and was stored in DICOM format of size 512 by 512 with 12-bit gray level resolution. The hybrid method which we proposed is to segment CT images by support vector machines after supervised thresholding, K means clustering, and texture feature extraction (Gray level co-occurrence Matrix-GLCM). We applied principle component analysis (PCA), forward orthogonal search algorithm by maximizing the overall dependency (FOS-MOD) and F-score to select the features from the GLCM.The experiment showed that F-score helps in accelerating training and classifying stage by 50% whilst the PCA-based feature selection method failed to extract the liver contour correctly. This may be explained by the fact that useful information for classifying may be lost when using PCA. FOS-MOD algorithm is time consuming mainly because its orthogonaliza-tion procedure and the calculation of the correlation matrix are very complex. In conclusion, F-score is a promising feature selection method for the svm-based classification. Our hybrid method with F-score can speed up the segmentation with accurate results ensured.