Simple and Robust Ideal Mid-Sagittal Line (iML) Extraction Method for Brain CT Images

Abstract

Identification of ideal mid-sagittal line (iML) is important for image registration, brain segmentation, pathology detection and particularly for medical image classification. In this paper, iML extraction method based on scale invariant feature transform (SIFT) features is proposed for brain CT images. The method consists of an offline part and an online part. In the offline part, the iML feature points of training set is extracted by an auxiliary tool and an optimized matching template set is obtained by our feature fusion and filtering algorithms. In the online part, a matching point set is generated by matching SIFT features of test images to the offline template. Then the point set is refined by our pruning algorithm and iMLs of test images are fitted by the refined point set. Both real and simulated image data sets are used to verify the accuracy, robustness and execution efficiency of the algorithm. Experimental results show that, our method achieves good accuracy and efficiency in both real and simulation image sets, and performs better tolerance to rotation, noise, fuzzy and asymmetry in comparison with other existing algorithms.