Bone cortex segmentation of CT images based on BP neural network

Objective: To measure the bone mass, the shape of bones and the bone strength through segmentation of the bone cortex in CT images, and to calculate the corresponding parameters in histomorphometry. Methods: CT images were first interpreted through the DCMTK to draw information of the corresponding images, then the OpenCV are used for preprocessing on the basis of ROI (range of interest), and the texture features of the image were extracted as the input vector. Results of the manual segmentation were used as the mentor signal to train BP neural network, which were then used for segmenting the bone cortex in a sequence of CT images. Results of the segmentation were further processed and displayed. Results: The segmentation efficiency of the bone cortex in CT images through neural network met the needs of the practical application. The separation results showed an obvious shape of the bone cortex with easy distinguishing from the surrounding tissues, which could satisfy the demand of the clinical diagnosis. Conclusions: When the texture features of the bone cortex are evident, this method can achieve a more satisfying segmentation effect with smooth contours, high segmentation accuracy and strong adaptability. With less artificial intervention in the process of the image segmentation, this method can be used for batch CT image segmentation of a complete set of the bone cortex. The inadequacy of the method lies in relatively longer training time demanded for the neural network training.