A novel segmentation method of medical CBCT image in liver organ based on Bayesian network

Abstract

Introduction: This paper proposed a kind of segmentation method for medical CBCT image in liver organ based on Bayesian network. Methods: Taking CBCT medical image of liver organ as a focus of medical image segmentation and to segment the area we are interested in with noise elimination and image enhancement. In order to show multiple image information including the liver organ CBCT image color, texture, shape and other features, image information can be extracted, and the extracted image features can be classified from the perspective of data mining by Bayesian network. Results: The method ensures that the target points of the liver organ CBCT image are clearly visible, which is not only suitable for the normal image registration, but also for the disordered image registration. Bayesian network method has good organization ability to distinguish pixels with similar gray level belonging to different tissues in the image, and map them back to the image space, so that tissue extraction from liver organ CBCT images has a good effect. Conclusion: The CBCT images are analysed based on medical image database to better achieve the required characteristics in data mining, extraction of non-rigid registration and image segmentation technology. And the result shows that this kind of method based on Bayesian network is a good theoretical method that can achieve expected outcome and the accuracy rate is good with high practical application.