Application Of Improved GVF Snake Algorithm In Lung Cancer Detection Tectnology

Automatic segmentation techniques of lung image has an important clinical significance for the reduce misdiagnosis and missed diagnosis. Lung image segmentation extracts the first source image of the lung parenchymal get lung nodule characteristics, the final analysis and screening through the classifier, given the findings and diagnoses to help doctors analyze and judge. Extraction of pulmonary parenchyma in the lung image segmentation system has an important significance.First of all, we aim at through the lungs of the existing automatic detection system for analysis, for active contour model can not solve the problem of deep depression is proposed to improve the energy formula based on GVF Snake algorithm, by introducing the image information such as gradient and curvature, in their common of forces, the improvement of the energy equation to determine the lung border, solve the active contour algorithm is unable to carry out the division of the deep depression.Secondly, we research deeply based on the improvement of the energy equation, the stop condition of the curve will change when GVF sanke algorithm stop, the curve will occur can not stop crossing the border and converge to the true point phenomenon, put forward by gradient information and curvature information to external guidance direction, the introduction of sign function to re-set the curve of the movement to stop the conditions to solve the boundary curve can not converge to the real problems and improve the speed of image segmentation and reduce the algorithm's time complexity.Then,we proposed adaptive border marching algorithm for the pleural adhesion at segmentation of lung nodules in the lungs appeared discontinuous or continuous, boundary is the boundary with the actual lung very far split the leakage issue, it is a new frontier repair method, using geometric means smooth lung boundary, solve the leakage lung cancer division of adhesions.Finally, in this paper we use the original CT image segmentation to experiment. According to a large number of experiments, analysis of lung image segmentation problems, provide direction for further research and valuable experience.