A fast adaptive active contour model based on local gray difference for parotid duct / 南方医科大学学报

OBJECTIVE@#To establish a fast adaptive active contour model based on local gray difference for parotid duct image segmentation.@*METHODS@#On the basis of the LBF model, we added the mean difference of the local gray scale inside and outside the contour as the energy term of the driving evolution curve, and the local gray-scale variance difference was used to replace and as the control term of the energy parameter value. Two local similarity factors of different neighborhood sizes were introduced to correct the effects of image gray unevenness and boundary blur to improve the segmentation efficiency.@*RESULTS@#During image segmentation, this algorithm allowed for adaptive adjustment of the evolution direction, velocity and the energy weight of the internal and external regions according to the difference of gray mean and variance between the internal and external regions. This algorithm was also capable of detecting the actual boundary in a complex gradient boundary region, thus enabling the evolution curve to approach the target boundary quickly and accurately.@*CONCLUSIONS@#The proposed algorithm is superior to the existing segmentation algorithms and allows fast and accurate segmentation of the parotid duct with well-preserved image details.

Automatic Initialization Active Contour Model for the Segmentation of the Chest Wall on Chest CT / 대한의료정보학회지

OBJECTIVES: Snake or active contours are extensively used in computer vision and medical image processing applications, and particularly to locate object boundaries. Yet problems associated with initialization and the poor convergence to boundary concavities have limited their utility. The new method of external force for active contours, which is called gradient vector flow (GVF), was recently introduced to address the problems. METHODS: This paper presents an automatic initialization value of the snake algorithm for the segmentation of the chest wall. Snake algorithms are required to have manually drawn initial contours, so this needs automatic initialization. In this paper, our proposed algorithm is the mean shape for automatic initialization in the GVF. RESULTS: The GVF is calculated as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the medical images. Finally, the mean shape coordinates are used to automatic initialize thepoint of the snake. The proposed algorithm is composed of three phases: the landmark phase, the procrustes shape distance metric phase and aligning a set of shapes phase. The experiments showed the good performance of our algorithm in segmenting the chest wall by chest computed tomography. CONCLUSIONS: An error analysis for the active contours results on simulated test medical images is also presented. We showed that GVF has a large capture range and it is able to move a snake into boundary concavities. Therefore, the suggested algorithm is better than the traditional potential forces of image segmentation.

The Snake Model Based on the Greedy Algorithm / 中国医学物理学杂志

Objective:For improving the imperfection of the Active contour model.Methods:This paper puts forward a new active contour model based on the greedy algorithm.The average contour length term is added into the internal energy of the model.The gradient directional energy is introduced to the external energy of the model.A fast algorithm is introduced to solve the minimum of region energy.The algorithm of add or delete snaxel also adopted in this paper.Segmentation of the MRI brain tumor are studied in the experiments.Results:Comparing to the manual segmentation and the GVF segmentation,the method is better.Conclusion:The results of experiments indicate that the model is none sensitive to initial contour.So this algorithm is practical.