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Two-Steps-GVF-Snake-Model

Active contours, or snakes, are widely used in medical image processing applications, mainly to locate the desired area boundaries. Gradient vector flow (GVF) field, like other methods of calculating external force fields, is proposed to address ordinary snake models’ problems, such as poor convergence in indentations and low accuracy in segmentation of objects owned weak borders. These problems are most pronounced in high-noise images, such as ultrasound images. In order to solve the problems more, we utilized the generalized gradient vector flow snake model using minimal surface and two steps converging using both vector based normalization and component-based normalization with distinct controlling parameters on active contour. We adopt minimal surface function to address the problem of low segmentation accuracy in other conventional methods. We also use two steps converging using both vector-based and component-based normalization with distinct controlling parameters to improve the snake curve converging into long and thin indentations plus higher accuracy in noisy and meandering areas. The results obtained and compared with other methods show that the proposed active contour model not only can converge better into long and thin indentations, along with maintaining weak boundaries but also shows higher accuracy in segmentation of tortuous areas, especially in noisy images.

AryaAftab/Physics-based-neural-network

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