2 results about "Curve evolution" patented technology

The embodiment of the application discloses a hippocampus segmentation method and device based on deep learning and a storage medium, wherein the hippocampus segmentation method based on deep learning comprises the following steps: S1, a first image generated by nuclear magnetic resonance imaging is acquired, a target shape in the first image is inferred by using a trained deep belief network, and a second image containing the target shape is obtained; S2, an energy function is constructed according to the second image, a deep belief network driven lattice Boltzmann model is obtained based on the energy function, curve evolution is performed on the first image by using the deep belief network driven lattice Boltzmann model, and a third image and a fourth image after segmentation are obtained, and the fourth image contains the target shape; S3, steps S1 to S2 are executed for multiple times, multiple groups of third images and fourth images obtained are fed back to an error correction convolutional neural network model, multiple fifth images are obtained, the multiple fifth images are averaged and fused, and a segmentation result is obtained.

The invention discloses an image segmentation method of a regional active contour model based on a Laplacian operator. According to the method, an adaptive-Laplacian operator is introduced, and a corresponding energy functional is used as a length regular term of a level set function, so that effective regularization of the level set function is realized. The regularization mechanism not only can suppress false contours generated in the curve evolution process, but also can adaptively adjust the diffusion process, so that noise interference in the level set evolution process is effectively suppressed, image edge structure details are kept, and segmentation robustness and accuracy are improved. Experimental results show that the method has high segmentation precision and robustness in complex natural image segmentation.