Multiresolution and multiregion variational level set image segmentation method

Abstract

The invention discloses a multiresolution and multiregion variational level set image segmentation method which comprises the following steps: setting the order of resolution and the number of segmented regions, and carrying out continuous downsampling on an original image in each dimension according to a spatial resolution so as to generate an image with a resolution of 2L; carrying out curve evolution on the image by using a variational level set minimized energy model so as to generate N-1 zero level set evolutionary curve equations; constructing an initialized evolutionary curve by takingthe evolutionary curve (obtained by taking 2i as coefficient) as the next resolution, then carrying out curve evolution on the initialized evolutionary curve by using a multiresolution level set method so as to obtain N-1 zero level set curve evolution equations in current resolution; and finally, repeating the evolution process until the original-resolution image is achieved, and then obtaining the segmentation results. The method provided by the invention has the advantages of avoiding the superposition and missing of the segmented regions, reducing the noise interference, and reducing the search space.

Description

technical field [0001] The present invention relates to an image segmentation method in the technical field of image processing, in particular to a multiresolution and multiregion variational level set (Multiresolution and Multiregion Level Set, MR-MRSL) image segmentation method. technical background [0002] Image segmentation is an important part of image feature extraction and classification. The purpose of image segmentation is to separate the gray homogeneous regions in the image and express them through the boundaries of each homogeneous region. In recent years, the level set segmentation method has been widely used in computer vision, such as image segmentation, motion tracking, and 3D reconstruction, due to its free topology and multi-information fusion. The level set image segmentation method based on the C-V model has several advantages: 1. The definition domain of the involved image function is the whole image, which has global characteristics. Therefore, the im...

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Problems solved by technology

However, for remote sensing images, medical images and natural images with noise, this method has several defects: 1. The C-V model only considers the gray level when dividing homogeneous regions, and it is powerless for multi-channel image processing; 2. After each update of the model, the signed distance function needs to be re-initialized, and the image with rich high-resolution data has a very large amount of calculation; 3. The model cannot stably detect the inner region of the target with thick holes and triple points

These methods are all improved based on the two-region level set method to improve the segmentation results, but image segmentation for multiple regions is very difficult, the main reason is that multiple closed curve segmentation will cause overlapping of the segmented image regions, and for a large amount of data is very time consuming when the number of loops is high

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