6 results about "Level set segmentation" patented technology

Systems and methods for delineating tumour boundaries using a three-dimensional (3D) image stack, such as photoacoustic image slices, comprising a plurality of images. Multiple maximum intensity projections (MIPs) are derived from the stack, and level set segmentation is performed on those MIPs, to derive a boundary that bounds a region of interest (i.e., a tumour boundary). Structural and functional information is then derived for the region of interest, to analyse the tumour tissue.

The present disclosure provides a method, an apparatus, an electronic device, and a computer-readable storage medium for evaluating the quality of image segmentation, which relates to the field of artificial intelligence. Among them, the method for evaluating the quality of image segmentation includes: segmenting an image based on a level set segmentation model to obtain a segmentation result; determining a metric parameter of pixels in the image based on the level set signed distance function; determining a warning value of the pixels in the image based on the metric parameter; obtaining a warning area of the image according to the warning value; and generating a quality evaluation result of the segmentation result based on the area of the warning area. Through the technical solution of the present disclosure, a relatively accurate detection result can be obtained, and the solution has good robustness. Furthermore, it is beneficial to further screen out reliable areas and warning areas in the segmentation result, thereby providing a reliable reference for the overall segmentation quality of the image.

The present invention discloses a sonar target processing method based on heterogeneous filtering detection and level set segmentation; the method is as follows: 1. Image acquisition; 2. Denoising processing; 3. Superpixel image segmentation; 4. Heterogeneous filtering target detection; 5. Adaptive threshold processing; 6. Target fine segmentation; According to the imaging characteristics of side-scan sonar, the present invention adopts a step-by-step heterogeneous filtering method to process the image. On the one hand, it effectively removes the imaging effect of uneven intensity in the sonar image, and on the other hand, it effectively enhances the target bright area and dark area of the image, effectively improving the false alarm correct rate of target detection. The adaptive threshold processing in the present invention determines the threshold according to the local information characteristics of the image after heterogeneous filtering, directly performs region segmentation on the filtered image, and obtains the initial contour of the target. The fine segmentation based on level set in the present invention uses the result of threshold processing as the initial contour, combines the superpixel boundary constraint, and drives the segmentation contour to the superpixel boundary to obtain an accurate target contour.

This invention proposes a rock thin section image segmentation method based on foreground / background divide-and-conquer and layered color processing. The method first performs color space transformation and principal component analysis (PCA) dimensionality reduction on the rock thin section image to construct a single-channel structural representation for segmentation. Then, a first global segmentation is performed based on a level set model to achieve preliminary extraction of macroscopic mineral structures. Subsequently, based on the initial segmentation results, the image is divided into foreground and background regions, and enhancement channels are selected in different color spaces for each region to perform a second-stage level set segmentation. Specifically, the background region recovers weak contrast structural information by enhancing chromaticity differences, while the foreground region improves the separation ability of fine grains by enhancing texture contrast. Finally, the two segmentation results are fused, and combined with region filtering and morphological optimization, the final rock thin section segmentation result is obtained. This method can effectively improve the accuracy and stability of rock thin section image segmentation under complex lithological conditions.

The present invention provides a method for segmenting pigmented skin lesion images based on reverse-channel filling CNN and level set. This method is based on reverse-channel filling CNN and a joint backpropagation learning algorithm. It obtains the approximate spatial position of the target based on the attention mechanism, enhances the spatial position features of the target through reverse-channel filling, and improves the accuracy of the target position output by CNN. Further, the feature energy learned by CNN is input into the level set segmentation model to drive the level set evolution, and a backpropagation learning algorithm combining CNN and level set is established to further improve the segmentation accuracy.

The application discloses a kind of three-dimensional reconstruction method and system based on two-dimensional forward-looking sonar image, the method includes: step one: for the two-dimensional forward-looking sonar sequence collected, introduce based on mahalanobis distance detection method, determine the initial profile of local binary fitting level set segmentation method, further, using a local binary fitting (LBF) level set segmentation method, determine the target to be reconstructed region based on the initial profile detected;Step two: using the registration method based on phase correlation, the target region determined in step one is registered, and the relative displacement of sonar movement is calculated.Step three: based on the results of the above steps, using blind deconvolution algorithm, reconstructs the three-dimensional point cloud image of target.Compared with the prior art, the three-dimensional reconstruction of the target can be performed without pre-acquiring the horizontal position information of the sonar, and has certain anti-noise ability.The method disclosed in the application is more suitable for three-dimensional reconstruction of underwater targets.