Abstract: Abstract: The current mainstream segmentation method for left atrial fibrosis is to manually divide the atrial wall area first， and then use threshold method to extract the fibrosis part within the atrial wall area. This not only requires the operator to have professional background knowledge， but also requires a large workload， and the threshold method is also difficult to accurately segment fibrosis areas with different degrees of severity at the same time. To address the above issues， this paper proposes a new method for segmenting fibrotic regions in MR images. Firstly， the Laplace sharpening algorithm is used to improve the contrast of the fibrotic area， while the kernel correlation filtering algorithm is used to track the target area to remove tissue outside the atrium; Secondly， the segmentation effects of region growth method， active contour method， and Hessian matrix based segmentation algorithm on fibrotic regions were compared， and the most effective segmentation method was selected; Finally， we reconstruct and render the 3D point cloud data of the fiber region. The experimental results show that this method does not require manual segmentation of the cardiac atrial wall region by image， and the segmentation results have high accuracy， which can better assist doctors in diagnosing related diseases.

Key words: Key words: magnetic resonance imaging, atrial fibrosis, image enhancement, target tracking, segmentation algorithm