LAUSR

Abstract The vessel structures of the blood circulatory system are one of the most complex structures of the human body. Modern computed tomography techniques allow acquiring high resolution images, but at the same time, the number of artifacts in output images is quite high. They may affect diagnostic result and may obscure or simulate pathology. The idea of our method is to represent a 3D computed tomography image as a combination of vascular structure and background that has normal distribution in some neighborhood. Locally adaptive non-linear filters decrease global difference between bright and dark voxels, even if it produces better local contrast. Luminosity and contrast are observed from image background and are used for normalization of the whole image. After making background normalization at each layer, we merge layers and reconstruct vessels structure. The proposed method has been tested on real cardiac CT images, the test results show that high quality 3D structures are reconstructed, without requiring a priori knowledge or user interaction. The tested dataset has been made publicly available. The proposed approach can be applied to denoising computed tomography images, enhancing of contrast in lesion areas without changing topology of initial vessel structures.