Joint sparsity and fidelity regularization for segmentation-driven CT image preprocessing

Abstract

In this paper, we propose a novel segmentation-driven computed tomography (CT) image preprocessing approach. The proposed approach, namely, joint sparsity and fidelity regularization (JSFR) model can be regarded as a generalized total variation (TV) denoising model or a generalized sparse representation denoising model by adding an additional gradient fidelity regularizer and a stronger gradient sparsity regularizer. Thus, JSFR model consists of three terms: intensity fidelity term, gradient fidelity term, and gradient sparsity term. The interactions and counterbalance of these terms make JSFR model has the ability to reduce intensity inhomogeneities and improve edge ambiguities of a given image. Experimental results carried out on the real dental cone-beam CT data demonstrate the effectiveness and usefulness of JSFR model for CT image intensity homogenization, edge enhancement, as well as tissue segmentation.

摘要

创新点

本文提出了一种新的分割驱动的CT图像预处理方法, 称为联合稀疏性和保真性正则化模型。与传统模型相比, 该模型引入了梯度保真项和梯度稀疏项, 因而是全变差TV去噪模型和稀疏表示去噪模型的推广。模型中灰度保真项, 梯度保真项和梯度稀疏项各项间的相互平衡使得该模型可以很好的降低CT图像灰度的非其次性, 同时, 也能够较好的增强CT图像的模糊边缘。本文用一组真实螺旋锥束CT牙齿数据评估了该模型的有效性。实验结果表面, 所提方法能够有效的实现CT图像的灰度齐次化, 边缘增强和组织分割。