A new variational shape-from-orientation approach to correcting intensity inhomogeneities in MR images

S. Lai, M. Fang
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引用次数: 20

Abstract

A new algorithm based on shape from orientation formulation in a regularization framework is proposed for correcting intensity inhomogeneities in MR images. Unlike most previous methods, this algorithm is fully automatic and very efficient. In addition, it can be applied to a wide variety of images since no prior classification knowledge is assumed. In this algorithm, the authors use a finite element basis to represent the bias field function. Orientation constraints are computed at the nodes of the finite element discretization away from the boundary between different regions. The selection of reliable orientation constraints is facilitated by the goodness of fitting a first-order polynomial model to the neighborhood of each nodal location. The selected orientation constraints are integrated in a regularization framework, which leads to the minimization of a convex and quadratic energy function. This energy minimization is achieved by solving a linear system with a large, sparse, symmetric and positive semi-definite stiffness matrix. The authors employ an adaptive preconditioned conjugate gradient algorithm to solve the linear system efficiently. Experimental results on a variety of MR images are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
一种新的变分形状定向校正MR图像强度不均匀性的方法
提出了一种正则化框架下基于方向形状公式的MR图像强度非均匀性校正算法。与以前的大多数方法不同,该算法是全自动的,非常高效。此外,它可以应用于各种各样的图像,因为没有假设先验分类知识。在该算法中,作者使用有限元基来表示偏置场函数。在远离不同区域边界的有限元离散节点处计算方向约束。通过将一阶多项式模型拟合到每个节点位置的邻域,方便了可靠方向约束的选择。选择的方向约束被整合到一个正则化框架中,从而导致凸和二次能量函数的最小化。这种能量最小化是通过求解一个具有大的、稀疏的、对称的、正半定刚度矩阵的线性系统来实现的。采用自适应预条件共轭梯度算法对线性系统进行有效求解。实验结果证明了该算法的有效性和高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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