MRI Tissue Classification and Bias Field Estimation Based on Coherent Local Intensity Clustering: A Unified Energy Minimization Framework

Abstract

Abstract. This paper presents a new energy minimization method for simultaneous tissue classification and bias field estimation of magnetic resonance (MR) images. We first derive an important characteristic of local image intensitiesthe intensities of different tissues within a neighborhood form separable clusters, and the center of each cluster can be well approximated by the product of the bias within the neighborhood and a tissue-dependent constant. We then introduce a coherent local intensity clustering (CLIC) criterion function as a metric to evaluate tissue classification and bias field estimation. An integration of this metric defines an energy on a bias field, membership functions of the tissues, and the parameters that approximate the true signal from the corresponding tissues. Thus, tissue classification and bias field estimation are simultaneously achieved by minimizing this energy. The smoothness of the derived optimal bias field is ensured by the spatially coherent nature of the CLIC criterion function. As a result, no extra effort is needed to smooth the bias field in our method. Moreover, the proposed algorithm is robust to the choice of initial conditions, thereby allowing fully automatic applications. Our algorithm has been applied to high field and ultra high field MR images with promising results.

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study

“…Most of the current algorithms were developed for brain imaging. [21][22][23] The implementation of these algorithms for breast MRI can be more challenging as a result of the increased FOV. Although N3 algorithm has been considered as an optimal method for intensity inhomogeneity correction by a recent review paper, 17 a study on clinical breast images has shown that using N3 alone may still have problems in breast images, which cannot allow an accurate segmentation.…”

“…More details of the CLIC method can be found in the report by Li et al 23 To evaluate the effect of bias field, we compared the breast segmentation results from three studies: (a) FCM clustering on raw images; (b) CLIC clustering on raw images; and (c) FCM clustering on bias-field-corrected images. In study (a), Basic FCM algorithm was applied to the raw MRI images of the postmortem breast samples.…”

“…18 Recently, a new energy minimization method using coherent local intensity clustering (CLIC) was introduced by Li et al based on the fuzzy c-means (FCM) algorithm. 23 CLIC method is robust to initialization and is capable of generating a smooth bias field due to the coherent nature of the criterion function. Both the bias field estimation and tissue classification can be calculated simultaneously with the CLIC method, which allows for fully automatic computer-assisted applications.…”

“…23,24 There are only few studies on breast density quantifications using the CLIC method on MRI images. 18 One major limitation for a clinical MRI segmentation study is the lack of a gold standard for breast density estimation.…”

A review of Methods for Bias Correction in Medical Images

“…In other words, the intensity of the same tissue should always be similar in one image and not vary with the location of the tissue. For many automated analysis techniques such as segmentation, classification and registration, this property can facilitate their procedures and make their results reasonable (C. Li, Li, Kao, & Xu, 2009;C. Li, Xu, Anderson, & Gore, 2009;Xu, Wan, & Bian, 2013;C.…”