Robust Intensity Standardization in Brain Magnetic Resonance Images

Abstract

The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient obtained on the same scanner in different moments. This affects postprocessing tasks such as automatic segmentation or unsupervised/supervised classification methods, which strictly depend on the observed image intensities, compromising the accuracy and efficiency of many image analyses algorithms. A large number of MR images from public databases, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, were employed together with synthetic MRIs. Combining both histogram and tissue-specific intensity information, a correspondence is obtained for each tissue across images. The novelty consists of computing three standardizing transformations for the three main brain tissues, for each tissue class separately. In order to create a continuous intensity mapping, spline smoothing of the overall slightly discontinuous piecewise-linear intensity transformation is performed. The robustness of the technique is assessed in a post hoc manner, by verifying that automatic segmentation of images before and after standardization gives a high overlapping (Dice index >0.9) for each tissue class, even across images coming from different sources. Furthermore, SBST efficacy is tested by evaluating if and how much it increases intertissue discrimination and by assessing gaussianity of tissue gray-level distributions before and after standardization. Some quantitative comparisons to already existing different approaches available in the literature are performed.

Keywords: Alzheimer’s Disease Neuroimaging Initiative; General intensity scale; Intensity standardization; Magnetic Resonance Imaging; Nonlinear registration.

Fig. 1

A graphical illustration of the L4 method, directly derived from the literature (a). Histogram landmarks for the three tissues (CSF, GM, WM) in the tissue-based (SBST) and L4 standardization (b). A spline fitting of the SBST curve is shown (600 × 194 mm (300 × 300 DPI))

Fig. 2

An example of image standardization. Histograms of the template and the whole L4-standardized image (a). Histograms of the template and the three tissues (CSF, GM, WM): nonstandardized (NS) (b), L4-standardized (c), and SBST-standardized (d) histograms, according to the transformation in Fig. 1b (189 x 123 mm (300 x 300 DPI))

Fig. 3

Box plot of the Dice index for 250 ADNI MRIs with different degree of neurodegenerative pathology, segmented with the FMRIB’s Automated Segmentation Tool (FAST) (414 x 190mm (300 x 300 DPI))

Fig. 4

A brain image, non-standardized (NS), SBST, and L4 intensity standardized, and their histograms (339 x 388 mm (300 x 300 DPI))

Fig. 5

Box plots (left to right, top to bottom) of CSF, GM, WM, and BRAIN mean absolute errors for 250 MR images (a mixture of ADNI and OASIS images randomly selected), non-standardized (NS), L4 and SBST standardized

Fig. 6

Variation in per-tissue Jeffreys divergences, between the same tissue, SBST and L4 standardized, with respect to the NS corresponding one (a). Variation in intertissue Jeffreys divergences (b)