Groupwise registration of MR brain images with tumors

Abstract

A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of 'image registration paths' to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p = 7.02 × 10^-9).

Fig. 1

A brain image with a tumor (left), its corresponding low-rank image (middle), and its sparse component (right). Arrows indicate blurred and distorted normal brain regions in the low-rank image. The sparse component captures not only the tumor region but also normal brain regions with large intensity variations across different images. The colorbar indicates relative values of the sparse component image.

Fig. 2

Flow diagram of the proposed GIR method for brain images with tumors.

Fig. 3

Example images of dataset I.

Fig. 4

Example images of dataset II.

Fig. 5

Low-rank images obtained with different values of λ. The images were randomly selected from dataset I (top) and dataset II (bottom), respectively.

Fig. 6

Image inpainting and intermediate results of a randomly selected image of dataset I: (a) an original image in dataset I, (b) sparse component, (c) tumor occurrence map, (d) tumor mask, (e) inpainted image obtained by M-LRSD, and (f) inpainted image obtained by LRSD. Normal brain regions of the original image were better preserved by M-LRSD than LRSD, as marked by the red arrows. The colorbars indicate relative value of sparse component and tumor occurrence map.

Fig. 7

Image error ratios of low-rank images of dataset I using LRSD and M-LRSD.

Fig. 8

Registered images of one randomly selected subject (top row) and average images of registered images (bottom row) obtained by DDGM+ORI, AVG+LRSD and DDGM+M-LRSD.

Fig. 9

Average Jaccard indexes of 54 brain regions of registered images obtained by DDGM+ORI, AVG+LRSD, and DDGM+M-LRSD. L: left, R: right.

Fig. 10

Image segmentation performance for dataset I (top row) and dataset II (bottom row). The high Recall rates indicated that the automatically generated tumor masks largely covered the brain tumors for most of the images.

Fig. 11

Image inpainting and intermediate results of a randomly selected image of dataset II: (a) an original image in dataset II, (b) sparse component, (c) tumor occurrence map, (d) tumor mask, (e) inpainted image obtained by M-LRSD, and (f) inpainted image obtained by LRSD. Normal brain regions of the original image were better preserved by M-LRSD than LRSD, as marked by the red arrows. The colorbars indicate relative value of sparse component and tumor occurrence map.

Fig. 12

Registered images of one randomly selected subject (top row) and group average images of registered images (bottom row) obtained by DDGM+ORI, AVG+LRSD and DDGM+M-LRSD.