3 research outputs found
Low-Rank Based Image Analyses for Pathological MR Image Segmentation and Recovery
The presence of pathologies in magnetic resonance (MR) brain images causes challenges in various image analysis areas, such as registration, atlas construction and atlas-based segmentation. We propose a novel method for the simultaneous recovery and segmentation of pathological MR brain images. Low-rank and sparse decomposition (LSD) approaches have been widely used in this field, decomposing pathological images into (1) low-rank components as recovered images, and (2) sparse components as pathological segmentation. However, conventional LSD approaches often fail to produce recovered images reliably, due to the lack of constraint between low-rank and sparse components. To tackle this problem, we propose a transformed low-rank and structured sparse decomposition (TLS2D) method. The proposed TLS2D integrates the structured sparse constraint, LSD and image alignment into a unified scheme, which is robust for distinguishing pathological regions. Furthermore, the well recovered images can be obtained using TLS2D with the combined structured sparse and computed image saliency as the adaptive sparsity constraint. The efficacy of the proposed method is verified on synthetic and real MR brain tumor images. Experimental results demonstrate that our method can effectively provide satisfactory image recovery and tumor segmentation
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors
High resolution Magnetic Resonance (MR) images are desired for accurate
diagnostics. In practice, image resolution is restricted by factors like
hardware and processing constraints. Recently, deep learning methods have been
shown to produce compelling state-of-the-art results for image
enhancement/super-resolution. Paying particular attention to desired
hi-resolution MR image structure, we propose a new regularized network that
exploits image priors, namely a low-rank structure and a sharpness prior to
enhance deep MR image super-resolution (SR). Our contributions are then
incorporating these priors in an analytically tractable fashion \color{black}
as well as towards a novel prior guided network architecture that accomplishes
the super-resolution task. This is particularly challenging for the low rank
prior since the rank is not a differentiable function of the image matrix(and
hence the network parameters), an issue we address by pursuing differentiable
approximations of the rank. Sharpness is emphasized by the variance of the
Laplacian which we show can be implemented by a fixed feedback layer at the
output of the network. As a key extension, we modify the fixed feedback
(Laplacian) layer by learning a new set of training data driven filters that
are optimized for enhanced sharpness. Experiments performed on publicly
available MR brain image databases and comparisons against existing
state-of-the-art methods show that the proposed prior guided network offers
significant practical gains in terms of improved SNR/image quality measures.
Because our priors are on output images, the proposed method is versatile and
can be combined with a wide variety of existing network architectures to
further enhance their performance.Comment: Accepted to IEEE transactions on Image Processin