Image denoising is one of the fundamental and essential tasks within image processing. In medical imaging, finding an effective algorithm that can remove random noise in MR images is important. This paper proposes an effective noise reduction method for brain magnetic resonance (MR) images. Our approach is based on the collateral filter which is a more powerful method than the bilateral filter in many cases. However, the computation of the collateral filter algorithm is quite time-consuming. To solve this problem, we improved the collateral filter algorithm with parallel computing using GPU. We adopted CUDA, an application programming interface for GPU by NVIDIA, to accelerate the computation. Our experimental evaluation on an Intel Xeon CPU E5-2620 v3 2.40GHz with a NVIDIA Tesla K40c GPU indicated that the proposed implementation runs dramatically faster than the traditional collateral filter. We believe that the proposed framework has established a general blueprint for achieving fast and robust filtering in a wide variety of medical image denoising applications.
Image registration is one of the fundamental and essential tasks within image processing. It is a process of determining the correspondence between structures in two images, which are called the template image and the reference image, respectively. The challenge of registration is to find an optimal geometric transformation between corresponding image data. This paper develops a new MR image registration algorithm that uses a closed incompressible viscous fluid model associated with mutual information. In our approach, we treat the image pixels as the fluid elements of a viscous fluid flow governed by the nonlinear Navier-Stokes partial differential equation (PDE). We replace the pressure term with the body force mainly used to guide the transformation with a weighting coefficient, which is expressed by the mutual information between the template and reference images. To solve this modified Navier-Stokes PDE, we adopted the fast numerical techniques proposed by Seibold1. The registration process of updating the body force, the velocity and deformation fields is repeated until the mutual information weight reaches a prescribed threshold. We applied our approach to the BrainWeb and real MR images. As consistent with the theory of the proposed fluid model, we found that our method accurately transformed the template images into the reference images based on the intensity flow. Experimental results indicate that our method is of potential in a wide variety of medical image registration applications.
Skull-stripping is one of the most important preprocessing steps in neuroimage analysis. We proposed a hybrid
algorithm based on an adaptive balloon snake model to handle this challenging task. The proposed framework
consists of two stages: first, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides
a labeled image for the snake contour initialization. In the second stage, the contour is initialized outside the
brain surface based on the FPCM result and evolves under the guidance of the balloon snake model, which drives
the contour with an adaptive inward normal force to capture the boundary of the brain. The similarity indices
indicate that our method outperformed the BSE and BET methods in skull-stripping the MR image volumes in
the IBSR data set. Experimental results show the effectiveness of this new scheme and potential applications in
a wide variety of skull-stripping applications.
We propose a double bilateral (DoBi) filter that consists of a classical bilateral filter and a new bilateral filter for image restoration. Bilateral filtering is a simple, noniterative, and effective denoising filter that smooths images while preserving edges by means of a nonlinear combination of adjacent pixel values. A median-metric weighting function is introduced by incorporating a switching median filter into the similarity function. This median-metric component associated with a spatial function constitute the second bilateral filter, which compensates the classical bilateral filter. Moreover, a parameter automation mechanism is proposed to facilitate the restoration procedure. A wide variety of images contaminated by various degrees of Gaussian, impulse, and mixed noise were used to assess the performance of this new restoration algorithm. Experimental results indicated that the DoBi filter outperformed several state-of-the-art methods in both visual image quality and restored signal quantity.
A new image segmentation algorithm that uses the simulation of a charged fluid is developed. Conceptually, a charged fluid consists of charged elements, each of which exerts a repelling electric force on the others. The charged fluid behaves like a liquid such that it flows through and around different obstacles. The boundary of the segmented object is determined by the image gradient, which is modeled as potential wells that stop the propagating front. The simulation is evolved in two steps that are governed by Poisson's equation. The first step distributes the elements of the charged fluid along the propagating interface until an electrostatic equilibrium is achieved. The second step advances the propagating front of the charged fluid such that it deforms into a new shape in response to the equilibrium electric potential and the image potential. The procedure is repeated until the propagating front resides on the boundary of objects being segmented. The electric potential of the simulated system is rapidly calculated using the finite-size particle (FSP) method implemented via the fast Fourier transform (FFT) algorithm. Experimental results using phantom images, photographic pictures, and medical images demonstrate the utility of this new algorithm in a wide variety of image processing applications.
Deformable models are important and popular techniques for
extracting the shape of objects in medical images. We used the
simulation of a physical system (a Charged Fluid) to guide the
evolution of a propagating interface to segment objects in brain
MR and CT images. The Charged Fluid was simulated as a system of
charged particles that exert a repelling electric force upon each
other. In our approach, the boundary of the segmenting object was
determined by the image gradient, which was modeled as potential
wells that stopped the propagating front. The simulation was
evolved in two steps that were governed by Poisson's equation. One
allowed the propagating interface of the Charged Fluid to deform
into a new shape in response to the electric potential and the
image potential. The other allowed Charged Fluid elements to flow
along the propagating interface, which was treated as the surface
of an isolated conductor, until an electrostatic equilibrium was
achieved. The electric potential of the simulated system was
rapidly computed from Poisson's equation by the aid of the Fast
Fourier Transform. The procedure was repeated until the
propagating front resided on the boundary of objects being
segmented. This method was used to extract the contour of the
brain surface for use in skull stripping applications.
The class of deformable models has been broadly used in the segmentation of medical images. We propose a
fast linkage contour, which is guided by some simple evolution rules, to extract the boundaries of objects in
2D images. A Moving Linkage consists of links, each of which is further composed of a tail joint and a head
joint. Classified into horizontal, vertical and frozen (unmoved) types, the links are constricted on grids by the
horizontal and vertical tracks corresponding to the image pixels. Drawing an analogy between an electric current
flowing along a circuit line in the presence of magnetic fields and how it reacts in response to the force, we use
the right-hand rule to guide the motion of each link. In our approach, a small closed linkage contour is initialized
inside the region of interest. Stealing the concept of entropy conditions from level sets, the linkage contour is
easily grown, split and merged governed by some simple deformation rules. The frozen link occurs once the
speed of the free link (horizontal or vertical) is less than a specified threshold due to significant image gradients.
The deformation procedure is repeated until all links become frozen, when the linkage contour resides on the
edge of the segmented object. This new deformable model was validated by the preliminary segmentation results
regarding phantom and medical images.
A computer simulation of a Charged Fluid was developed to segment
objects in medical images. A Charged Fluid conceptually consists
of charged particles, each of which exerts a repelling electric
force upon the others. In our approach, we treat the image
gradients as potential wells where a simulated Charged Fluid flows
through. The evolution of a Charged Fluid consists of two
different procedures. One allows Charged Fluid elements to advance
toward new positions in the simulation along the direction of
their total effective forces. The other allows Charged Fluid
elements to flow along the contour until an electrostatic
equilibrium is achieved. The procedure is repeated until all
Charged Fluid elements reside on the boundaries of objects being
segmented. Preliminary segmentation results were obtained using
this new technique to segment irregular objects in medical images.