The present paper focuses on a new class of mesh filter for grayscale images, called grid smoothing filter. The framework presented considers an image as a sampling grid associated to a set of gray levels. Furthermore, the sampling grid is seen as mesh composed by vertices and edges, the number of vertices being equal to the number of pixels in the image. Embedding the mesh in a 2D Euclidian sp...

The prior model or penalizing term in Bayesian image analysis is typically a Markov random eld parametrized by one or more smoothing parameters. For many commonly applied Markov random eld penalizing terms there do not exist both objective and practically applicable methods for choosing the smoothing parameters. In this paper we discuss approaches to analysis of residuals in a simple case of im...

Images often contain noise due to imperfections of image acquisition techniques. Noise should be removed from images so that the details of image objects (e.g., blood vessels, inner foldings, or tumors in human brain) can be clearly seen, and the subsequent image analyses are reliable. With broad usage of images in many disciplines like medical science, image denoising has become an important r...

While region-based image alignment algorithms that use gradient descent can achieve sub-pixel accuracy when they converge, their convergence depends on the smoothness of the image intensity values. Image smoothness is often enforced through the use of multi-scale approaches in which images are smoothed and downsampled. Yet, these approaches typically use fixed smoothing parameters which may be ...

This paper describes a novel edge-preserved smoothing method specialized for intravascular ultrasound (IVUS) image. In the proposed method, the processing of an anisotropic diffusion filter is controlled by the weighted separability of an IVUS image. The present method not only reduces a speckle noise but also enhances an edge on an IVUS image. The effectiveness of the proposed method is verifi...

In this correspondence, we present a new approach to two-dimensional (2-D) robust spline image smoothing based on the M-estimator algorithm. Unlike in other M-estimator based image processing algorithms, the new algorithm takes into consideration the spatial relations between picture elements. The contribution of the sample to the model depends not only on the current residual of that sample, b...

Introduction: Filtering can greatly affect the quality of clinical images. Determining the best filter and the proper degree of smoothing can help to ensure the most accurate diagnosis. Methods: Forty five patient’s data aquired during brain phantom SPECT studies were reconstructed using filtered back-projection technique. The ramp, Shepp-Logan, Cosine, Hamming, Hanning, Butterworth, Metz...

The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...

We consider a class of smoothing methods for minimization problems where the feasible set is convex but the objective function is not convex, not differentiable and perhaps not even locally Lipschitz at the solutions. Such optimization problems arise from wide applications including image restoration, signal reconstruction, variable selection, optimal control, stochastic equilibrium and spheric...

This thesis will focus on applying smoothing splines to magnetic resonance image (MRI) analysis. Some additional work on support vector machine with a hybrid loss function will be discussed. We apply smoothing splines to both the structural MRI and functional MRI. For the structural MRI, we fit thin plate splines to overlapping blocks of the image with different configurations of knots. The opt...