In this paper we study the impact of denoising the raw high angular resolution diffusion imaging (HARDI) data with the Non-Local Means filter adapted to Rician noise (NLMr). We first show that NLMr filtering improves robustness of apparent diffusion coefficient (ADC) and orientation distribution function (ODF) reconstructions from synthetic HARDI datasets. Our results suggest that the NLMr filt...

Introduction: Myocardial T2* measurement is a valuable tool for non-invasive assessment of tissue iron overload which can prevent disease in thalassemia patients (1). For the heavily iron loaded heart, T2* is substantially shortened (<10ms) and the signal to noise ratios (SNRs) of later echo images are usually very low (2). Various signal processing techniques have been proposed for denoising M...

To denoise a reference patch, the Non-Local-Means denoising filter processes a set of neighbor patches. Few Nearest Neighbors (NN) are used to limit the computational burden of the algorithm. Here here we show analytically that the NN approach introduces a bias in the denoised patch, and we propose a different neighbors’ collection criterion, named Statistical NN (SNN), to alleviate this issue....

Clinical MRI data is normally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. For this reason, de-noising methods are often applied to increase the SNR and improve image quality. Most of these methods work on single channel images by correcting each grey level using an implicit model of the surrounding region, but with...

The first part of this paper deals with the topic of finding equivalent norms and characterizations for vector-valued Besov and Triebel-Lizorkin spaces Bs p,q(E) and F s p,q(E). We will deduce general criteria by transferring and extending a theorem of Bui, Paluszyński and Taibleson from the scalar to the vector-valued case. By using special norms and characterizations we will derive necessary ...

The fast global k-means (FGKM) clustering algorithm is one of the most effective approaches for resolving the local convergence of the k-means clustering algorithm. Numerical experiments show that it can effectively determine a global or near global minimizer of the cost function. However, the FGKM algorithm needs a large amount of computational time or storage space when handling large data se...

Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure i...

Electrocardiogram (ECG) is an important biomedical signal for analyzing heart activity. Analysis of ECG becomes difficult if noise is augmented with the signal during acquisition. During recent years, several denoising techniques were analyzed within the field of signal processing. In this paper, non local means (NLM) filtering technique is explored for denoising the ECG signal. The noisy ECG s...

OBJECTIVE Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The obje...

This chapter presents an efficient technique for linking edge points in order to generate a closed contour representation. The original intensity image, as well as its corresponding edge map, are assumed to be given as input to the algorithm (i.e., an edge map is previously computed by some of the classical edge detector algorithms). The proposed technique consists of two stages. The first stag...