A Non-Linear and Non-Iterative Noise Reduction Technique for Medical Images: Concept and Methods Comparison

Filtering is a preliminary process in many medical image processing applications. It is aiming at reducing noise in images. Any post-processing tasks may benefit from the reduction of noise. In this contribution, a method for edge-preserving smoothing of 2D and 3D medical images is described. The proposed method uses a narrow spatial window and takes only a single iteration to denoise an image. It integrates geometric, photometric and local structural similarities to achieve non-linear noise reduction. We have applied this novel method to medical images and compared its denoising capability with other noise reduction techniques. The experimental results have shown that our method is capable of reducing severe noise, and is an adequate pre-process to improving the quality of segmentation and facilitating the feature extraction process

A nonlinear and non-iterative noise reduction technique for medical images: concept and methods comparison

Filtering is a preliminary process in many medical image processing applications. It is aiming at reducing noise in images. Any post-processing tasks may benefit from the reduction of noise. In this paper, a method for edge-preserving smoothing of 2D and 3D medical images is described. The proposed method uses a narrow spatial window and takes only a single iteration to deinoise an image. It integrates geometric, photometric and local structural similarities to achieve nonlinear noise reduction. We have applied this novel method to medical images and compared its denoising capability with other noise reduction techniques. The experimental results have shown that our method is capable of reducing severe noise, and is an adequate preprocess to improving the quality of segmentation and facilitating the feature extraction process. (C) 2004 CARS and Elsevier B.V. All rights reserved

Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning

Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly

Local Orientation Smoothness Prior for Vascular Segmentation of Angiography

We present a new generic method for vascular segmentation of angiography. Angiography is used for the medical diagnosis of arterial diseases. To facilitate an e#ective and e#cient review of the vascular information in the angiograms, segmentation is a first stage for other post-processing routines. The method we propose uses a novel a priori --- local orientation smoothness prior --- to enforce an adaptive regularization constraint for the vascular segmentation within the Bayes' framework. It aspires to segment a variety of angiographies and is aimed at improving the quality of segmentation in low blood flow regions. Our algorithm is tested on numerical phantoms and clinical datasets. The experimental results show that our method produces better segmentations than the maximum likelihood estimation and the estimation with a multi-level logistic Markov random field model. Furthermore, the novel algorithm produces aneurysm segmentations comparable to the manual segmentations obtained from an experienced consultant radiologist

Vascular segmentation in three-dimensional rotational angiography based on maximum intensity projections

Three-dimensional rotational angiography (3D-RA) is a relatively new and promising technique for imaging blood vessels. In this paper, we propose a novel 3D-RA vascular segmentation algorithm, which is fully automatic and very computationally efficient, based on the maximum intensity projections (MEP) of 3D-RA images. Validation results on 13 clinical 3D-RA datasets reveal that, according to the agreement between the segmentation results and the ground truth, our method (a) outperforms both the Maximum a posteriori-expectation maximization (MAP-EM)-based method and the MAP-Markov random field (MAP-MRF)-based segmentation method, and (b) works comparably to the optimal global thresholding method. Experimental results also show that our method can successfully segment major vascular structures in 3D-RA and produce a lower false positive rate than that of the MAP-EM-based and MAP-MRF-based methods