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Median type filters coupled with the Laplacian distribution assumption have shown a high efficiency in suppressing impulse noise. We however demonstrate in this paper that the Gaussian distribution assumption is more preferable than Laplacian distribution assumption in suppressing impulse noise, especially for high noise densities. This conclusion is supported by numerical experiments with different noise densities and filter models. [ABSTRACT FROM AUTHOR]

In most computer vision applications, the extraction of key image features, what- ever the transformations applied and the image degradations observed, is of major importance. A large diversity of approaches has been reported in the literature. This chapter concentrates on one particular processing frame: the moment-based methods. It provides a survey of methods proposed so far for the derivation of moment invari- ants to geometric transforms and blurring effects. Theoretical formulations and some selected examples dealing with very different problems are given. [ABSTRACT FROM AUTHOR]

A study was conducted to analyze the characteristics of the motion of coronary arteries in two and three dimensions. The main left coronary artery centerlines were determined from digital subtraction angiography image sequences. Local and global motion properties were examined along the sequence. A new numeric was proposed to symbolic image transformation to support the needed facts for knowledge-based motion interpretation.

This work addresses random geometrical uncertainties that are intrinsically observed in radiation therapy by means of a new deconvolution method combining a series expansion and a Butterworth filter. The method efficiently suppresses high-frequency components by discarding the higher order terms of the series expansion and then filtering out deviations on the field edges. An additional approximation is made in order to set the fluence values outside the field to zero in the robust profiles. Thismethod is compared to the deconvolution kernel method for a regular 2D fluence map, a real intensity-modulated radiation therapy field, and a prostate case. The results show that accuracy is improved while fulfilling clinical planning requirements. [ABSTRACT FROM AUTHOR]

In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors. [ABSTRACT FROM AUTHOR]

Scale invariants of Tchebichef moments are usually achieved by a linear combination of corresponding invariants of geometric moments or via an iterative algorithm to eliminate the scale factor. According to the properties of Tchebichef polynomials, we propose a new approach to construct scale invariants of Tchebichef moments. An algorithm based on matrix multiplication is also provided to efficiently compute the 3D moments and invariants. Several experiments are carried out to validate the effectiveness of our descriptors and algorithm. [ABSTRACT FROM AUTHOR]

Prostate segmentation in 3-D transrectal ultrasound images is an important step in the definition of the intra-operative planning of high intensity focused ultrasound (HIFU) therapy. This paper presents two main approaches for the semi-automatic methods based on discrete dynamic contour and optimal surface detection. They operate in 3-D and require a minimal user interaction. They are considered both alone or sequentially combined, with and without postregularization, and applied on anisotropic and isotropic volumes. Their performance, using different metrics, has been evaluated on a set of 28 3-D images by comparison with two expert delineations. For the most efficient algorithm, the symmetric average surface distance was found to be 0.77 mm. [ABSTRACT FROM AUTHOR]

Processing blurred images is a key problem in many image applications. Existing methods to obtain blur invariants which are invariant with respect to centrally symmetric blur are based on geometric moments or complex moments. In this paper, we propose a new method to construct a set of blur invariants using the orthogonal Legendre moments. Some important properties of Legendre moments for the blurred image are presented and proved. The performance of the proposed descriptors is evaluated with various point-spread functions and different image noises. The comparison of the present approach with previous methods in terms of pattern recognition accuracy is also provided. The experimental results show that the proposed descriptors are more robust to noise and have better discriminative power than the methods based on geometric or complex moments. [ABSTRACT FROM AUTHOR]

The article discusses the evolution of computer-assisted and image-guided therapy. It explores the breakthrough and impact of therapeutic methods including temperature-based therapies, conventional radiotherapy, and laser-based therapy as well as multimodal therapy which minimizes impact to the surrounding tissues and organs. It also outlines the innovations of the physically enhanced and image-guided drug therapy where drug delivery is activated by heat, ultrasound, and light.

The present work describes a Bayesian maximum a posteriori (MAP) method using a statistical multiscale wavelet prior model. Rather than using the orthogonal discrete wavelet transform (DWT), this prior is built on the translation invariant wavelet transform (TIWT). The statistical modeling of wavelet coefficients relies on the generalized Gaussian distribution. Image reconstruction is performed in spatial domain with a fast block sequential iteration algorithm. We study theoretically the TIWT MAP method by analyzing the Hessian of the prior function to provide some insights on noise and resolution properties of image reconstruction. We adapt the key concept of local shift invariance and explore how the TIWT MAP algorithm behaves with different scales. It is also shown that larger support wavelet filters do not offer better performance in contrast recovery studies. These theoretical developments are confirmed through simulation studies. The results show that the proposed method is more attractive than other MAP methods using either the conventional Gibbs prior or the DWT-based wavelet prior. [ABSTRACT FROM AUTHOR]

The article provides the basic formulations of moments. The authors review recently studied orthogonal moments including Tchebichef, Krawtchouk, Racah, dual Hahn moments, which all posses the property important in image processing, specifically in medical imaging. They discuss the different classification of moments: nonorthogonal moments, geometric moments, rotational moments, orthogonal moments, continuous orthogonal moments and discrete orthogonal moments, then sketch their different expressions, instead of presenting their theoretical details.

The article reports on the results of a bibliometric analysis to determine how the scientific contents are evolving over time, and what relations might exist between journals and conferences. It focuses on biomedical engineering production (BME). The article describes how BME and the journals evolved with time. It shows that the most frequent words are common to the journals.

The article studies IEEE publications using correspondence analysis. The IEEE subset consists of 354, 356 documents published between 1967 and 2003. The documents include papers in journals, conference papers and conference proceedings. The findings show the evolution over time of the contents of IEEE publications. The results also reveal that the engineering field is more attracted to computer science and less rooted to electronics and electrotechnical engineering.

The article discusses the macroimaging aspect of medical imaging. A classification is proposed that identifies motion from imaging and vice versa and involves correction of motion as well as evoked motion. A method wherein the objects of interest are made visible due to some internal motion was performed through the use of magnetic resonance angiography. It is noted that imaging of motion which is not corrected or compensated may lead to blurring of image quality. Reconstruction from motion and evoked motion are further discussed.

Comments on the rationale for the research on computer vision and graphics. Reason for the search for more windows in science; Acquisition of scientific autonomy; Expression of concerns on biomedical issues.

Clinical interventional hemodynamic studies quantify the ventricular function from two-dimensional (2-D) X-ray projection images without having enough information of the actual three-dimensional (3-D) shape of this cardiac cavity. This paper reports a left ventricle 3-D reconstruction method from two orthogonal angiographic projections. This investigation is motivated by the lack of information about the actual 3-D shape of the cardiac cavity. The proposed algorithm works in 3-D space and considers the oblique projection geometry associated with the biplane image acquisition equipment. The reconstruction process starts by performing an approximate reconstruction based on the Cylindrical Closure Operation and the Dempster--Shafer theory. This approximate reconstruction is appropriately deformed in order to match the given projections. The deformation procedure is carried out by an iterative process that, by means of the Dempster--Shafer and the fuzzy integral theory, combines the information provided by the projection error and the connectivity between voxels. The performance of the proposed reconstruction method is evaluated by considering first the reconstruction of two 3-D binary databases from two orthogonal synthetized projections, obtaining errors as low as 6.48%. The method is then tested on real data, where two orthogonal preprocessed angiographic images are used for reconstruction. The performance of the technique, in this case, is assessed by means of the projection error, whose average for both views is 7.5%. The reconstruction method is also tested by performing the 3-D reconstruction of a ventriculographic sequence throughout an entire cardiac cycle. [ABSTRACT FROM AUTHOR]

Discusses biomedical signal processing schemes. Time-series processing methods; Time and frequency transforms; Blind signal processing; Empirical mode decomposition; Kernel methods; Data mining.

Computational modeling and imaging in biology and medicine are gaining more and more interest with the discovery of in-depth structural and functional knowledge at all space and time scales (molecule to proteins, cells to organs and organizms). The recursion between description levels for highly dynamical, interacting and complex systems, i.e the integrative approach, is a very challenging topic where formal models, observational tools and experimental investigations have to be closely designed, coupled and confronted together. Imaging techniques play a major role in this interdisciplinary attempt to elucidate this biocomplexity: they convey relevant information about the underlying mechanisms, depict the conformations and anatomical topologies and draw the biophysical laws they may follow. Furthermore, the basic image analysis tools (from calibration to segmentation, motion estimation and registration up to pattern recognition) are generic enough to be of value whatever the objects under consideration. The same comments apply when Computer Graphics or Virtual Reality techniques are concerned. This paper will survey the recent contributions dealing with both models, imaging data and processing frames. Examples ranging over different scales, from macro to nano, will be given in order to enhance the mutual benefits and perspectives that can be expected from this coupling. [ABSTRACT FROM AUTHOR]