General Adaptive Neighborhood Image Processing for Biomedical Applications

In biomedical imaging, the image processing techniques using spatially invariant transformations, with fixed operational windows, give efficient and compact computing structures, with the conventional separation between data and operations. Nevertheless, these operators have several strong drawbacks, such as removing significant details, changing some meaningful parts of large objects, and creating artificial patterns. This kind of approaches is generally not sufficiently relevant for helping the biomedical professionals to perform accurate diagnosis and therapy by using image processing techniques. Alternative approaches addressing context-dependent processing have been proposed with the introduction of spatially-adaptive operators (Bouannaya and Schonfeld, 2008; Ciuc et al., 2000; Gordon and Rangayyan, 1984;Maragos and Vachier, 2009; Roerdink, 2009; Salembier, 1992), where the adaptive concept results from the spatial adjustment of the sliding operational window. A spatially-adaptive image processing approach implies that operators will no longer be spatially invariant, but must vary over the whole image with adaptive windows, taking locally into account the image context by involving the geometrical, morphological or radiometric aspects. Nevertheless, most of the adaptive approaches require a priori or extrinsic informations on the image for efficient processing and analysis. An original approach, called General Adaptive Neighborhood Image Processing (GANIP), has been introduced and applied in the past few years by Debayle & Pinoli (2006a;b); Pinoli and Debayle (2007). This approach allows the building of multiscale and spatially adaptive image processing transforms using context-dependent intrinsic operational windows. With the help of a specified analyzing criterion (such as luminance, contrast, ...) and of the General Linear Image Processing (GLIP) (Oppenheim, 1967; Pinoli, 1997a), such transforms perform a more significant spatial and radiometric analysis. Indeed, they take intrinsically into account the local radiometric, morphological or geometrical characteristics of an image, and are consistent with the physical (transmitted or reflected light or electromagnetic radiation) and/or physiological (human visual perception) settings underlying the image formation processes. The proposed GAN-based transforms are very useful and outperforms several classical or modern techniques (Gonzalez and Woods, 2008) - such as linear spatial transforms, frequency noise filtering, anisotropic diffusion, thresholding, region-based transforms - used for image filtering and segmentation (Debayle and Pinoli, 2006b; 2009a; Pinoli and Debayle, 2007). This book chapter aims to first expose the fundamentals of the GANIP approach (Section 2) by introducing the GLIP frameworks, the General Adaptive Neighborhood (GAN) sets and two kinds of GAN-based image transforms: the GAN morphological filters and the GAN Choquet filters. Thereafter in Section 3, several GANIP processes are illustrated in the fields of image restoration, image enhancement and image segmentation on practical biomedical application examples. Finally, Section 4 gives some conclusions and prospects of the proposed GANIP approach

Analytical method to measure three-dimensional strain patterns in the left ventricle from single slice displacement data

Background: Displacement encoded Cardiovascular MR (CMR) can provide high spatial resolution measurements of three-dimensional (3D) Lagrangian displacement. Spatial gradients of the Lagrangian displacement field are used to measure regional myocardial strain. In general, adjacent parallel slices are needed in order to calculate the spatial gradient in the through-slice direction. This necessitates the acquisition of additional data and prolongs the scan time. The goal of this study is to define an analytic solution that supports the reconstruction of the out-of-plane components of the Lagrangian strain tensor in addition to the in-plane components from a single-slice displacement CMR dataset with high spatio-temporal resolution. The technique assumes incompressibility of the myocardium as a physical constraint. Results: The feasibility of the method is demonstrated in a healthy human subject and the results are compared to those of other studies. The proposed method was validated with simulated data and strain estimates from experimentally measured DENSE data, which were compared to the strain calculation from a conventional two-slice acquisition. Conclusion: This analytical method reduces the need to acquire data from adjacent slices when calculating regional Lagrangian strains and can effectively reduce the long scan time by a factor of two

Seyfert's Sextet: A Slowly Dissolving Stephan's Quintet?

We present a multiwavelength study of the highly evolved compact galaxy group known as Seyfert's Sextet (HCG79: SS). We interpret SS as a 2-3 Gyr more evolved analog of Stephan's Quintet (HCG92: SQ). We postulate that SS formed by sequential acquisition of 4-5 primarily late-type field galaxies. Four of the five galaxies show an early-type morphology which is likely the result of secular evolution driven by gas stripping. Stellar stripping has produced a massive/luminous halo and embedded galaxies that are overluminous for their size. These are interpreted as remnant bulges of the accreted spirals. H79d could be interpreted as the most recent intruder being the only galaxy with an intact ISM and uncertain evidence for tidal perturbation. In addition to stripping activity we find evidence for past accretion events. H79b (NGC6027) shows a strong counter-rotating emission line component interpreted as an accreted dwarf spiral. H79a shows evidence for an infalling component of gas representing feedback or possible cross fueling by H79d. The biggest challenge to this scenario involves the low gas fraction in the group. If SS formed from normal field spirals then much of the gas is missing. Finally, despite its advanced stage of evolution, we find no evidence for major mergers and infer that SS (and SQ) are telling us that such groups coalesce via slow dissolution.Comment: 70 pages, 19 figures, 15 tables - accepted for publication in the Astronomical Journa

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

An extension of min/max flow framework

In this paper, the min/max flow scheme for image restoration is revised. The novelty consists of the fol- 24 lowing three parts. The first is to analyze the reason of the speckle generation and then to modify the 25 original scheme. The second is to point out that the continued application of this scheme cannot result 26 in an adaptive stopping of the curvature flow. This is followed by modifications of the original scheme 27 through the introduction of the Gradient Vector Flow (GVF) field and the zero-crossing detector, so as 28 to control the smoothing effect. Our experimental results with image restoration show that the proposed 29 schemes can reach a steady state solution while preserving the essential structures of objects. The third is 30 to extend the min/max flow scheme to deal with the boundary leaking problem, which is indeed an 31 intrinsic shortcoming of the familiar geodesic active contour model. The min/max flow framework pro- 32 vides us with an effective way to approximate the optimal solution. From an implementation point of 33 view, this extended scheme makes the speed function simpler and more flexible. The experimental 34 results of segmentation and region tracking show that the boundary leaking problem can be effectively 35 suppressed

On a fast bilateral filtering formulation using functional rearrangements

We introduce an exact reformulation of a broad class of neighborhood filters, among which the bilateral filters, in terms of two functional rearrangements: the decreasing and the relative rearrangements. Independently of the image spatial dimension (one-dimensional signal, image, volume of images, etc.), we reformulate these filters as integral operators defined in a one-dimensional space corresponding to the level sets measures. We prove the equivalence between the usual pixel-based version and the rearranged version of the filter. When restricted to the discrete setting, our reformulation of bilateral filters extends previous results for the so-called fast bilateral filtering. We, in addition, prove that the solution of the discrete setting, understood as constant-wise interpolators, converges to the solution of the continuous setting. Finally, we numerically illustrate computational aspects concerning quality approximation and execution time provided by the rearranged formulation.Comment: 29 pages, Journal of Mathematical Imaging and Vision, 2015. arXiv admin note: substantial text overlap with arXiv:1406.712

Brain Tumour Extraction Based On Segmentation

No Abstrac